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I-GhostNetV3: A Lightweight Deep Learning Framework for Vision-Sensor-Based Rice Leaf Disease Detection in Smart
Puyu Zhang1, Rui Li1, Yuxuan Liu2
1College of Electronic Information Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China.
Sensors (Basel, Switzerland)
|February 13, 2026
Summary
This study introduces I-GhostNetV3, an efficient AI model for identifying rice leaf diseases using computer vision. It achieves high accuracy in complex field conditions, aiding smart agriculture applications.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Agricultural Technology
Background:
- Accurate rice leaf disease diagnosis is vital for smart agriculture.
- Lightweight Convolutional Neural Networks (CNNs) face challenges in complex field environments due to lesions, background clutter, and lighting variations.
Purpose of the Study:
- To develop an improved lightweight CNN, I-GhostNetV3, for RGB rice leaf disease recognition.
- To enhance lesion representation and suppress background interference for more robust disease identification.
Main Methods:
- I-GhostNetV3 incorporates Adaptive Parallel Attention (APA) for enhanced lesion features and Fusion Coordinate-Channel Attention (FCCA) for background suppression.
- The model is based on GhostNetV3, with modular enhancements designed for controlled computational overhead.
Main Results:
- I-GhostNetV3 achieved 90.02% Top-1 accuracy on the Rice Leaf Bacterial and Fungal Disease (RLBF) dataset.
- The model has 1.831 million parameters and 248.694 million FLOPs, outperforming MobileNetV2 and EfficientNet-B0.
- Performance was validated on the PlantVillage-Corn dataset as a transfer learning check.
Conclusions:
- I-GhostNetV3 demonstrates high efficiency and accuracy for rice leaf disease recognition.
- The model shows promise as an efficient backbone for edge deployment in precision agriculture.
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