Prevalence and Recognition of *Phytophthora*

#### **Chapter 5**

## *Phytophthora* Diseases Prevalence, Its Effects and Controls in Ghana

*Benedicta Nsiah Frimpong, Samuel Oteng Ampadu, Allen Oppong, Isaac Nunoo and Lydia Brobbey*

#### **Abstract**

The success of the UN Sustainable Development Goals in reducing hunger and poverty is limited by crop losses. Globally, plant pests and diseases account for 40% yield losses which threatens food and nutrition security, livelihoods of citizenry and erode the resources of local and national economies. *Phytophthora* diseases are among the most important diseases in sub-Saharan Africa which result in severe socio-economic consequences. Roots and tubers and cash commodity crops are important staples and foreign exchange earner crops in Ghana which are significantly challenged by the incidence and severity of *Phytophthora* diseases. To ensure food availability, safeguard the local financial ecosystem and protect the environment, innovative and sound management practices are needed and this chapter reviews the different *Phytophthora* diseases on crops; more specifically with (cocoa and taro as case studies), the consequences and available management options that can be applied to manage the disease situation in Ghana.

**Keywords:** economic loss, incidence, severity, Cocoa, Taro, sustainable development, Ghana

#### **1. Introduction**

#### **1.1 Origin of** *Phytophthora* **diseases**

*Phytophthora* is a genus of filamentous Oomycetes, within the Kingdom Chromista which is also referred to as Kingdom Stramenopila [1–3]. There are several species within the class Oomycetes of which over 120 species are known [4] and could either be soil or water borne. Morphologically, they bear the resemblance of fungi with both sexual and asexual spores. Most are pathogens causing disease in a large range of plant hosts. The pathogens not only cause economic damage to crops but to the natural ecosystem as well. They affect both traditional and nontraditional agricultural crops, floricultural plants such as ornamentals and forest plants and they are pervasive in soil and water globally [3]. Due to their economic and environmental impact, there is expanding interest in *Phytophthora* genetics and genomics, resulting in the recent releases of genome sequences of *P. ramorum*, *P. sojae, P. infestans, P. capsici and P. litchi* [5–8].

The identification of gene families encoding classes of toxins, elicitors, and effectors shared among the *Phytophthora* species is critical to understanding the disease process. The most devastating specie worldwide is the *P. infestans* which in history caused huge damage to Irish Potatoes in 1845 as a result of Potato Late Blight outbreak thus, causing great famine in the Irish land with about 25% of its population starving and evacuating [3]. *P. infestans* are noted to affect the Solanaceous plants while the rest may either be host specific or attack varied host plants. Below are some selected species for the temperate and tropical regions of the globe; their target host plants and signs and symptoms (**Table 1**).

#### **1.2** *Phytophthora* **diseases prevalence: Evidence from Ghana**

#### *1.2.1* Phytophthora *disease in cocoa production*

In Ghana, the earliest form of *Phytophthora* disease was caused by the pathogen P. megakarya. P. megakarya is native and pervasive in the Western and Central parts of Africa. Known to have spread from the West form Cameroon to Ghana and Cote D'Ivoire through Nigeria and Togo and Southwards to Gabon and Equatorial Guinea [13]. *Phytophthora* megakarya is the most destructive fungal pathogen on cocoa production in Ghana [13]. The disease has been in Ghana for many years but on other alternative hosts [16]. It was originally identified in Nigeria in 1979 [17], reported in Togo in 1982 [18], and was subsequently reported in Ghana in 1985 [16]. Though the incidence of *Phytophthora* disease was originally reported in Ghana by 1985, Darkwa (1981) concluded that P.megakarya probably occurred before 1980 until it was officially reported in 1985 at Akomadan-Ashanti Region.

According to Tsopmbeng et al. [8] an isolate of *Phytophthora* from *Mimusops elengi* at Aburi in Ghana was distinctly different from what was hitherto referred to as cocoa or G-isolate. Turner et al. [8] further reported that the Mimusops isolate produced oospores in mixed culture with the cocoa isolate. Presently, the G-isolate has now been identified as P. palmivora and the N-isolate as P. megakarya [16, 19]. Until 1985, *Phytophthora* palmivora was the only known causal agent for *Phytophthora* pod rot (black pod) disease in Ghana. The appearance of *Phytophthora* megakarya in 1985 in Ghana added a new dimension to the disease complex of cocoa in the country. Similarly, studies on black pod diseases by [20] confirmed that some parts of Volta Region of Ghana consistently had the predominant type caused by P. megakarya. This is plausible due to the fact that the region shares boundary with Togo, a country predominantly affected by P. megakarya species [16].

#### *1.2.2* Phytophthora *disease in Taro production*

It is also important to highlight that not only has the prevalence of *Phytophthora* affected cocoa production after its earliest occurrence but also the production of Taro. The production of taro in Ghana, in recent times, has been affected by the taro leaf blight caused by *Phytophthora* colocasiae which has also been reported to have threatened the sustainability of taro production globally [21, 22]. In Ghana, [23] reported the presence of the disease after similar reports in Nigeria and Cameroon. The disease affects all parts of the crop including the leaves, corms, petioles and cormels, resulting in extensive damage of the foliage and reduced yield [24]. It has therefore become a limiting factor to taro production in all taro growing countries.

Taro (*Colocasia esculenta* var., antiquorum) is one of the most important food crops in Ghana [25]. It is a hunger crop and cultivated in almost all the ten regions of the country [26]. The corm is used for flour for bakery and in the preparation of local dishes. The corm is also high in carbohydrates [27]. The leaves can be eaten as vegetable in the country, and it is an excellent source of vitamins. A lot of village folks depend on


**Table 1.**

*Some selected species and the affected host plants.*

this crop for their livelihood. Farmers obtain regular income from the production as well as food for the family [26]. Despite, the importance of taro as an important food security crop in Ghana; its production is hampered by a leaf blight disease caused by *Phytophthora* colocasiae. Marian Raciborski first described *Phytophthora* colocasiae in 1900 from Java. It was first reported in Ghana in 2012 [28]. It is the most destructive fungal disease responsible for heavy yield losses (25 to 50%) of taro [27]. In addition, this pathogen causes a serious postharvest decay of taro corms.

#### *1.2.3 Method of isolating and identification of* Phytophthora spp

Proper plant diseases identification is critical and it forms the basis for population genetics, epidemiological studies and development of effective control mechanisms. This review reports on how authors have isolated *phytophthora spp* in cocoa and taro respectively.

#### *1.2.3.1* P. Megakarya *isolation*

Detailed account on experiment conducted by [29] investigating shade trees as alternative host of P. megakarya is given as follows. The team conducted an experiment on a 5-hectare cocoa field at erstwhile Brong Ahafo region, precisely Bechem which was planted in 1984 with two hybrids; T79/501 x Amel, and T60 x Na45 respectively. Cocoa plants in the test field were largely infected with P. megakarya. Forty-eight out of the fifty isolates recovered from the cocoa pods representing ninety-six percent were found to be *P. megakarya* with only two identified as *P. palmivora***.**

The team identified 34 shade trees at the test site so roots with no visible lesions of approximately 1-2 cm thick were collected from a depth ranging from 20 to 50 cm. separate samples were placed in black polythene bags and refrigerated at 4oC for up to 2 weeks before isolations were done. Samples were taken in two month intervals in the 1996/97, 1997/98 and 1998/99 cropping calendar (June, August, October, December, February and April). During the isolation process, the bulk soil and pieces of other root parts were thoroughly washed with running water. A razor blade was used to cut about 1–2 were of the roots and washed in three separate sterile distilled water. The surface sterilized immersing for 5 min in a 10% sodium hypochlorite solution and wiped dry on a paper towel. The roots were again washed for an hour in sterile distilled water on a flask shaker. In all 100 root pieces were cut from each test tree and sub divided into two groups. The isolation methods involved two techniques of "baiting with cocoa pod husks and direct plating on P. megakarya and P. palmivora agar (PPMA)" [30]. Isolates were identified on the basis of three parameters; growth rates, colony morphology and sporangium features. Out of 34 shade trees tested, P. megakarya was recovered from four of the roots from the shade trees after three consecutive years. P. megakarya was isolated most frequently in the wet season than the dry.

