Enhancing Strawberry Harvesting Efficiency through Yolo-v7 Object Detection Assessment

Authors : Mehmet NERGİZ

Pages : 519-533

Doi:10.55525/tjst.1342555

View : 45 | Download : 84

Publication Date : 2023-09-01

Article Type : Research Paper

Abstract :Strawberry fruits which are rich in vitamin A and carotenoids offer benefits for maintaining healthy epithelial tissues and promoting maturity and growth. The intensive cultivation and swift maturation of strawberries make them susceptible to premature harvesting, leading to spoilage and financial losses for farmers. This underscores the need for an automated detection method to monitor strawberry development and accurately identify growth phases of fruits. To address this challenge, a dataset called Strawberry-DS, comprising 247 images captured in a greenhouse at the Agricultural Research Center in Giza, Egypt, is utilized in this research. The images of the dataset encompass various viewpoints, including top and angled perspectives, and illustrate six distinct growth phases: \`green\`, “red”, \`white\`, \`turning\`, \`early-turning\` and \`late-turning\`. This study employs the Yolo-v7 approach for object detection, enabling the recognition and classification of strawberries in different growth phases. The achieved [email protected] values for the growth phases are as follows: 0.37 for \`green,\` 0.335 for \`white,\` 0.505 for \`early-turning,\` 1.0 for \`turning,\` 0.337 for \`late-turning,\` and 0.804 for \`red\`. The comprehensive performance outcomes across all classes are as follows: precision at 0.792, recall at 0.575, [email protected] at 0.558, and [email protected]:.95 at 0.46. Notably, these results show the efficacy of the proposed research, both in terms of performance evaluation and visual assessment, even when dealing with distracting scenarios involving imbalanced label distributions and unclear labeling of developmental phases of the fruits. This research article yields advantages such as achieving reasonable and reliable identification of strawberries, even when operating in real-time scenarios which also leads to a decrease in expenses associated with human labor.
Keywords : Çilek, Yolo v7, Nesne tanıma, Tarım, Derin öğrenme