Identification of Paddy Leaf Disease using Machine Learning Methods

The skill of growing fruits and vegetables is known as horticulture, and it is the foundation of both business and well-being. Crop observation and disease detection have even taken precedence over conventional methods of modern farming, despite those techniques being rendered outdated by ignorance of plant illnesses, which can result in financial losses. Plant disease detection and treatment strategies are becoming more sophisticated as horticulture moves toward the Industrial Internet of things (IIoT) 5.0. This effort focuses on creating a model that can precisely diagnose rice leaf disease by examining the leaves of rice plants in order to accomplish this aim. Utilizing a logistic regression classification algorithm, the ResNet50 model based on R-CNN has been evaluated with a dataset of plant leaf image information. 97% accuracy is attained with the Resnet-50 model when compared with other models namely, DenseNet-161 and Inception Net V3 +RCNN.