Performance Analysis of Multiple Leaf Disease Detection in Plants Using CNN Model

Agriculture provides food for everyone, even in situations of rapid population growth. It is advisable to detect plant illnesses in their early phases of development in order to produce enough food for the entire population. Modern deep learning methods, machine learning, and Convolutional neural network have the capability to transform crop management and disease detection in the field of precision agriculture. This project offers a holistic response that makes use of these cutting-edge technologies to solve a variety of issues that farmers must deal with, including the timely and accurate identification of plant diseases and the provision of efficient treatment options. The development and optimization of deep learning models, particularly CNNs, to accurately identify plant diseases forms the basis of this study. These models were developed using large image datasets that included both healthy plants and various illness symptoms. The use of CNNs makes it possible to extract complex information from images, facilitating accurate disease classification. We present a novel multimodal fusion strategy because we understand that there are many other aspects besides visual cues that have an impact on plant health.