Rice Leaf Disease Detection and Remedies using Deep Learning
Keywords:
Bacterial Leaf Blight, Brown Spot, Convolutional Neural Network, Leaf Blast, Tungro.Abstract
This research provides a unique method using convolutional neural networks (CNN) for the automated diagnosis of four important rice leaf diseases: Leaf Blast, Leaf Blight, Tungro, and Brown Spot. The CNN model is trained using a dataset that includes pictures of both healthy and sick rice leaves, with training and testing conducted at an 8020 ratio respectively. When it comes to correctly determining if certain illnesses are present in rice leaves, the trained model performs admirably. Additionally, a user-friendly website interface is created so that users can upload pictures of infected rice leaves to diagnose diseases in real-time. After the diagnosis, the website offers customized recommendations categorized into 3 namely chemical pesticides, botanical pesticides, and biopesticides for treating the particular ailment found, which is a great help to farmers in properly maintaining crop health. By bridging the gap between cutting-edge technology and agricultural methods, this integrated system presents a viable way to increase the sustainability and production of rice crops attaining an accuracy of 98%.
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