Authors - Karuna A. Katakadhond, Manohar Madgi Abstract - Groundnut being a major oilseed crops, contributes to nearly 10% of the total value of produce from agricultural crops in India. Several researches indicate that disease infestations at different stages of crop growth can lead to 30-70% of yield reduction and significant economic losses. This challenge can be addressed by using Artificial Intelligence (AI) based smart monitoring and recommendation systems through early detection, identification, and prediction of crop diseases. The primary objective of the study is to develop an AI driven smart monitoring framework capable of detecting, identifying, and predicting biotic and abiotic factors responsible for major disease occurrences in groundnut plants. Additionally, the systems goal is to provide an effective and efficient recommendation system for sustainable agriculture from an integrated and practical perspective with its technical and economic performance to the farmers for managing the field level infestations. This includes prediction of diseases and timely recommendation of plant protection chemicals which may reduce the yield loss and enhance the productivity of the crop.
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