Soft Computing Based Agricultural Crops Monitoring and Disease Detection

In light of the growing need for intelligent farming, it is crucial to ensure effective development & enhanced output of plants. Tracking a plant from its development to its picking is essential in order to improve output & profitability. The paper presents the development of a system that utilizes image processing techniques to identify and monitor diseases in fruits during the entire process through planting to picking. This objective is accomplished by utilizing the idea of artificial neural network. The investigation focuses on four illnesses that affect tomato crops. The approach under consideration utilizes two picture databases. The initial dataset is utilized to train contaminated photos, while the subsequent dataset is employed for implementing additional inquiry photographs. The training dataset undergoes weight modification through the process of back propagation. The results of the study demonstrate the categorization & matching of pictures to their corresponding groups. The photographs are classified into colour, texture, & morphological categories. The morphological characteristic yields a 94<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>accuracy rate, surpassing the performance of the remaining two characteristics. The system's design exhibits a high level of efficacy in identifying the propagation of diseases. The technique has been implemented practically using MATLAB.