Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method

Chenglin Wang, Haoming Wang, Qiyu Han, Zhaoguo Zhang (), Dandan Kong and Xiangjun Zou
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Chenglin Wang: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650504, China
Haoming Wang: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650504, China
Qiyu Han: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650504, China
Zhaoguo Zhang: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650504, China
Dandan Kong: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650504, China
Xiangjun Zou: College of Intelligent Manufacturing and Modern Industry, Xinjiang University, Urumqi 830046, China

Agriculture, 2024, vol. 14, issue 5, 1-17

Abstract: As strawberries are a widely grown cash crop, the development of strawberry fruit-picking robots for an intelligent harvesting system should match the rapid development of strawberry cultivation technology. Ripeness identification is a key step to realizing selective harvesting by strawberry fruit-picking robots. Therefore, this study proposes combining deep learning and image processing for target detection and classification of ripe strawberries. First, the YOLOv8+ model is proposed for identifying ripe and unripe strawberries and extracting ripe strawberry targets in images. The ECA attention mechanism is added to the backbone network of YOLOv8+ to improve the performance of the model, and Focal-EIOU loss is used in loss function to solve the problem of imbalance between easy- and difficult-to-classify samples. Second, the centerline of the ripe strawberries is extracted, and the red pixels in the centerline of the ripe strawberries are counted according to the H-channel of their hue, saturation, and value (HSV). The percentage of red pixels in the centerline is calculated as a new parameter to quantify ripeness, and the ripe strawberries are classified as either fully ripe strawberries or not fully ripe strawberries. The results show that the improved YOLOv8+ model can accurately and comprehensively identify whether the strawberries are ripe or not, and the mAP50 curve steadily increases and converges to a relatively high value, with an accuracy of 97.81%, a recall of 96.36%, and an F1 score of 97.07. The accuracy of the image processing method for classifying ripe strawberries was 91.91%, FPR was 5.03%, and FNR was 14.28%. This study demonstrates the program’s ability to quickly and accurately identify strawberries at different stages of ripeness in a facility environment, which can provide guidance for selective picking by subsequent fruit-picking robots.

Keywords: fruit detection; ripeness identification; deep learning; YOLOv8 model; computer vision (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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