ANN-Based Model for Analysis and Determination of Crop Damage

An essential part of India’s economy is agriculture. In rural areas, most people depend on agriculture for a living, growing cereals, vegetables, fruits, and spices. It affects a person's and country's economies since these products are very profitable. Still, crops can be damaged for various reasons, including crop diseases, pesticide damage, and other causes. It will be a significant loss for farmers. Aside from that, the country's economy is negatively affected by a lack of supply in the agricultural products market. So, crop damage assessment in the early stages will benefit farmers, as it allows them to take necessary action. This study presents an investigation into the utilization of machine learning (ML) methods within the agricultural industry. The study is centered on the categorization and prediction of crop impairment resulting from diseases and pests. The framework of Artificial Neural Network(ANN), which is a machine learning algorithm capable of categorizing crop damage, is introduces in this paper. The ANN results with 91.69% training accuracy and 83.48% testing accuracy. This trained model is used for crop damage assessment.