Published Paper Details:

There are a few insect and fungal species that have been identified as agricultural pests and pathogens so far. Insect pests are a major cause of yield loss in India. Some examples include Helicoverpa armigera on pulses, cotton, vegetables, and sunflower; Spodoptera on vegetables; Pyrilla on wheat, rice, millet, and sugarcane; the recently introduced pest Ceratovacuna lanigera (woolly aphids) on sugarcane; and mealy bug on cotton (Phenacoccus gossypiphilous) and grapes (Maconellicoccus hirsutus). Therefore, the primary objective of this preliminary investigation is pest detection. Insights gained from this study will aid in the cultivation of high-quality crops for domestic consumption. The agricultural economy context is very important. Farmers might profit from pest protection for this type of crop. By keeping an eye on their crops and inspecting for pests, farmers may better target their pest control efforts. In this way, one can reduce their reliance on agrochemicals. In addition, early detection of pests allows for less intensive use of appropriate treatments, reducing the overall impact on the crop. With the use of Neural Networks, we have collected photos of plants that have been damaged by pests and have preprocessed and detected using clustering and vector machine algorithms.