Assisting the Planning of Harvesting Plans for Large Strawberry Fields through Image-Processing Method Based on Deep Learning

Chenglin Wang, Qiyu Han, Chunjiang Li, Jianian Li (), Dandan Kong, Faan Wang and Xiangjun Zou
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Chenglin 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
Chunjiang Li: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650504, China
Jianian Li: 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
Faan Wang: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650504, China
Xiangjun Zou: Foshan-Zhongke Innovation Research Institute of Intelligent Agriculture and Robotics, Guangzhou 528231, China

Agriculture, 2024, vol. 14, issue 4, 1-22

Abstract: Reasonably formulating the strawberry harvesting sequence can improve the quality of harvested strawberries and reduce strawberry decay. Growth information based on drone image processing can assist the strawberry harvesting, however, it is still a challenge to develop a reliable method for object identification in drone images. This study proposed a deep learning method, including an improved YOLOv8 model and a new image-processing framework, which could accurately and comprehensively identify mature strawberries, immature strawberries, and strawberry flowers in drone images. The improved YOLOv8 model used the shuffle attention block and the VoV–GSCSP block to enhance identification accuracy and detection speed. The environmental stability-based region segmentation was used to extract the strawberry plant area (including fruits, stems, and leaves). Edge extraction and peak detection were used to estimate the number of strawberry plants. Based on the number of strawberry plants and the distribution of mature strawberries, we draw a growth chart of strawberries (reflecting the urgency of picking in different regions). The experiment showed that the improved YOLOv8 model demonstrated an average accuracy of 82.50% in identifying immature strawberries, 87.40% for mature ones, and 82.90% for strawberry flowers in drone images. The model exhibited an average detection speed of 6.2 ms and a model size of 20.1 MB. The proposed new image-processing technique estimated the number of strawberry plants in a total of 100 images. The bias of the error for images captured at a height of 2 m is 1.1200, and the rmse is 1.3565; The bias of the error for the images captured at a height of 3 m is 2.8400, and the rmse is 3.0199. The assessment of picking priorities for various regions of the strawberry field in this study yielded an average accuracy of 80.53%, based on those provided by 10 experts. By capturing images throughout the entire growth cycle, we can calculate the harvest index for different regions. This means farmers can not only obtain overall ripeness information of strawberries in different regions but also adjust agricultural strategies based on the harvest index to improve both the quantity and quality of fruit set on strawberry plants, as well as plan the harvesting sequence for high-quality strawberry yields.

Keywords: strawberry harvesting; YOLOv8 model; drone image analysis; growth information (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
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