**2.3 Soil indicators**

Soil indicators fill the gap of traditional soil testing because merely measuring and reporting individual parameters is no longer sufficient; it requires an in-depth understanding of soil quality by inferring various parameters. U.S.D.A. classified the soil into four classes, such as visual, physical, chemical, and biological indicators. Visual was mostly observed to be a conventional type and mainly analysed by farmers through local knowledge and also obtained through photographic interpretation, subsoil exposure, erosion, presence of weeds and colour. The physical indicators connected to the organisation of the particles and pores like particle-size distribution, aggregate stability, max. Root depth, penetration resistance, hydraulic conductivity, infiltration rate, water holding capacity, water content, porosity, soil depth, particle density, water-dispersible clay, shear strength, stone content, clay mineralogy, total surface area¸ soil odour [48–56]. The chemical property such as pH; T.O.C. or organic matter, Nutrient Availability electrical conductivity; selected heavy metals, organic pollutants, particulate matter [55–66] Soil respiration; N. mineralisation, earthworms, nematodes respiration, urease activity enzyme activities, total species number, fungal biomass functional diversity, bacterial biomass, potential denitrification activity, potential amonium oxidation, mycorrhiza populations root health, soil fauna diversity, phosphatase activity, microbial diversity are the biological indicators that measure the quality of soil [49, 54, 67–74]. The selection of these indicators needs to ensure that they are sensitive and responsive to pressure and change in land use management. **Table 1** infers that indicators measured for different countries (**Table 1**). Soil indicators refer to the capacity of soil to perform crop production that used in response to the dynamic changes in an agroecosystem.


#### **Table 1.**

*Different types of indicators used for different countries.*

*Internet of Things and Machine Learning Applications for Smart Precision Agriculture DOI: http://dx.doi.org/10.5772/intechopen.97679*
