Neuromorphic Sensing and Temporal Neural Networks: A Novel Approach for Early Detection of Plant Diseases

India is beset with problems with crop production that impact farmers, the agricultural industry, and the country's economy. In order to solve this, a system that uses crop leaf image analysis to forecast agricultural yield was created. To categorize illnesses and suggest pesticides, the system makes use of TensorFlow technology and convolutional neural networks (CNNs). The technology combines deep learning algorithms and Java Web Services (JWS) with an Android app. After 15 epochs, the 5-layer CNN model achieved 95.05% accuracy out of three CNN models with different layers (3, 4, and 5). After 20 epochs, the same model achieved 89.67% validation accuracy. This method shows how well deep learning methods and mobile app development work together to identify crop illnesses and suggest herbicides. The system provides promising ways to reduce losses and increase agricultural productivity .In order to solve this, a system that uses crop leaf image analysis to forecast agricultural yield was created. To categorize illnesses and suggest pesticides, the system makes use of TensorFlow technology and convolutional neural networks (CNNs).