Cotton Health-Guard: AI-Enhanced Crop Health Assessment Through Image Classification

2. Purpose Of The Study

India is an agriculture-based economy, though the service sector is also contributing to the growth of the economy. In India, the major employment is provided by agriculture, nearly one-third of the population is employed in agriculture (“FAO in India,” n.d). The contribution of the agriculture sector to the GDP is nearly 20% of the economy. Cotton (Gossypium hirsutum L.), popularly known as “white gold”, is a natural fiber crop that is grown in India (Nikam et al., 2017).

As per (Cotton Manufacturing Companies, Cotton Manufacturers and Exporters in India, 2022; Cotton update, 2022) the production of cotton in India is 352.48 lakh bales for the year 2021 and estimated to be 340.62 lakh bales for the year 2022. As per the ICAC report, 37% of the world's cotton cultivation area is in India, making it the highest cotton-producing country (Cotton Corporation India Ltd, 2022). However, one of the problems the farmers are facing is the damage caused to the plants by insect pests, notably those caused by Helicoverpa armigera, commonly referred to as American Bollworm (Yadav & Goel, 2019). Nearly 40% of the pesticides used in India are only for the cotton crop (Mollaee et al., 2019; Training Manual on “Cultivation of Long Staple Cotton (ELS),” 2007). The basic purpose of this study is to create a model that can be used to identify the cotton crop’s infection based on the image of the leaf. Using the image, we will be classifying whether the crop is infected or not. Using a drone and mobile camera, an image will be captured and fed into the model, which will inform the farmers how much is the infection in the plant and on the farm, based on it we can also suggest the pesticides required.