**5. Future work**

After reviewing the research, multispectral and hyperspectral imaging techniques proved to be the most reliable indirect method. However, hyperspectral imaging is very costly, and there is still a limitation in the capability of designing systems for the detection of diseases in real-time under field conditions. Most preharvest and postharvest techniques are completed under controlled environments as seen in **Table 4** and automation techniques are developed mainly for postharvest produce for fruit sorting as seen in **Table 5**. Because the use of preharvest techniques provides an early analysis of disease severity, preharvest is more suitable to use over postharvest techniques.

PCA is an effective tool in reducing data dimensionality and to enhance bruise features. Results showed that the wavebands centered at 558, 678, 728, and 892 nm were optimal in detecting bruises on 'Golden Delicious' apples. A simple classification technique was introduced to determine whether apples are bruised or intact. This classification technique resulted in an accuracy of 93.5% for detecting intact

Both preharvest and postharvest imaging and VOC profiling techniques have proven to be very effective inaccurately classifying different types of plant diseases. Not only do these techniques give a good indication of overall plant health but also can accurately distinguish healthy produce from unhealthy produce. Preharvest techniques such as [18, 20] achieved a classification accuracy of plant diseases of 82.5% and 90% respectively. Postharvest techniques showed to be more promising as seen in [6, 22] with a classification accuracy of fruit defects of 89.2% and 93% respectively. Preharvest disease detection techniques can be classified as an early disease detection method as seen in [8, 18], in which immediate action can be taken to revive plants and crops. This aspect is a major advantage and cannot be achieved with postharvest techniques. Postharvest techniques such as [6, 19] have introduced automated systems in which defected postharvest produce can be distinguished and sorted automatically with an accuracy of 93% and 86% respectively. However, the integration of automation under real-time and field-based environments is still very limited.

As seen in [8], a robot rig is used under field-based conditions to detect diseases in grapevine canopy but is controlled manually and full automation is not achieved. Also, [21] implemented a semi-autonomous robot to test soil fertility under field-based environments using multiple gas sensors, but results were ineffective in disease detection. The methods discussed differ greatly, from the use of simple digital cameras to the use of more advanced and sophisticated hyperspectral and multispectral imaging methods. Techniques such as [6, 19, 22] that use multispectral cameras and bandpass filters, show higher classification accuracy as seen in **Table 5** when

apples and about 86% for detecting bruised apples.

*Schematic of the used hyperspectral imaging system [19].*

**4. Conclusion**

**68**

**Figure 27.**

*Biomimetics*


**Table 4.** *Review of preharvest*

**Paper**

**71**

Jhuria [7] Apple Image dataset

Dubey [6] Apple Image dataset

Unay [18]

Monochrome

Jonagold

Skin defects,

Off-Site Controlled

Background

 and Lighting

bruises,

and rot

Apples

Digital Camera with

multiple bandpass filters

> Li [9]

 2

cameras with a bandpass

filter

Xing [22]

Multispectral

Golden

Apple bruises

 Off-Site Closed Chamber

 Full

Multiple

Simple

Principal

86%

Component

Analysis

automation

fruits

thresholding

Delicious

Apples

Camera (400-1000

> **Table 5.**

*Review of postharvest*

 *disease detection techniques.*

 nm)

Monochrome

Apples

 Surface defects

 Off-Site Closed Chamber

 Full

Multiple

Simple subtraction

Neural Network

 93%

automation

fruits

thresholding

 **Acquisition**

 **Method**

 **Test Fruit**

 Apples

 Apples

 Apple blotch,

rot, and scab

 Apple scab and

Off-Site Controlled

Background

Off-Site Controlled

Background

 and Lighting

 and Lighting

Apple rot

 **Target Disease**

**Environment**

**Automation/**

**Coverage**

**Segmentation**

**Classifier**

 **Accuracy**

*DOI: http://dx.doi.org/10.5772/intechopen.97612*

**Manual**

Images Taken

Single

Color and Texture

Neural Network

 High

fruit

Extraction

Manually

Images Taken

Single

K-means

Support Vector

High

*Pre-Harvest and Post-Harvest Techniques for Plant Disease Detections*

Machine

fruit

segmentation

Manually

Images Taken

Single

Isodata

Support Vector

89.20%

Machine

fruit

Thresholding

Manually

**Area**

**Method**

 *disease detection techniques.*

#### *Biomimetics*


### *Pre-Harvest and Post-Harvest Techniques for Plant Disease Detections DOI: http://dx.doi.org/10.5772/intechopen.97612*

**Table 5.** *Reviewofpostharvest*

 *disease detection techniques.*

**Paper**

**70**

Patil [13]

Li [9]

Xu [19]

 Digital Camera with

Tomato

Nutrient deficiency

 Closed Chamber

 Images Taken

Single leaf

 Percent

Fuzzy k nearest

82.50%

Histogram

neighbor

classifier

Manually

plants

0.4 million CCD pixels

> Camargo [4]

Oberti [12]

 Three-CCD

Grapevines

 Powdery mildew

On-Site

Robot Rig

Grapevine

NDVI

Local Gradient

85%

calculation

Method

canopy

Limited

automation

Controlled

Lighting

On-Site

Semiautonomous

Lawn

 NA

NA

NA

 six-

wheel robot

Multispectral

 Camera

1912x1076

Pobkrut [15]

**Table 4.**

*Review of preharvest*

 *disease detection techniques.*

 Multiple Gas Sensors

Soil fertility

VOCs

(e-nose)

 24 bit JPEG Image

Cotton Crop

 Green stink bug, Bacteria

Off-Site

 Images Taken

Single leaf

Co-occurrence

Support Vector

90%

Machine

Classifier

matrix

Manually

angular, Ascochyta blight

virus

samples

 10 Mega pixel Digital

Pomegranate

General disease spots

leaves

Camera

 12 Mega Pixel Digital

Sugarcane

Fungi diseases

Off-Site

Images Taken

Single leaf

 Triangle

NA

98%

Thresholding

Manually

Controlled

Background

 Off-Site

Images Taken

Single leaf

 K-means

Fuzzy Logic

High

Classification

clustering

Manually

Controlled

Background

leaves

Camera

 **Acquisition**

 **Method**

 **Test Plant**

 **Target Disease**

**Environment**

**Automation/**

**Coverage**

**Segmentation**

**Classifier**

 **Accuracy**

*Biomimetics*

**Method**

**Area**

**Manual**

#### *Biomimetics*

For a reliable, rapid, and field-based disease detection system, a new preharvest automated method is required. A fusion of techniques such as VOC profiling and NIR imaging methods could be integrated into a robot for processing a large number of plants. Because of the uncertainty of lighting and other conditions of the field environment more advanced tools are required to capture data without being affected. An agriculture robot can be designed to move in agriculture fields to detect stresses in areas while providing position information. RGB and NIR imaging methods could be integrated into a robot and used in synchronization to measure overall plant health using the Normalized Differential Vegetative Index (NDVI). Because of its low cost, NIR imaging techniques can be very efficient and effective when integrated with a robot. The detection of stressful areas in a field with GPS information can be used for selective pesticide spraying.

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