Intelligent recognition system for citrus plant diseases based on image analysis

Hong-Hua Qiu; Jian-Wen Guo; Yu-Chen Li; Xiang Huo; Yong-Heng Cai

doi:10.1117/12.3011278

8 November 2023 Intelligent recognition system for citrus plant diseases based on image analysis

Hong-Hua Qiu, Jian-Wen Guo, Yu-Chen Li, Xiang Huo, Yong-Heng Cai

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129231M (2023) https://doi.org/10.1117/12.3011278
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

Deep learning has been extensively researched for plant disease image classification, and there is a need to develop a crop disease image recognition system that provides intelligent information services to farmers. This study focuses on citrus plants and establishes a dataset comprising infected and healthy citrus leaf images. By combining diffusion models and transfer learning, four models (VGG16, ResNet18, Efficient Net, and Shuffle Net) are trained using three different approaches to evaluate their performance. The study aims to identify the most suitable model and develop an intelligent recognition system for citrus plant diseases based on image analysis. The feasibility of using deep learning for citrus disease classification is preliminarily validated using the created dataset.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hong-Hua Qiu, Jian-Wen Guo, Yu-Chen Li, Xiang Huo, and Yong-Heng Cai "Intelligent recognition system for citrus plant diseases based on image analysis", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129231M (8 November 2023); https://doi.org/10.1117/12.3011278

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KEYWORDS

Education and training

Diseases and disorders

Data modeling

Deep learning

Intelligence systems

Machine learning

Diffusion

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