What is it about?
A current research work in the agriculture area involves recognizing and classifying plants diseases based on live images. Farmer can traditionally do the cultivation process, hence here the identification of the disease was by manual (visual appearance) or send the sample data set to the nearest laboratory. In our proposed method we will provide accurate and early detection of various diseases in Oryza sativa (rice) plants, that can help the farmers in applying suitable treatment on the rice plants and improve productivity.
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Perspectives
We are using optimized deep learning models such as the ResNet-152, CoAtNet for classification and identify the diseases. We have captured healthy and unhealthy images from Villupuram district, Tamil Nadu, India. The total amount of captured images was 3071 from our farmer's field with proper sunlight. It was highly efficient and detects the diseases or recognizes the diseases from the captured image with different categories (Bacterial Leaf Blight, Leaf Blast, Brown Spot, and Tungro / Leaf smut).
Anandhan Karunanithi
GITAM University
Read the Original
This page is a summary of: Enhanced Hybrid Neural Networks (CoAtNet) for Paddy Crops Disease Detection and Classification, Revue d intelligence artificielle, December 2022, International Information and Engineering Technology Association,
DOI: 10.18280/ria.360503.
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