YOLOv8-Pearpollen: Method for the Lightweight Identification of Pollen Germination Vigor in Pear Trees

Weili Sun, Cairong Chen, Tengfei Liu, Haoyu Jiang, Luxu Tian, Xiuqing Fu, Mingxu Niu, Shihao Huang and Fei Hu ()
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Weili Sun: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Cairong Chen: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Tengfei Liu: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Haoyu Jiang: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Luxu Tian: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Xiuqing Fu: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Mingxu Niu: College of Horticulture, Nanjing Agricultural University, Nanjing 210031, China
Shihao Huang: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Fei Hu: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China

Agriculture, 2024, vol. 14, issue 8, 1-21

Abstract: Pear trees must be artificially pollinated to ensure yield, and the efficiency of pollination and the quality of pollen germination affect the size, shape, taste, and nutritional value of the fruit. Detecting the pollen germination vigor of pear trees is important to improve the efficiency of artificial pollination and consequently the fruiting rate of pear trees. To overcome the limitations of traditional manual detection methods, such as low efficiency and accuracy and high cost, and to meet the requirements of screening high-quality pollen to promote the yield and production of fruit trees, we proposed a detection method for pear pollen germination vigor named YOLOv8-Pearpollen, an improved version of YOLOv8-n. A pear pollen germination dataset was constructed, and the image was enhanced using Blend Alpha to improve the robustness of the data. A combination of knowledge distillation and model pruning was used to reduce the complexity of the model and the difficulty of deployment in hardware facilities while ensuring that the model achieved or approached the detection effect of a large-volume model that can adapt to the actual requirements of agricultural production. Various ablation tests on knowledge distillation and model pruning were conducted to obtain a high-quality lightweighting method suitable for this model. Test results showed that the mAP of YOLOv8-Pearpollen reached 96.7%. The Params, FLOPs, and weights were only 1.5 M, 4.0 G, and 3.1 MB, respectively, and the detection speed was 147.1 FPS. A high degree of lightweighting and superior detection accuracy were simultaneously achieved.

Keywords: pear pollen; sprouting vigor; lightweight; object detection; YOLOv8 (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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