Deep Learning Integration In Agriculture Framework For Farmer Sustainability

Early diagnosing of plant diseases is crucial in safeguarding the well-being of crops and ensuring maximum agricultural productivity. Identification of diseases using the standard protocols is usually time-consuming and requires expertise knowledge, which renders it ineffective. This project presents a deep learning framework for solving these problems, which is aimed at increasing sustainability in agriculture by increasing production, managing resources, and preserving ecological health. The framework is built on cutting-edge deep learning technologies, such as DenseNet121, to identify and categorize diseases in plants based on images. The user provides images of the plant, which are sent to the trained model for disease identification.The system, upon identification, gives specific fertilizer recommendations so that the identified nutritional deficiencies can be solved and plant health can be improved. The framework’s performance is evaluated using accuracy, precision, recall, F-measure, and other metrics constructed on the ground truth dataset of plant images. These metrics provide assurance of the performance of the disease detection system. This solution integrates automated disease detection with the ability to provide targeted fertilization recommendations, offering farmers effective tools based on data analysis with the goals of improving crop yield, adopting a sustainable approach, and enhancing agricultural productivity.