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Research Paper | Computer Science | India | Volume 12 Issue 12, December 2023 | Popularity: 5.3 / 10
Automated DSegNet Model for the Classification and Recognition of Rice Plant Diseases using Transfer Learning
D. Felicia Rose Anandhi, S. Sathiamoorthy
Abstract: Detecting and categorizing diseases in rice plants involves the application of techniques like Computer Vision (CV) and Machine Learning (ML) to identify and classify ailments impacting rice crops. The utilization of these methods has the potential to aid agricultural professionals and farmers in swiftly identifying and managing diseases, ultimately contributing to food security and optimal crop yields. The classification and recognition of rice plant diseases using Deep Learning (DL) have emerged as a successful approach for automated disease detection. As a subset of Artificial Intelligence (AI), DL concentrates on training neural networks with diverse layers to autonomously acquire complex representations and patterns from data. This research employs the Deep-SegNet model in developing the DSegNet method for the Automatic Recognition and Classification of Rice Plant Diseases. The DSegNet approach integrates various stages to enhance accuracy and diagnostic performance. At the initial level, a preprocessing stage is implemented, involving image resizing and the application of a Bilateral Filter (BF) to enhance image quality. Subsequently, SegNet-based segmentation is employed to identify the disease-affected areas, and feature extraction is carried out using the MobileNetV3 architecture. Finally, the extracted features are input into a Support Vector Machine (SVM) classification model to differentiate between different disease types. A comprehensive analysis of experimental results demonstrates that the DSegNet technique outperforms other recent approaches in terms of performance.
Keywords: Deep Learning, Segmentation, Rice Disease, Machine Learning, Transfer Learning, Computer Vision
Edition: Volume 12 Issue 12, December 2023
Pages: 1936 - 1944
DOI: https://www.doi.org/10.21275/SR231220124427
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