Affiliation:
1. School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei 230036, China
2. State Key Laboratory of Tea Plant Biology and Utilization, Hefei 230036, China
3. School of Engineering, Anhui Agricultural University, Hefei 230036, China
Abstract
The sorting of machine-picked fresh tea leaves after mechanized harvesting remains a challenge because of the complex morphological characteristics and physicochemical properties of fresh tea leaves. First, the recognition results of four types of models, namely, YOLOv5, YOLOv3, Fast RCNN, and SSD, were compared. It was found that YOLOv5, with guaranteed recognition accuracy, had a recognition speed of 4.7 ms/frame (about four times that of the second ranked YOLOv3). Therefore, this study presents a novel fresh tea leaf sorting system that provides rapid and high-precision multi-channel sorting for four grades of tea leaves using a tea leaf recognition model based on the You Only Look Once (YOLOv5) deep learning model. Subsequently, a raw dataset, consisting of 6400 target images of different grades and different moisture contents, was used to evaluate three different optimization methods. Among these, the Stochastic Gradient Descent (SGD) optimization method was found to provide the best model training results with an average recognition accuracy of 98.2%. In addition, the recognition efficacy of the recognition model was found to be positively correlated with the gradient coverage of tea’s moisture content in the training set. Theoretical analysis was then conducted, along with the experimental investigation of the air-blowing force on the fresh tea leaves in the sorting process, with 30° determined to be the optimal air-blowing angle. Finally, the overall results showed that the construction of the full moisture content training set enabled a model recognition accuracy of up to 88.8%, a recall of 88.4%, a recognition speed of 4.7 ms/frame, and an overall sorting accuracy of 85.4%. This result is promising for multi-channel sorting of fresh tea leaf grades in complex situations, and as such provides a strong basis for the application of tea leaf sorting equipment.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Anhui Provincial Education Department Key Projects
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference29 articles.
1. Research progress of classification technology and related equipment for machine-picked tea fresh leave;Tang;China Tea Process.,2015
2. Luo, K., Wu, Z., Cao, C., Qin, K., Zhang, X., and An, M. (2022). Biomechanical characterization of bionic mechanical harvesting of tea buds. Agriculture, 12.
3. Research on the evaluation model of tea stall green based on multispectral image parameters;Zhang;J. Zhejiang Univ. Technol.,2017
4. Automated strawberry sorting system based on image processing;Xu;Comput. Electron. Agric.,2010
5. Application of machine learning algorithms in quality assurance of fermentation process of black tea- based on electrical properties;Zhu;J. Food Eng.,2019
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