Deep learning system for paddy plant disease detection and classification

Automatic detection and analysis of rice crop diseases is widely required in the farming industry, which can be utilized to avoid squandering financial and other resources, reduce yield losses, and improve treatment efficiency, resulting in healthier crop output. An automated approach was proposed for accurately detecting and classifying diseases from a supplied photograph. The proposed system for the recognition of rice plant diseases adopts a computer vision-based approach that employs the techniques of image processing, machine learning, and deep learning, reducing the reliance on conventional methods to protect paddy crops from diseases like bacterial leaf blight, false smut, brown leaf spot, rice blast, and sheath rot, the five primary diseases that frequently plague the Indian rice fields. Following image pre-processing, image segmentation is employed to determine the diseased section of the paddy plant, with the diseases listed above being identified purely on the basis of their visual contents. An integration of a support vector machine classifier and convolutional neural networks are used to recognize and classify specific varieties of paddy plant diseases. With ReLU and softmax functions, the suggested deep learning-based strategy attained the highest validation accuracy of 0.9145. Following recognition, a predictive remedy is recommended, which can assist agriculture-related individuals and organizations in taking suitable measures to combat these diseases.

Automated summary

Automatic detection and analysis of rice crop diseases is essential in the farming industry to prevent resource wastage, reduce yield losses, and improve treatment efficiency. A computer vision-based approach using image processing, machine learning, and deep learning techniques is proposed to accurately detect and classify rice plant diseases. Five primary diseases that frequently affect Indian rice fields can be identified through image segmentation and visual content recognition. The suggested deep learning-based strategy achieved a high validation accuracy of 0.9145 using a support vector machine classifier and convolutional neural networks. After recognition, a predictive remedy is recommended to assist agriculture-related individuals and organizations in combating these diseases.