#### *1.2.3.2* P. colocasiae *isolation*

A survey was conducted by [31] during the 2019 rainy (July to November, 2019) and dry seasons (November, 2019 to February, 2020) in Sunyani and Dorma-Central Municipalities to assess the incidence and severity of taro blight disease in these zones. The team collected randomly sampled from infected leaves and petioles showing sign like "the development, exudation and oozing of amber, reddishbrown or bright-orange droplets from both sides of the leaf margins, water-soaked necrotic areas, which have combined into large lesions with white powdery

Phytophthora *Diseases Prevalence, Its Effects and Controls in Ghana DOI: http://dx.doi.org/10.5772/intechopen.99130*

appearance and blighted leaf blade. The sample was take to the University of Energy and Natural Resources Lab in Ghana for isolation and purification of the pathogen. The isolation was carried out under a Laminar flow hood. The diseased part of the taro leaves and petiole was cut. The pieces were surface sterilized in 70% ethanol for a minute and carefully washed in three exchanges of distilled water. The pieces were blotted dry on Whatman paper for 2 minutes and plated on potato dextrose agar (PDA, Oxoid, England) at the 28°C for seven days. They were examined daily for the development of mycelial growth. The isolation process was reproduced three times. The mixed population cultures were sub- cultured by transferring hyphal tip from the mycelium edge onto a new prepared PDA medium using flamed inoculation needle to purify it.

Wet mount from the pure cultures was prepared to identify the pathogen morphologically. By using bi-nuclear microscope, the characters of the putative pathogen such as hyphae type, shape of sporangia, micro and macro conidia were also examined morphologically and the characteristics compared to a standard established identification protocols by [32, 33].

#### **1.3 Symptoms and distribution of virulent** *phytophthora* **diseases in Ghana**

#### *1.3.1* Phytophthora *disease cycle and environmental parameters for disease incidence*

Direct correlation has been established between black pod disease incidence and weather condition. Thus black pod disease has been seen to be highly influenced by environmental factors and several studies [13, 34] have confirmed the role played by climate variability in the prevalence of black pod disease caused by *phytophthora* species. Akrofi et al. [35] reported that the disease develops well under frequent precipitation, high relative humidity and low temperature. Under high and regular precipitation regimes, P. megakarya is reported to result in a total yield loss in Cameroon where no action was taken [13, 35]. Under similar conditions; Asare-Nyako and Dakwa [34] reported losses in the range of 60 to 100% in Ghana. Asare-Nyako and Dakwa [34] emphasized that, the black pod disease in Ghana developed quickly during the day when the relative humidity stayed above 80% under shady cocoa and the frequency and amount of rainfall influenced the intensity of the disease development. Asare-Nyako and Dakwa [34] reiterated that the peak level of infection varied yearly between location and with the rainfall pattern.

In Ghana, black pod disease caused by P. megakarya is usually severe between August and October [16, 36]. The topmost phases of disease occurrence provide rich information in predicting disease development trends and serve as an important disease management tool. The developmental stages during *phytophthora* disease cycle in cocoa is presented in **Figure 1**.

P. colocasiae survives under high temperatures and humidity, in wet areas and plots that are densely planted [12]. Study by [25] in Aowin Suaman district in the Western Region of Ghana; a tropical rainforest with monthly temperature of 27°C and annual rainfall between 1500 and 1800 millimeters, recorded high incidence of Taro leaf blight; 99% as the described condition favored the spread of the disease.

#### *1.3.2 Symptoms and distribution of P. megakarya*

*Phytophthora* disease incidence and crop losses vary from one locality and farm to another [35] and fluctuate across seasons [20]. P. megakarya infects every

**Figure 1.** Phytophthora *disease cycle in cocoa. Photo credit: Akrofi et al. [13].*

developmental stage and every part of the cacao plant under wet and humid conditions. Infection of seedlings leads to leaf blight and root rot in nurseries, while infections of the stem, chupons and branches lead to cankers. While every stage of pod development is susceptible to infection, immature pods are the most susceptible. Pod infection also leads to pod rot [13]. In Ghana, P. megakarya form stem cankers very rapidly. Unlike P. palmivora cankers which are usually distributed normally on the tree trunk, P. megakarya cankers tend to be concentrated on the lower parts of the stem close to the ground though it affects all parts of the tree [36]. Due to this, treating with chemicals become difficult and unproductive.

In Ghana, [13] found that P. megakarya has spread from Akomadan and Bechem where it was first reported in 1985 into 50 more administrative districts in the six cocoa growing regions of Ghana covering an approximate area of 75,298 km2. They further noted that the current distribution in the country is as follows: Ashanti, 13 districts (17,676 km2); Brong Ahafo Region, 10 districts (10,422 km2); Central Region, 4 districts (5900 km2); Eastern, 7 districts (7760 km2); Western, 12 districts (25,698 km2) and Volta, 6 districts 7843 km2. The corresponding percentage areas infested in the regions are 23.5%, 13.8%, 7.8%, 10.3%, 34.1% and 10.4% respectively [13]. Pictures of infected cocoa pod showing symptoms is presented in **Figure 2**.

#### *1.3.3 Symptoms and spread of* Phytophthora *colocasiae*

Taro (*Colocasia esculenta* (L.) Schott) suffers attacks from several pathogens, among which *Phytophthora* colocasiae, Racib, associated with the Taro leaf blight being the most destructive. The disease is associated with 90% and 50% loss in leaf

#### **Figure 2.**

*Symptoms of P. megakarya in cocoa in Ghana. Photo credit: Akrofi et al. [13]. (a) multiple lesions on cocoa pod; (b) coalescing lesions; (c) abundant sporangia indicated by the arrow; (d) diverse infection phases on the cocoa; (e) distal infection; (f) proximal infection; (g) lateral infection; (h) canker lesions prior to scraping and (i) canker lesions once scraped displaying scarlet colouration.*

and corm yield of taro, respectively [22]. *Phytophthora* colocasiae is disseminated by infected vegetative plant parts and possibly contaminated soil [21]. Conventionally, variations in *Phytophthora* species have been detected on host differential, biochemical test, morphological and molecular level characterizations [37]. According to studies, the foliar pathogen has spread across Africa, East Asia, the Americas, the Caribbean, and the Pacific, as well as all other taro-growing regions of the world, with varying degrees of severity [28, 38].

#### **Figure 3.**

*A. Asymptomatic leaf; B. Symptomatic leaf. Photo credit: Abdulai et al. [31].*

#### **Figure 4.**

*A. Water-soaked appearance on plant; B. Light exudate from both sides of the water-soaked leaf margins. Photo credit: Abdulai et al. [31].*

In terms of its dissemination, [31] indicated that windy rains and splashing water from irrigation or running water are two ways that the sporangia on the infected plant surface are quickly disseminated. The pathogen can grow in the soil as an encysted zoospore with thick cover layers or as chlamydospores in the absence of the host for several months as a survival mechanism under dry stress conditions [28, 39]. Blight of the leaf blade is the most visible symptom of the disease; other symptoms include postharvest rot of the corm and rotting of the petiole in susceptible varieties [40]. Early plant leaf infection is most common in areas where there is sufficient accumulation of guttation droplets, dew, or rainfall. The pathogen sporangia usually appear on infected leaves as small, brown, watersoaked necrotic areas that quickly coalesce into large lesions from which yellow exudates emerge, followed by defoliation and plant death within a few weeks after infection [25, 28].

The fluctuating day/night cycle influences the development of specific symptoms. Cool night temperatures encourage lesion expansion with 3–5 mm wide water-soaked margins that dry out during the day and return to water-soaked status at night, resulting in zonation around the necrotic lesion that is easily visible when viewed from the bottom of the infected leaf [41]. Some symptomatic and asymptomatic plant parts are shown in **Figures 3**–**5**.

Phytophthora *Diseases Prevalence, Its Effects and Controls in Ghana DOI: http://dx.doi.org/10.5772/intechopen.99130*

#### **Figure 5.**

*A–D. Fresh and dried Amber, bright-Orange or reddish-Brown exudate on Taro leaves. Photo credit: Abdulai et al. [31].*

#### **2. Social and economic impact of infectious** *Phytophthora* **diseases in Ghana**

#### **2.1 Impact of** *Phytophthora* **megakarya**

Though cocoa is native of South America, the bulk of the beans production comes from Africa with Ghana being the second largest world's producer after Cote D'Ivoire [42, 43] With Ghana's position in the International cocoa production and export markets [42], cocoa contribution to the nation's economic growth is limited by high yield losses resulting from *Phytophthora* disease infections [13]. *Phytophthora* palmivora which accounted for pod losses of less than 30% was the only known causal agent of black pod disease of cocoa in Ghana prior to 1985 A. P. megakarya causes yield losses as high as 60–100% in Ghana according to a report by [44]. P. megakarya has become the main yield-limiting factor for cocoa production in affected areas [36], rapidly surpassing the importance of P. palmivora. The emergence of P. megakarya has had dramatic social and economic consequences in cocoa producing countries in West and Central Africa including Ghana, clearly demonstrating the scale of damage that it may cause in case it spreads into other cocoa producing territories.

