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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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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
PubMed
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.

Keywords:
I-GhostNetV3attention mechanismslightweight convolutional networksrice disease classificationsmart agriculturevision sensors

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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.