ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1619365
This article is part of the Research TopicPrecision Information Identification and Integrated Control: Pest Identification, Crop Health Monitoring, and Field ManagementView all 23 articles
LiSA-MobileNetV2: An Extremely Lightweight Deep Learning Model with Swish Activation and Attention Mechanism for Accurate Rice Disease Classification
Provisionally accepted
- 1College of Plant Protection, Hunan Agricultural University, Changsha, China
- 2Hunan Provincial Engineering and Technology Research Center for Agricultural Big Data Analysis and Decision-Making, Hunan Agricultural University, Changsha, Hunan Province, China
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In the context of intelligent agriculture in China, rapid and accurate identification of crop diseases is essential for ensuring food security and improving crop yield. Although lightweight convolutional neural networks (CNNs) are widely adopted for plant disease recognition due to their computational efficiency, they often suffer from limited feature representation and classification accuracy. To address these challenges, we propose LiSA-MobileNetV2, an improved MobileNetV2-based model designed for rice disease classification. First, we restructure the inverted residual module to simplify the network architecture, achieving a test accuracy of 92.32%, representing a 2.41% improvement over the original MovileNetV2 (89.91%). This indicate that a more lightweight network can enhance feature representation in specific disease recognition. Second, integrating the Swish activation function further improves accuracy to 94.04% by enhancing the model's non-linear feature learning. Finally, the addition of a squeeze-and-excitation attention mechanism raises accuracy to 95.68%, representing a 5.77% improvement over the original model.Importantly, the parameter size and FLOPs are reduced by 74.69% and 48.18%, respectively, maintaining strong computational efficiency. These results demonstrate that combining structural simplification, advanced activation, and efficient attention mechanisms significantly improves CNN performance. LiSA-MobileNetV2 provides a high-accuracy, resource-efficient solution for real-time rice disease detection in smart farming systems.
Keywords: Disease recognition, rice, Lightweight CNN, attention mechanism, activation function
Received: 28 Apr 2025; Accepted: 14 Jul 2025.
Copyright: © 2025 Xu, Li, Li, Yuan and Dai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhijun Dai, College of Plant Protection, Hunan Agricultural University, Changsha, China
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