Particularly in Ghana, it was reported that some cocoa farms were neglected or abandoned and, some cocoa farmers switched over to cultivate vegetables and other crops because of P. megakarya infections on their cocoa farms [35, 36].

A report by COCOBOD in 2014 is also indicative that Ghana lost over 25% (212,500 MT) of its annual output of 850,000 MT of cocoa beans to black pod disease, representing a revenue loss of about GH¢7.5 million in 2012. P. megakarya still remains an invasive species in Ghana and was reported to be spreading in the Ghanaian cocoa belt towards the border with Cote d'Ivoire [29]. A study by [16] noted that several national programmes, including the National Cocoa Pests and Diseases Control Programme (CODAPEC), were instituted by the Ghanaian government in which P. megakarya infected farms were sprayed with fungicides at the expense of the government. The money spent on these programs could have been better spent on improving the lives of farmers. In addition, [36] noted that in view of the severity of P. megakarya mediated black pod during the disease-conducive period (July–October), some famers in Ghana attached some belief to its incidence due to the devastating nature; thinking that it was a strange disease caused by evil forces or the effects of the Volta Lake [35] which has influenced farmers to adopt wrong attitudes towards its control.

#### **2.2 Impact of** *Phytophthora* **colocasiae**

Globally, it is generally believed that diseases decrease agricultural productivity by more than 10%, which is comparable to half a billion tonnes of total food produced each year [40]. The impact of fungal diseases on crop production has been well explained by [31] who reported that, when fungal diseases are properly controlled on five (5) major crops alone, more than 600 million people could be fed each year in the world. Taro plant is not an exception, as it is known to be infected by more than ten serious pests and diseases caused by a number of insect pests and pathogens across the globe [45]. Among all the disease-causing agents in taro plants, P. colocasiae, which causes leaf blight of taro is known to be the most important. This pathogen has been reported widely for causing leaf yield loss of 95% and 50% in postharvest rot of corm yield and quality [39, 46].

It is believed that P. colocasiae is disseminated by means of vegetative propagation materials [28] and the case may not be different in Ghana. There are no accredited supply centers for planting materials in the country, and farmers rely on families, neighbors and open market for their supplies of planting materials, which may be coming from already infested fields. The constraints of taro blight disease to productivity of taro have been acknowledged in the West-African Sub Region [28, 38, 47]. The disease poses serious threats to global food security as well as economic hardship to the people in these taro producing regions of the world. In Ghana, apart from the three northern regions, taro production is mainly carried out in the southern part of the country. A few research works have been reported so far on Taro [47–50]. Even then, the focus had been on the profitability of the taro enterprise. More studies such as ours reporting on the incidence and severity of P. colocasiae are needed to provide valuable data to inform interventions towards the management of taro blight in the country.

#### **3. Management of** *phytophthora* **diseases in Ghana**

#### **3.1 Management of** *Phytophthora* **megakarya**

Huge losses resulting from P. megakarya and associated management cost pose serious threats on the socio-economic development of cocoa growing countries in terms of their financial resources such as Ghana. Timely, more integrated and sustainable practices which involve the use of resistant varieties, chemical application,

#### Phytophthora *Diseases Prevalence, Its Effects and Controls in Ghana DOI: http://dx.doi.org/10.5772/intechopen.99130*

quarantining germplasm received outside the country, cultural and biological control are imperative [13, 35]. To effectively prevent disease caused by P. megakarya, these integrated control strategies must be employed on time. Planting material movement from one location to another within Ghana account for the quick spread of the pod rot pathogens. The amalgamation of cultural and chemical methods in Ghana has proven to be effective against P. megakarya. Cultural practices present a cost effective way of managing plant diseases as it provides the right ecosystem for effective performance of fungicides. Cultural practices alone, including judicious shade management, pruning, removal of basal chupons, mistletoes and frequent harvesting, can be sufficient to control P. palmivora [34, 36]. Cultural practices are not only essential for increasing yield, but also provide the right environment for the efficient performance of recommended fungicides [36]. Frequent harvesting, for instance, saves partly infected mature pods and reduces sources of sporangial inoculum while shade management; opening up the canopy and reducing basal chupons, enhances air circulation in the cocoa farm, thereby reducing disease incidence [51]. Iwaro et al. [51] noted that at least six applications are required in one black pod sea.

The recommendation of 3-weekly fungicide spraying in Ghana (son (May– October). This rather high frequency of spraying, coupled with the ever-increasing cost of inputs (labour and fungicides) and the lack of knowledge in techniques for effective spraying, make the adoption of chemical control very low. Four-weekly spraying of either metalaxyl and copper-1-oxide (Ridomil 72 plus) or cuprous oxide (Nordox 75) combined with cultural practices had been found effective against *Phytophthora* megakarya in researcher-managed trials [52]. This spraying regime reduces the number of sprays per season to five.

#### **3.2 Management of** *Phytophthora* **colocasiae**

To be able to control or manage taro blight disease, which usually limits the productivity of this crop, it is important that the pathogen is isolated from the diseased tissues and characterized. On that basis, the pathogen was successfully isolated and identified morphologically as P. colocasiae based on the important characters of the pathogen using standard Mycological identification keys according to [32, 33]. The sporangia are ovoid to ellipsoid with a well-defined narrow semi-papillate structure and are usually formed at the end of unbranched or casually branched sporangiophores at the edge of necrotic lesions. The sporangium is normally segregated from sporangiophores by the rain, leaving a small pedicel that is attached to their base [53], signifying the important role rain plays in the pathogen dispersal.

The incidence and severity of the disease are closely linked to the ability of the pathogen to be dispersed from one place to the other and hence the reason for the varied incidence and severity of the disease across the various fields in the communities'/farmers' fields. Management practices of taro leaf blight include hygienic practices, use of disease-free planting materials, wide spacing between plants when planting, clearing, removal and burning of infected debris (leaves) during the initial stage of disease development, separating the diseased plant from the healthy ones, planting near forest plantations which can serve as a barrier to disease transmission to the taro plants [54, 55].

Singh et al. [40] in his study was able to avoid serious taro blight disease in his field by planting during the dry season. Appropriate timing of planting is therefore recommended. Biological control methods such as the use of microorganisms, eg. *Pseudomonas fluorescens*, Trichoderma viride have also been applied [56]. Chemical control involving the use of systemic and protectant fungicides such as phosphorus acid (Foschek); copper (e.g. copper oxychloride); Mancozeb (e.g. Dithane M45) and metalaxyl (e.g. Ridomil Gold MZ) has successfully been used to control taro blight disease [46, 55]. Lastly, the use of the most effective and promising management strategy is the utilization of resistant taro cultivars [40] some of which were recently released by Scientists at CSIR-Crops Research Institute, Kumasi, Ghana.

#### **4. Conclusion and recommendation**

Despite the widespread distribution of *Phytophthora* diseases and resulting crop harm across the globe, there is a scarcity of knowledge about the diseases' incidence and intensity in Ghana. However, to efficiently establish a long-term management program for its control for farmers in Ghana to increase productivity, there is a need for more research to determine factors likely to limit the productivity of crops affected by *Phytophthora* disease. The continuous development of improved and high yielding varieties that are resistant or tolerant to Phythothora diseases should be intensified for all crops.

### **Conflict of interest**

None.

#### **Author details**

Benedicta Nsiah Frimpong1 \*, Samuel Oteng Ampadu1 , Allen Oppong1 , Isaac Nunoo2,3 and Lydia Brobbey1

1 CSIR - Crops Research Institute, Kumasi, Ghana

2 Rural Education for Agricultural Development International, Kumasi, Ghana

3 Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

\*Address all correspondence to: benenash@yahoo.co.uk

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*DOI: http://dx.doi.org/10.5772/intechopen.99130* Phytophthora *Diseases Prevalence, Its Effects and Controls in Ghana*

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[25] Van der Puije, G. C., Ackah, F. K., and Moses, E. (2015). Prevalence of leaf blight disease caused by Phytophthora colocasiae in taro in the Aowin Suaman Districts of Ghana. *Hort Flora Research Spectrum 4 (3): 282*, *284*.

