Manifold Learning Algorithm Based on Constrained Particle Swarm Multi-objective Optimization

Wang, Hui; Cai, Tommaso; Wang, Yinfeng; Yang, Geng; Liang, Junwei

doi:10.1007/978-981-19-4109-2_8

Cited by 1 publication

(1 citation statement)

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“…Compared with the constrained multi-objective optimization algorithm used in the domestic mainstream detection of citrus yellow shoot, the YOLOv5 single-stage object detection algorithm has its own advantages. Due to space reasons, YOLOv5 is only listed here for comparison with some algorithms such as PSO-MOFSA, FSMEA and CMOA-CHDIA [7][8][9]. It is trained based on the same data set.…”

Section: Performance Comparisonmentioning

confidence: 99%

Citrus Yellow Shoot Disease Detection based on YOLOV5

Wang¹,

Zhang²,

Li³

2023

HSET

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Citrus yellow shoot disease (also called Huanglongbing in Chinese) is a devastating disease of citrus. A large number of experiments have shown that citrus yellow branch disease cannot be cured, but can only be prevented and controlled. Traditional detection methods are limited by long time and low coverage, and cannot predict diseases in time. Thanks to the powerful feature representation capabilities of convolutional neural networks, the detection of citrus yellow shoot disease based on deep learning has become the mainstream of current research. In this paper, we transform the recognition task of citrus greening disease into the detection of lesion area, focus on the feasibility of using deep learning association algorithm to identify the symptoms of citrus yellow shoot disease, and evaluate the recognition accuracy. Specifically, we preprocessed the collected citrus plant images to construct the training data set, and built a citrus yellow shoot disease recognition model based on the YOLOv5s algorithm. The results show that the accuracy reaches 91.3% and the recall rate reaches 88.9% after detection by YOLOv5s model. Finally, it is found that this model can accurately find citrus products with citrus yellow shoot disease, so as to timely prevention and control, improve the identification efficiency of citrus yellow shoot disease and reduce the cost of detection.

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Section: Performance Comparisonmentioning

confidence: 99%