YOLOv8-Pearpollen: Method for the Lightweight Identification of Pollen Germination Vigor in Pear Trees
-
Published:2024-08-12
Issue:8
Volume:14
Page:1348
-
ISSN:2077-0472
-
Container-title:Agriculture
-
language:en
-
Short-container-title:Agriculture
Author:
Sun Weili1, Chen Cairong2, Liu Tengfei1, Jiang Haoyu1, Tian Luxu2, Fu Xiuqing2ORCID, Niu Mingxu3, Huang Shihao2, Hu Fei1
Affiliation:
1. College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China 2. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China 3. College of Horticulture, Nanjing Agricultural University, Nanjing 210031, China
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.
Funder
Major Science and Technology Projects of Xinjiang Academy of Agricultural and Reclamation Sciences the Jiangsu Agriculture Science and Technology Innovation Fund Hainan Seed Industry Laboratory Jiangsu Province Seed Industry Revitalization Unveiled Project
Reference38 articles.
1. Huete, A., Tran, N.N., Nguyen, H., Xie, Q., and Katelaris, C. (August, January 28). Forecasting pollen aerobiology with Modis EVI, land cover, and phenology using machine learning tools. Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), Yokohama, Japan. 2. TipChip: A modular, MEMS-based platform for experimentation and phenotyping of tip-growing cells;Agudelo;Plant J.,2013 3. Dechkrong, P., Srima, S., Nilwaranon, T., Tongyoo, P., de Jong, H., and Chunwongse, J. (2020). Morphological Characterization of Anther and Pollen Formation in an EMS Induced Tomato Mutant with Blossom Drop Phenotype. Plant Biol. Crop Res., 1. 4. Deep learning-based high-throughput detection of in vitro germination to assess pollen viability from microscopic images;Zhang;J. Exp. Bot.,2023 5. Classifying black and white spruce pollen using layered machine learning;Punyasena;New Phytol.,2012
|
|