[26] Ackah, F. K., van der Puije, G. C., and Moses, E. (2014). First evaluation of taro (*Colocasia esculenta*) genotypes against leaf blight (Phytophthora colocasiae) in Ghana. *HortFlora Research Spectrum*, *3*(4), 301-309. ISSN: 2250-2823

[27] Shakywar, R. C., Pathak, S. P., Kumar, S., and Singh, A. K. (2012). Evaluation of fungicides and plant extracts (botanicals) against Phytophthora colocasiae raciborski causing leaf blight of Taro. J. Plant Dis. Sci, *7*, 197-200.

[28] Omane, E., Oduro, K. A., Cornelius, E. W., Opoku, I. Y., Akrofi, A. Y., Sharma, K. and Bandyopadhyay, R. (2012). First report of leaf blight of taro (Colocasia esculenta) caused by

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Phytophthora colocasiae in Ghana. Plant disease, *96*(2), 292. https://doi. org/10.1094/pdis-09-11-0789

[29] Opoku, I. Y., Akrofi, A. Y., and Appiah, A. A. (2002). Shade trees are alternative hosts of the cocoa pathogen Phytophthora megakarya. Crop Protection, *21*(8), 629-634. https://doi. org/10.1016/S0261-2194(02)00013-3

[30] Opoku, I.Y. 1994. Survival of Phytophthora palmivora and Phytophthora megakarya in soil and in cocoa roots. PhD. Thesis, University of London, UK.

[31] Abdulai, M., Norshie, P. M., and Santo, K. G. (2020). Incidence and severity of taro (*Colocasia esculenta* L.) blight disease caused by Phytophthora colocasiae in the Bono Region of Ghana. *SSRG International Journal of Agriculture and Environmental Science*, *7*(2), 52-63. ISSN: 2394 - 2568

[32] Ellis, D. H., Davis, S., Alexiou, H., Handke, R., and Bartley, R. (2007). *Descriptions of medical fungi* (pp. 61-167). Adelaide: University of Adelaide.

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*33*(2), 237-248. https://doi.org/10.4314/ gjas.v33i2.1876

[37] Adomako, J., Kwoseh, C., Moses, E., and Prempeh, R. N. (2018). Variations in morphological and molecular characteristics of Phytophthora colocasiae population causing leaf blight of taro in Ghana. Archives of Phytopathology and Plant Protection, *51*(19-20), 1009-1021. https://doi.org/10 .1080/03235408.2018.1550846

[38] Bandyopadhyay, R., Sharma, K., Onyeka, T. J., Aregbesola, A., and Kumar, P. L. (2011). First Report of Taro (Colocasia esculenta) Leaf Blight Caused by Phytophthora colocasiae in Nigeria. Plant disease, *95*(5), 618. https://doi.org/10.1094/ pdis-12-10-0890

[39] Brooks, F. E. (2008). Detached-leaf bioassay for evaluating taro resistance to *Phytophthora colocasiae*. Plant disease, *92*(1), 126-131.

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[41] Tsopmbeng, G. R., Fontem, D. A., and Yamde, K. F. (2012). Evaluation of culture media for growth and sporulation of Phytophthora colocasiae Racib., causal agent of taro leaf blight. International Journal of Biological and Chemical Sciences, *6*(4), 1566-1573. https://doi.org/10.4314/ijbcs.v6i4.16

[42] Bangmarigu, E. and Qineti, A. (2018). Cocoa Production and Export in Ghana. Paper prepared for presentation for the 162nd EAAE Seminar. The evaluation of new CAP instruments: Lessons learned and the road ahead. April 26-27, 2018

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and Bailey Bryan, (2017). Phytophthora megakarya and P. palmivora, Causal Agents of Black Pod Rot, Induce Similar Plant Defense Responses Late during Infection of Susceptible Cacao Pods. Front. Plant Sci., 14 February 2017. https://doi.org/10.3389/fpls.2017.00169

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*39*(2), 171-180. https://doi.org/10.4314/ gjas.v39i2.2140

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[52] Akrofi, A. Y., Opoku, I. Y., and Appiah, A. A. (1995). On-farm farmer managed trials to control black pod disease caused by Phytophthora megakarya in Ghana. In *Proceedings of the First International Cocoa Pests and Disease Seminar, Accra, Ghana, November, 1995* (pp. 6-10). https://doi. org/10.1016/S0261-2194(02)00193-X

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[54] Hunter, D., Pouono, K., and Semisi, S. (1998). The impact of taro leaf blight in the Pacific Islands with special reference to Samoa. Journal of South Pacific Agriculture, *5*(2), 44-56.

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#### **Chapter 6**

## Recognition and Early Stage Detection of *Phytophthora* in a Crop Farm Using IoT

*Pooja Vajpayee and Kuldeep Kr. Yogi*

#### **Abstract**

Detection of agricultural plant pests is seen as one of the farmers' problems. Automated Pest Detection Machine enables early detection of crop insects with advanced computer vision and image recognition. Innovative research in the field of agriculture has demonstrated a new direction by Internet of Things (IoT). IoT needs to be widely experienced at the early stage, so that it is widely used in different farming applications. It allows farmers increase their crop yield with reduced time and greater precision. For the past decade, climate change and precipitation have been unpredictable. Due to this, many Indian farmers are adopting smart methods for environment known as intelligent farming. Smart farming is an automated and IOT-based information technology (Internet of Things). In all wireless environments IOT is developing quickly and widely. The Internet of Things helps to monitor agricultural crops and thus quickly and effectively increase farmers' income. This paper presents a literature review on IoT devices for recognizing and detecting insects in crop fields. Different types of framework/models are present which are explaining the procedure of insect detection.

**Keywords:** Internet of things (IoT), smart agriculture, pest detection, deep learning

#### **1. Introduction**

The livelihoods of Indians are mainly from agriculture. It has been noted in the last decade that there has not been much agricultural crop development. As crop prices decrease, food prices are constantly increasing. Since 2010 more than 40 million people have been driven into poverty [1]. This may be due to water wastes, low soil fertility, abuse of fertiliser, climate change and diseases, etc. There are numerous factors responsible Effective farm intervention is very important and IOT is the solution for integration with wireless sensor networks. It is capable of changing the way agriculture develops and contributes greatly to making smart agriculture. There is a three-tier system in the internet. It contains the layer, network layer and application layer of perception. Sensor motes include perception layer. Devices enabled by ICT, sensor motes are the building blocks of sensor technology. It comprises cameras, RFID tags, sensors and network sensors for object recognition and real-time information collection. The network layer is a universal service IOT infrastructure. The combination of the layer of perception and the application layer is directed. The layer of application is a layer that combines the IOT with specific industry technology.

The internet has almost been applicable in all industries, including intelligent agriculture, smart parking, environmental monitoring for intelligent buildings, health transport and much more.

#### **1.1 Internet of Things (IOT)**

The Internet of Things (IOT) is the easiest and most powerful way to solving problems. IOT is established from different assemblies, with tonners of recorders, software, pieces of axes. It also makes details more detailed. Without human interference IOT permits the sharing of data over a network. We will mirror things naturally on the internet as everyday people like a sensor, a car driver etc. An IP address is given so that data can be transmitted across a network. In 2016, the number of connected devices grew 30% relative to 2015 according to the report produced by Garner. He adds that this number is set to grow by 26 billion by 2020 [2].

For the following factors, IOT technology is more efficient:


#### **2. A model for smart agriculture using IoT**

In 2016, Patil and Kale [3] reviewed climate change and rainfall over the past decade as annoying. As a result, many Indian farmers are implementing climateintelligent practices in recent times called intelligent agriculture. Smart farming is an integrated and IOT-driven information technology (Internet of Things). In all wireless settings IOT evolves easily and broadly. Within this article, the introduction of sensor systems and wireless communications to the implementation of IOT technologies and the systems in current agriculture are analyzed and evaluated, in conjunction with the current conditions of the agricultural system Remote Management System (RMS) is proposed to incorporate an approach with internet and cellular communications. Main aim is to capture real-time farming production data offering easy access to farming equipment such as fast massage service warnings and guidance on weather conditions, crops etc (**Figure 1**).

