Advancing Plant Leaf Disease Identification Using Improved Residual Networks

Xiong Bi ,Hongchun Wang

doi:10.24294/ijmss.v6i5.3639

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Advancing Plant Leaf Disease Identification Using Improved Residual Networks

Xiong Bi

Hongchun Wang

International Journal of Mathematics and Systems Science 2023, 6(5); https://doi.org/10.24294/ijmss.v6i5.3639

Submitted：13 Dec 2023

Accepted：13 Dec 2023

Published：18 Dec 2023

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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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References

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