A Method for Sorting High-Quality Fresh Sichuan Pepper Based on a Multi-Domain Multi-Scale Feature Fusion Algorithm

Post-harvest selection of high-quality Sichuan pepper is a critical step in the production process. To achieve this, a visual system needs to analyze Sichuan pepper with varying postures and maturity levels. To quickly and accurately sort high-quality fresh Sichuan pepper, this study proposes a mult...

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Bibliographic Details
Published in:Foods 2024-08, Vol.13 (17), p.2776
Main Authors: Xiang, Pengjun, Pan, Fei, Duan, Xuliang, Yang, Daizhuang, Hu, Mengdie, He, Dawei, Zhao, Xiaoyu, Huang, Fang
Format: Article
Language:English
Subjects:
Accuracy
Agriculture
Algorithms
Classification
Crops
Datasets
Deep learning
Defective products
Efficiency
Harvest
Harvesting
Image quality
instance segmentation
Machine learning
machine vision
Maturity
maturity classification
Modules
Robots
Sichuan pepper sorting
smart agriculture
Temperature
Vegetables
Vision systems
Visual system
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Summary:Post-harvest selection of high-quality Sichuan pepper is a critical step in the production process. To achieve this, a visual system needs to analyze Sichuan pepper with varying postures and maturity levels. To quickly and accurately sort high-quality fresh Sichuan pepper, this study proposes a multi-scale frequency domain feature fusion module (MSF3M) and a multi-scale dual-domain feature fusion module (MS-DFFM) to construct a multi-scale, multi-domain fusion algorithm for feature fusion of Sichuan pepper images. The MultiDomain YOLOv8 Model network is then built to segment and classify the target Sichuan pepper, distinguishing the maturity level of individual Sichuan peppercorns. A selection method based on the average local pixel value difference is proposed for sorting high-quality fresh Sichuan pepper. Experimental results show that the MultiDomain YOLOv8-seg achieves an mAP50 of 88.8% for the segmentation of fresh Sichuan pepper, with a model size of only 5.84 MB. The MultiDomain YOLOv8-cls excels in Sichuan pepper maturity classification, with an accuracy of 98.34%. Compared to the YOLOv8 baseline model, the MultiDomain YOLOv8 model offers higher accuracy and a more lightweight structure, making it highly effective in reducing misjudgments and enhancing post-harvest processing efficiency in agricultural applications, ultimately increasing producer profits.
ISSN:2304-8158
2304-8158
DOI:10.3390/foods13172776