#### **3. Design and realization of a real-time detection device for insect pests of field crops**

Mercedes, S., Bo, G. And Yuxia, H., in 2011 [4] investigated the vast species and huge quantity of insect parasites of field crops. Hundreds of common insect pests in farmland are caught by lamps. After the Black Light was trapped, the insect pests were manually recognized and numbered. And the process of predicting was used primarily for a long time in China. It was closely linked to the overall quality of the forecast person's impact, accuracy and efficacy, and was ultimately determined by greater subjective factors.

*Recognition and Early Stage Detection of* Phytophthora *in a Crop Farm Using IoT DOI: http://dx.doi.org/10.5772/intechopen.97767*

Students studied the picture identification on field plants of insect pests in detail. The static digital camera pictures were categorized of 40 species (25 families, 8 orders) of nightmarish insect pests [5]. With gray-scale images, the 30-five typical plagues (Lepidoptera) manually put were recognized [6]. The auto trapped, killed, and placed into laboratory for further study the eight species of insect pests in cotton fields [7].

#### **3.1 Hardware and software system**

With the hardware and software framework the second-generation insect pest detection device was developed. Hardware provided trapping, astonishment and buffering, a uniform lighting, a dispersion-transporter and a vision machine. The software framework has improved image, segmented images, chosen functionality and known insect pests. The unit carried out the entire automation from the collection of insect pests to the identification.

#### **3.2 Hardware design of the detection system**

See **Figure 2**.

#### **3.3 Software design of the detection system**

Owing to the vast number of details and high efficiency in real time, the cost of the image processing equipment has been raised. Windows 2003 used the framework and the Visual C++ 6.0 language for visual development. MFC-based application software was developed using an OK C30S acquisition graphics card with API functions.

#### **Figure 2.**

*Hardware components for real time monitoring system for insect pests on field crops. (1) "Trapping, stunning and buffering unit, (2) CCD (3) Illumination unit and (4) Scattering and transporting unit".*

#### **3.4 Inference**

Real-time detection device for field plagues of second generation has been developed. It performed all automation, from the capture, dispersion, transport, collecting of images, picture analysis to pest recognition. It reduced the duration of detecting pests and increased the degree of automation. The right identification ratio of nine species of pests surpassed 86%. The study further focused on enhancing pest species and improving the detection efficiency.

#### **4. Internet of Things application to monitoring plant disease and insect pests**

Shi, Y., Z., Wang, X. Shi, Y. And Zhang, S. in 2015 [8] studied the efficient way of enhancing agricultural low-tech culture quality by using information and communication technology to establish a plant disease and long-term insect pest control system as the farm expert was unable to use the farm for farm management and insecticide disease control. The article introduces internet-based information perception technology (IOT) as well as the role of IOT technology for agricultural disorders and for the control of insect pests, including farming disabilities and the insect pest control system, the collection of sensor nodes, data processing and exploitation of insect pest information, etc. An IOT-based disease and insect pest control system consisting of three levels and three systems were proposed. A new way of accessing agricultural information on the farm is provided by the system.

#### **5. Agricultural crop monitoring using IoT**

"Sreekantha, D.K. and Kavya, A.M", in 2017 [9] investigated the reorganisation of the IOT for agriculture, helping farmers to deal with problems in the region with a broad variety of technology including accuracy and sustainable agriculture. IOT

*Recognition and Early Stage Detection of* Phytophthora *in a Crop Farm Using IoT DOI: http://dx.doi.org/10.5772/intechopen.97767*

technology helps to gather information on conditions such as weather, precipitation, soil temperatures and fertility, field tracking online allows weed identification, water level, pest detection and interference of livestock, crops growth and farming. IOT moves farmers from everywhere and at any time to link to their farm. Farms are monitored using wireless sensor networks, and microcontrollers for the control and automation of agricultural processes. Wireless cameras have been used to view the environments remotely in the form of pictures and images. A smart telephone enables farmers, at every time and anywhere in the world to keep up to date with the current conditions of their agriculture. IOT technology can cut costs and increase conventional agriculture's productivity.

#### **5.1 Inference**

The Internet of things helps agricultural crop tracking to increase crop production and thus the farmers' income quickly and effectively. A wireless sensor network and sensors of all kinds are used for the collection of crop conditions and environmental change and are transmitted to farmers/devices via a network to cause corrective action. Farmers are in connection with the circumstances of the agriculture sector around the globe at any moment and wherever. Few connectivity drawbacks have to be addressed by encouraging the technologies to conserve resources and also by facilitating the user experience.

#### **6. Remote insects trap monitoring system using deep learning framework and IoT**

"Ramalingam, B., Mohan, R.E., Pookkuttath, S., Gómez, B.F., Sairam Borusu, C.S.C., Wee Teng, T. and Tamilselvam, Y.K in 2020" [10] researched that early insect identification and control (human physical conditions as an example houses, hospitals hotels, parks, camps, flooring, industries related to food etc) and agricultural farms were important for developed environments. These pest control steps are currently labor-dependent manual, repetitive, unpredictable and time-consuming activities. Latest advances in Internet of Things (IOT) and Artificial Intelligence (AI) and the can automate a range of maintenance operations, improving efficiency and safety dramatically. This document includes the implementation of Deep Learning (DL) and IOT monitoring system of insect traps in real-time as well as the detection of insects. *"The system architecture for remote trap monitoring is developed with IOT and the unified target detection framework of faster RCNN (Region-based convolution neural networks) Residual neural Networks 50 (ResNet50). The object detection system for Faster RCNN (ResNet 50) was trained and deployed in IOT using designed environmental insects and farm insect imagery".* The proposed device was tested with four-layer IOT and the picture of constructed ecosystem insects caught by sticky trap sheets in real time. In addition, insects from farms have been examined by a different database of photographs of insects (**Figure 3**).

#### **6.1 Inference**

In this article, the IOT and deep learning system was proposed for the remote insect tracking and the automated process of insect detection. The Faster version of RCNN ResNet object identification mechanism was accustomed to automately classifies the parasite type, using the Four-Layer IOT system to built the remote trap insect tracking mechanism. Included is an ecosystem insect data base and farm field insect archive that has been checked for offline and on-line reliability in

**Figure 3.** *System for remote trap control and insect detection based on IOT and DL.*

the detection of intensely learning insects. According to other object recognition mechanisms such as SSD and Yolo, the accepted device provided optimal insect detection efficiency. The research has shown that 96 percent of insects identified with built-in environmental insects were obtained by the qualified model, 94 percent were identified with farmland insects, and 0.2 s were needed on average for processing the one image. This case study has demonstrated the automation of remote identification through IOT and a DL-based insect monitoring system using a qualified CNN framework and overcomes insect control systems failure.

#### **7. Automated remote insect surveillance at a global scale and the Internet of Things**

"Potamitis, I., Eliopoulos, P. and Rigakis, I" in 2017 [11]. In many of our records, a large number of extreme insect plagues of agricultural and health importance were studied in a broad spatial scale as the principle of remote insect control. The trap is used to make the trap, the timeline, the GPS tag and where necessary, the inbound insect species from the wing beat safe to inject. Standard low-cost pest traps for certain insect species augment the insects. Both large crop insects are tracked in order to decide if a treatment strategy can be undertaken before a significant infestation takes place. Monitoring processes are based on specially designed mosquito traps. Conventional insect monitoring is used in the spectrum of such tracking. It takes physical labour, consumes money and also needs an expert to

*Recognition and Early Stage Detection of* Phytophthora *in a Crop Farm Using IoT DOI: http://dx.doi.org/10.5772/intechopen.97767*

be accurate enough, often with the potential for raising human security concerns. It is limited on its own expenses.

These drawbacks decrease the amount of manual insect surveillance and thus its precision, which eventually contributes to considerable cultivation losses due to pest damage. You intend to monitor the existence, the stamping period, the detection of species and the population density of target plaguicides with 'supervision' to unmatched data extraction levels. Insect counts are wirelessly transmitted to the central supervisory agency, and predictive tools for the control of insect pests and environmental factors related to population growth are visualized and streamed through. The work illustrates how traps can be organized in networks that report collectively on local, state, world, continental and international data using the new Internet of Things technologies (IOT).

