Diagnosis of bacterial leaf blight, leaf smut, and brown spot in rice leafs using VGG16

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In the context of agricultural sustainability and food security, timely and precise identification of plant diseases holds paramount significance. This research proposes an innovative approach to diagnose bacterial leaf blight (BLB), rice blast (RB), and brown spot (BS) in rice leaves utilizing the VGG16 convolutional neural network (CNN). By leveraging deep learning capabilities, the proposed model analyzes high-resolution images of rice leaves to classify and distinguish between the different diseases. The VGG16 architecture has shown remarkable performance in image recognition tasks, and this study aims to harness its potential for precision agriculture. Rice (Oryza sativa) is a staple crop that plays a crucial role in global food security. Timely and precise diagnosis of these diseases is vital for effective disease management and crop protection. Our methodology involves the collection and curation of a diverse dataset of rice leaf images. The VGG16 model, a proven deep convolutional neural network (CNN) architecture, is employed for extraction of features and classification. The results of our study demonstrate the efficacy of the VGG16 model in distinguishing between different rice leaf diseases with remarkable precision. This high level of accuracy holds great promise for the field of agriculture, offering a non-invasive and efficient means of disease detection. Early disease identification can facilitate timely intervention, reducing crop losses and mitigating the economic impact on farmers. The classification accuracy of the proposed method is 97.77% using the publicly available dataset.