Advancing Plant Leaf Disease Identification Using Improved Residual Networks
Vol 6, Issue 5, 2023
VIEWS - 260 (Abstract) 87 (PDF)
Abstract
In agriculture, crop yield and quality are critical for global food supply and human survival. Challenges such as plant leaf diseases necessitate a fast, automatic, economical, and accurate method. This paper utilizes deep learning, transfer learning, and specific feature learning modules (CBAM, Inception-ResNet) for their outstanding performance in image processing and classification. The ResNet model, pretrained on ImageNet, serves as the cornerstone, with introduced feature learning modules in our IRCResNet model. Experimental results show our model achieves an average prediction accuracy of 96.8574% on public datasets, thoroughly validating our approach and significantly enhancing plant leaf disease identification.
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DOI: https://doi.org/10.24294/ijmss.v6i5.3639
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