This research is undoubtedly cross-disciplinary, lies at the intersection of entomology, optoelectronic engineering and computer and crop-science and involves the production and introduction by many of the most important agricultural pests of low-cost low-energy technologies to minimize the amount of quantitative and qualitative crop failure. They claims that clever traps interacting through IOT will have a huge effect on the crops security decision-making process in real time and would undermine current manual practices directly from the field to a manually managed entity in the very near future. In this article three instances of Rhynchophorus ferrugineus are investigated: track the use of (a) picusan, (b) lindgren and (c) monitoring various stored grain beetle pests using sored grain pitfall trap for Rhynchophorus ferruginus (Olivier) (Coleoptera: curculionidae). There is a very detailed approach to the industry that delivers accuracy per cent on automated quantity when as opposed to the actual defined number of insects in each type of trap.

#### **8. IoT monitoring system for early detection of agricultural pests and diseases**

"Materne, N. and Inoue, M. in 2018" [12] examined the advancement in sensor technology has driven the technological revolution in agriculture. In these days, however the opportunity to use digital technologies relating to the Internet of Things (IOT) increase sharply; the growth of the roles system of farming continues in its early stages. The current threats of less preferred climate conditions thrive on the increased risk of cross-border plant pesticides and diseases that damage crops, as well as on the danger to the food security and on some major losses for the farmers, provided that agricultural sector is still suffering from climate changes. In this study, they merged Wireless Sensor Network (WSN) sensor devices to establish an agricultural field monitoring framework that simultaneously tracks eight primary environmental parameters that are known as highly interconnectioned to booming pests and plant diseases. The overall system configuration provided for real-time tracking and regular collection of the huge volume of data. This is why they have investigated the knowledge obtained using machine learning approaches using "KNN, Random Forest, Logistic Recovery and Linear Regression algorithms". The purpose of this article is to perform an experiment on the advantage of using the IOT systems in agricultural lands to gather and analyze data to determine a prediction model that could be used to help forecast the outbreaks of plantation diseases.

#### **8.1 Inference**

They suggested an IOT framework with functionalities for day-to-day tracking of farmland environmental parameters. They also developed a predictive model

#### *Agro-Economic Risks of* Phytophthora *and an Effective Biocontrol Approach*

for the provision by applying machine learning algorithms to avoid the outbreaks of pest and diseases in planting. The study merged IOT and machine learning technologies to improve agriculture and agriculture to draw on new concepts and developments in technology in order to sustain, increase yields and increase agricultural efficiency. The work represents only eight sensed parameters; the number of sensors, like meteorological information, may be added for potential production purposes. In addition, it is important to enhance the work of cloud providers so long as the behaviour and features of each type of pest and disease are taken into account.

#### **9. Research on insect pest image detection and recognition based on bio-inspired methods**

"Deng, L., Wang, Y., Han, Z. and Yu, R" in 2018 [13] performed a study entitled "Research on insect pest image detection and recognition based on bio-inspired methods". In this study, methods inspired by human visual systems were suggested to easily identify and identify insect pests. SUN was used for the generation of saliency and area of interest maps and identification in pest images using a Natural Statistics Model (NGM) to inspire human visual focus. In order to exclude invariant characteristics, the bio- influenced Hierarchic Model and X (HMAX) model were used to reflect a plague presence. Scale Invariant Function Transform (SIFT) in the HMAX model was integrated to improve rotational changes invariance. In the meanwhile, Coarse non-negative encoding (NNSC) simulates clear cell replies. Furthermore an Invariant Texture Properties has been removed using the Local Pattern (LCP) algorithm. Finally, the extracted characteristics were provided for recognition by Support Vector Machines (SVM). Experimental studies have shown a gain from the proposed approach relative to the approaches "HMAX, Sparse Coding and Normal Input Memory (NIMBLE), which is similar to the Deep Convolution Network" (**Figure 4**).

#### **9.1 Framework**

See **Figure 5**.

#### **9.2 Result**

With an identification rate of 85.5%, the proposed approach obtained a successful outcome and could efficiently identify insect pests in diverse ecosystems. The suggested solution offered a new approach to the identification and recognition of insect pests.

"P. Tirelli, N.A. Borghese" [14] found that surveillance of the population of pesticides in the plant sector is currently a concern. At present, the device is based on dispersed images, which can be automatically collected and transmitted images of stuck areas to a remote station by means of a wireless sensor network. The station tests the density of the production of insects at various farm sites and alerts when the threshold of the insect is surpassed. The client nodes are spread in the fields that serve as monitoring stations. The main node co-ordinates the network and retrieves images from client nodes. During the four week monitoring cycle, the network periodically operates, and the viability is assessed, forecasts the population curve of the pest insects compared with everyday assessment.

*Recognition and Early Stage Detection of* Phytophthora *in a Crop Farm Using IoT DOI: http://dx.doi.org/10.5772/intechopen.97767*

#### **Figure 4.**

*Sample images taken in natural conditions. (a) Locusta migratoria, (b) Parasa lepida, (c) Gypsy moth larva, (d) Empoasca flavescens, (e) Spodoptera exigua, (f) Chrysochus chinensis, (g) Laspeyresia pomonella larva, (h) Spodoptera exigua larva, (i) Atractomorpha sinensis, (j) Laspeyresia pomonella.*

#### **10. Insect pest image detection and recognition based on bio-inspired methods**

"Nanni, L., Maguolo, G. and Pancino, F" in 2020 [15] performed a study entitled "Insect pest image detection and recognition based on bio-inspired methods". Identification of insect pests is important for crop safety in many areas of the world. They introduce in this article an artificial classification based on the combination of saliency and neural networks. SALIENCY techniques are widespread image processing algorithms that identify the most important image pixels. In this paper you are using three different salience approaches as preprocessing of images and make

three distinct images for each form of saliency. For each original image they generate new photographs to train various neural convolution networks, they create 3\*3 = 9 new pictures (**Figure 6**).

#### **10.1 Result**

They evaluate each execution of each preprocessing/network pair as well as they examine the performance of their grouping. You assess your solution on the major IP102 and a tiny dataset. Its best ensembles achieve the maximum degree of

#### **Figure 5.** *Feature extraction framework of LCP.*

**Figure 6.** *Image samples from IP102.*

*Recognition and Early Stage Detection of* Phytophthora *in a Crop Farm Using IoT DOI: http://dx.doi.org/10.5772/intechopen.97767*


#### **Table 1.**

*Composition of dataset.*

technical precision, with both the smaller (92.43%) and the IP102 (61.93%), matching the efficiency of smaller human experts (**Table 1**).

#### **11. Automatic detection and monitoring of insect pests-A review**

Cardim et al. in 2020 [16] studied that certain insect pest species can be automatically identified and tracked. In order to develop integrated pesticide management (IPM) for precision agriculture, several systems were developed. For many essential pests, automatic detection traps were developed. This emerging methods and strategies are very promising to identify hostile and quarantine pests early on and to monitor them. The paper attempts to evaluate technological and scientific state-of-the-art sensor technologies in order to track and automatically identify insect pests (**Figure 7**).

"In the article the methods are discussed for the identification, the applications are introduced, as well as recent progress, comprising machine learning and the Internet of Things, infrared monitors for pests, audio sensors and classification through images".

**Figure 7.** *Automatic trap for moth species tracking (a) and fruit flies (b) EFOS, Trapview, Slovenia.*

**Figure 8.** *Global architectural design.*

#### **12. Pest detection and extraction using image processing techniques**

"Miranda, J.L., Gerardo, B.D. and Tanguilig III, B.T" in 2014 [17] performed a study entitled "Pest Detection and Extraction Using Image Processing Techniques". Detecting pests in paddy fields is an important problem in agriculture, so effective steps to tackle infestation and reduce the use of pesticides should be created. Imaging techniques are commonly used in agriculture to offer optimum protection for crops, thereby leading to greater crop management and production. The surveillance of pests depends on workers but electronic control is developing to mitigate the efforts and mistakes of human beings (**Figure 8**).

This study broadens the application of various imaging procedures for the identification and extraction of insect pests through the establishment of an integrated paddy field detection and extraction device for estimating plague densities. Experimental review shows the proposed approach to detect pests in rice fields to provide a quick, reliable and simple solution.

#### **13. Insect detection and classification based on an improved convolutional neural network**

"Xia, D., Chen, P., Wang, B., Zhang, J. and Xie, C" in 2018 [18], to solve the issue of multi-classification of insects in the field, evaluated a prototype of a neural network. The model will use the benefits of the neural network such that multifaceted insect traits are completely extracted. In the regional proposal process, rather than a standard, selection technique, the regional proposal network is implemented in order to produce fewer proposal windows, which are particularly valuable for improving the accuracy of forecasts and accelerating calculations. Experimental studies indicate that the proposed approach is better than the current conventional insect classification algorithms and is higher reliably (**Figure 9**).

*Recognition and Early Stage Detection of* Phytophthora *in a Crop Farm Using IoT DOI: http://dx.doi.org/10.5772/intechopen.97767*

**Figure 9.** *The proposed detection model using VGG19 having schematic structure.*

### **14. Image processing techniques for insect shape detection in field crops**

Thenmozhi, K. and Reddy, U.S., 2017 [19] performed a study entitiled "Image Processing Techniques for Insect Shape Detection in Field Crops". In farming, the identification of crop pest is considered one of the farmers' challenges. An integrated machine vision and picture analysis device for insect detection enables improved identification of early-stage plant insects with a lower time and greater precision that will improve crop yield. Digital image processing methods were used in the current work to detect the insect forms in the sugar cane crop with photographs of crop insects for pre-processing, segmentation and extraction. Sobel edge detecting is introduced to distal media. In the extraction of the feature nine geometrical features can be defined in the structure of the insect. This recognition of insect shape performs well and achieves high precision for round (circular), oval, triangular and rectangular sugarcane field insects. The study was carried out using the Image Processing Toolbox in MATLAB 2015b (**Figure 10**).

#### **15. IOT-based drone for improvement of crop quality in agricultural field**

"Saha, A.K., Saha, J., Ray, R., Sircar, S., Dutta, S., Chattopadhyay, S.P. and Saha, H.N.", in 2018 [20] researched that the need for increased population and agriculture is becoming increasingly frequent with unmanned air vehicles. Drones with suitable cameras, sensors and modules can help to make agriculture simpler, more effective and more accurate. The solutions proposed relating to these drones will help to expand the potential of further improvement if combined with various machine learning and the Internet of Things concepts. The relevant work in

*Agro-Economic Risks of* Phytophthora *and an Effective Biocontrol Approach*

#### **Figure 10.**

*Flow chart for detecting crop insects form.*

this field and the solutions which could be incorporated into the drone using the Raspberry Pi 3 B module were highlighted in this paper.

#### **15.1 Inference**

They conclude that drones or UAVs would be of tremendous help in agriculture as they are crucial at the start of a crop cycle with an increasing population. Not only will it minimize time, but it will also generate better cultivation based on analyzed data. The systematic monitoring will make crop management more effective. With the next developments, with less electricity usage, the output rate will increase rapidly. Drones are used in the planting of plant nutrients in the soil not just in soil and field analysis but also in planting seeds. The use of drones could also eliminate the crop monitoring obstacles that had previously been faced. Drones are not stopped here because they are integrated into hyper-spectral, thermal, or multispectral sensors and drones may detect which parts of the soil are dry. Furthermore, drones will also be used for scanning with near-infrared and visible light in order to determine crop health. Drones therefore act as an ideal aeroplane for the accuracy data collection.

#### **16. In intelligent agriculture, a vision-based flying insects counting and recognition system**

"Zhong, Y., Gao, J., Lei, Q. and Zhou, Y" in 2018 [21] studied that a Design and implementation of flying insects counting and classification system based on vision. The system is built as follows: firstly, in the surveillance area a yellow sticky trap is installed to catch flying insects, and the camera is mounted to capture images on the spot. Then the process of detection and

*Recognition and Early Stage Detection of* Phytophthora *in a Crop Farm Using IoT DOI: http://dx.doi.org/10.5772/intechopen.97767*

#### **Figure 11.**

*The components of agricultural monitoring service platform.*

coarse counting on the basis of the detection and classification of objects by You Only Look Once (YOLO), using global functionalities is designed. Six insect species including fly, fruit fly, bee, mouth, moth, and chaff have been chosen to evaluate the efficacy of the system. The Raspberry PI insect counting and recognition system is implemented. The results of the tests show promising performance compared to conventional methods. On Raspberry PI the average counting accuracy is 92.5% and average classification accuracy is 90.18 percent. The system proposed is simple to use and provides fast and reliable identification data; it can therefore be applied for smart farming applications (**Figure 11**).

#### **17. Pest24: a large-scale very small object data set of agricultural pests for multi-target detection**

"Wang, Q.J., Zhang, S.Y., Dong, S.F., Zhang, G.C., Yang, J., Li, R. and Wang, H.Q.", in 2020 [22] studied that Accurate agriculture poses new challenges for onsite pest monitoring in real time based on the new AI technology generation. This paper establishes a large-scale standardised data collection of agricultural pests, called Pest24 to provide a large data resource for the training of profound learning models for the detection of pests. In particular, the current data set consists of 25.378 images from our automatic plague trap and imaging system annotated with pests. 24 typical pest categories, which are mainly responsible for destroying field crops in China every year, are involved in Pest24. They use various cutting-edge methods of detection, such as RCNN Faster, SSD, YOLOv3 and Cascade R-CNN, to detect pests in the data set and to generate promising findings for in real time field pests monitoring. In the exploration of factors that impact on pest detection accuracy, the data set are analysed into a range of aspects and the three factors that mainly affect the performance of the pest detection, i.e. relative scale, number of instances and adhesion of objects. Overall, Pest24 usually features large-scale multipest image data, small object sizes, high object resemblance and dense pesticide distribution.


#### **18. Internet of things for smart agriculture: technologies, practices and future direction**

"Ray, P.P" in 2017 [23] studied that innovative science in agriculture has taken a new path with the introduction of the Internet of Things (IoT). IoT must be widely experimented to be applied in different agricultural applications at the emerging stage. In this paper, they review many possible IoT applications and the particular

*Recognition and Early Stage Detection of* Phytophthora *in a Crop Farm Using IoT DOI: http://dx.doi.org/10.5772/intechopen.97767*

questions and challenges relating to improved agricultural IoT deployment. The devices and wireless communication technologies linked to IoT are evaluated extensively to meet the unique requirements of agricultural and agriculture applications. Research has been carried out on those IoT sensor systems which deliver intelligent and intelligent services to smart farming. Various case studies are provided to analyse the IoT-based solutions implemented according to their implementation parameters by different organisations and individuals and categories. The related issues in these solutions are also illustrated, while defining progress factors and potential IoT work maps.

#### **19. Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks**

"Jiang, P., Chen, Y., Liu, B., He, D. and Liang, C.", in 2019 [24] examined that The Alternarial leaf spot, the brown spot, the mosaic, the grey spot and Rust are five common types of apple leaf conditions. However, the detection of apple diseases in current research is not reliable and easy to ensure that the apple industry is growing healthy. This paper provides a deeper learning approach aimed at the detection in real time of enhanced neural networks (CNNs). This article is the first use of the Apple Leaf Disease Data Set (ALDD) data augmentation and annotation technology which consists of laboratory pictures and complex images in real life. Based on this proposal a new apple leaf detection model that uses deep-CNN is proposed by integrating the GoogLeNet Inception and Rainbow concatenation system. Finally, five common Aple Leaf diseases using a dataset of 26,377 image of diseased Apple leaves are trained by the proposed INAR-SSD (SSD with Initiation and Reinbow Concatenation Module) model. The experimental results show that a high rate of 23.13 FPS 78.80 per cent of mAP is used for the INAR-SSD model in the ALDD. The results show that an early diagnosis approach to apple leaf disease has been established by the new INAR-SSD model, which detects these diseases in real time with greater precision and rapidity than before.

#### **20. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection**

"Behmann, J., Mahlein, A.K., Rumpf, T., Römer, C. and Plümer, L.", 2015 [25] studied that the early and accurate biotic stress detection is needed for effective crop protection. Important results have been obtained in recent years on the early identification of weeds, herbal diseases and insect pesticides in crops. The findings relate both to the development of optical sensors that are non-invasive high resolution and methods for data processing that can manage signals from the resolution, size and complexity of these sensors. Several machinery learning methods, such as vector machines and neural classification (supervised learning) networks, have been used for precision agriculture (unsupervised learning). These methods can measure both linear and non-linear models and require few statistics. Early detection of plant diseases by the use of supervised or unattended learning methods, and weed detection by the use of formal descriptors, is effective applications. A brief introduction to machine learning that an analysis of its potential to protect precision crops and an overview of instructive examples from several areas.

#### **21. A novel cloud computing based smart farming system for early detection of borer insects in tomatoes**

"Rupanagudi, S.R., Ranjani, B.S., Nagaraj, P., Bhat, V.G. and Thippeswamy, G.", 2015 [26], examined that farmers suffer tremendous losses every year as a result of plague infestations and that affects their livelihoods. In this article we will address a new solution to this problem by continuous video processing, cloud computing and robotics monitoring of crops. The paper describes the methods for pesticide detection in one of the most popular tomato fruits in the world. "An insight into how in this project even the notion of the Internet of Things can be conceptualized".

#### **22. Wireless sensor network based automated irrigation and crop field monitoring system**

"Nisha, G. and Megala, J." in 2014 [27] studied that wireless Sensor Automatic irrigation system based on a network to maximise agricultural water use. A network of wireless sensors and temperature sensors distributed with solid moisture in the field is the unit. The Zigbee Protocol is used for the management of sensor information and for the control by means of an algorithm with sensor threshold values to a microcontroller irrigation system. A solar panel and a mobile Internet guide are used for the unit. A wireless camera in the field with image processing techniques monitors the area of the disease. The device is low costs and benefits power independence in water-limited geographically isolated areas.

#### **23. A review of recent sensing technologies to detect invertebrates on crops**

This study is performed by "Liu, H., Lee, S.H. and Chahl, J.S." in 2017 [28]. In order to detect pesticides more effectively, researchers have developed different technologies. The existing sensing technology, however, is still limited to effective field applications. This review paper is designed for exploring relative technologies and finding a method for the sensing and detection of crop invertebrates such as butterflies, sauté, snails and slugs. Two main areas for the identification and detection of invertebrates were identified: acoustic sensing and vision system (MVS). The acoustic sensor is suitable for detection and identification of soil pests, stored grains and wood, whereas acoustic sensors must usually be fitted to inspection samples causing difficulties for efficient inland applications. MVS has the potential to detect and identify invertebrates in crops in a more efficient and flexible manner. The invertebrate identification technologies have recently been studied in detail with MVS, but the detection of infertile fields is relatively weak. This study summarizes current research deficiencies and discusses possible research directions.

#### **24. IOT based strawberry disease prediction system for smart farming**

This study performed by "Kim, S., Lee, M. and Shin, C.", 2018 [29]. In this study cloud-based technology was built to manage information

*Recognition and Early Stage Detection of* Phytophthora *in a Crop Farm Using IoT DOI: http://dx.doi.org/10.5772/intechopen.97767*

#### **Figure 12.**

*The Internet of Things (IoT)-Hub is used in this network model. FaaS: Farm as a Service; api: application programming interface; LoRa: Long Range.*

gathering, analysis and prediction in a shared framework. The proposed integrated system Farm as a Service (FaaS) assists high-level applications support through farms activity and observing and associated equipment, data and models management. Such a system records, connects and manages IoT devices and analyses information about the environment and growth. Furthermore, this study consisted of the IoT-Hub network model. Effective data transmission for every IoT device and communication for non-standard items is supported by this model and is highly reliable in communication in most difficult circumstances. IoT-Hub thus make sure the steadiness of agricultural environment-specific technology. Specific systems are implemented at different levels in an integrated agricultural specialist FaaS system. A strawberry infection prediction system has been designed and analysed, and this system has been compared with other infection models (**Figure 12**).

#### **25. A multispectral 3-D vision system for invertebrate detection on crops**

"Liu, H., Lee, S.H. and Chahl, J.S.", in 2017 [30] examined that the benefits of multi-spectral and hyperspectral vision systems have been demonstrated to detect such invertebrate pests efficiently and accurately. However, the identification of certain camouflaged pests on host plants has been restricted by only use of spectral details. Three-dimensional (3-D) representations are widely studied for multifaceted object recognition and scene perception in many fields. However, because of a lack of appropriate data collection methods and efficient algorithms, 3-D technologies have no invertebrate detection applications. "They created a multi-spectral vision system that enables the development of denser plant and pest clouds with multi-spectral images of UV, blue, green, red, and

near-infraround". An algorithm was designed to differentiate wide leafs from relatively large pests in nuclei at the noisy stage. The vision can be used as an automated pesticide sprayer sensor, or to support advanced pesticide monitoring systems.

#### **26. Design of intelligent agriculture management information system based on IoT**

Yan-e, D., in 2011 [31] studied that Agricultural IT (AIT) has been the most productive means and instruments for improving agriculture productivity and making maximum use of agricultural resources and has been widely used on all aspects of agricultural. The use of information technology from agricultural production as an important sub technology of AIT measures the level of agricultural computerization and the efficacy of decision-making on farm production. In this paper the methods for MIS intelligent agriculture design and architecture is discussed based on the implementation of the concept of management of agricultural knowledge and analysis of agricultural data characteristics (**Figure 13**).

**Early detection of** *Phytophthora* **spp using IOT and machine learning architecture**

**Figure 13.** *Investigation in Crop Production of Agriculture Information Management Flow.*

*Recognition and Early Stage Detection of* Phytophthora *in a Crop Farm Using IoT DOI: http://dx.doi.org/10.5772/intechopen.97767*

**Figure 14.** *IoT architecture for* Phytophthora *spp. detection.*

#### See **Figure 14**.

The above flow is a combination of IoT networks and machine learning models to predict and recommend the *Phytophthora* spp. from crops. Here the initial steps to form the architecture are network deployment and configure with initial node parameters. This phase have various sub-modules where the network nodes placed on field, sensing areas formations etc. Once the nodes deployed in the field, network node regularly sense data in the form of captured frames and send it to the base station. The base station having various detection and recommendation models to analyse the data frames.

All transmitted frames from the field to the base station formed in the actual format and load in the ML models. ML models in the network base station are already trained models for various crops disease properties and labels. These are able to classify the captured properties and recommend the disease and various possible treatments. After classification, if the model predicts infection then it suggests the medicines and track for future updates.



*Recognition and Early Stage Detection of* Phytophthora *in a Crop Farm Using IoT DOI: http://dx.doi.org/10.5772/intechopen.97767*


*DOI: http://dx.doi.org/10.5772/intechopen.97767 Recognition and Early Stage Detection of* Phytophthora *in a Crop Farm Using IoT*


#### **27. Conclusion**

A literature review on IoT devices to identify and track insects in crop fields is discussed in this article. Each solution has been seen to have its possibilities and limitations. The species of pests must be increased and the identification must be improved. Detailed knowledge on the real-time and historical context is required to ensure effective control and allocation of capital. The Internet of Things allowed the rapid and efficient tracking of agricultural crops so as to increase the crop production and hence the farmer's revenues. In order to gather information on crop conditions and environmental changes, the wireless sensors network and sensors of various kinds were used.

This information is transmitted to the equipment network which initiates corrective action. For remote insect control and automatic insect identification, IoT and deep learning technologies are used. The Faster RCNN ResNet IoT Target Recognition Platform can be used to automatically identify the insect type with a four-layer IOT for remote trap insect monitoring.

#### **Author details**

Pooja Vajpayee1 \* and Kuldeep Kr. Yogi2

1 Department of Computer Science and Engineering, RKGIT, Ghaziabad, India

2 Department of Computer Science and Engineering, Banasthali Vidyapith, Jaipur, India

\*Address all correspondence to: poojafcs@rkgit.edu.in

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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## *Edited by Waleed Mohamed Hussain Abdulkhair*

This book examines the risk of *Phytophthora* infection on different economic plants. It is divided into three sections that address the threats of *Phytophthora* infections to economic plants in Egypt and Ghana, the biocontrol of *Phytophthora* infections, and the prevalence and recognition of *Phytophthora* infection. This book discusses significant aspects of *Phytophthora* diseases as well as methods of their control to maintain sustainable agriculture and national economy. It is a valuable scientific resource for farmers, agriculturists, and other interested readers.

Published in London, UK © 2021 IntechOpen © Andrii Yalanskyi / iStock

Agro-Economic Risks of *Phytophthora*

and an Effective Biocontrol Approach

Agro-Economic Risks of

*Phytophthora* and an Effective

Biocontrol Approach

*Edited by* 

*Waleed Mohamed Hussain Abdulkhair*