文献类型: 会议论文
第一作者: Juntao Xiong
作者: Juntao Xiong 1 ; Dan Hong 1 ; Zhenhui Zheng 2 ; Jinyu Feng 1 ; Weixian Chen 3 ; Kaihan Huang 3 ; Zexuan Wu 1 ; Zhijie Li 3 ; Jing Wang 4 ; Wei Rong 3 ;
作者机构: 1.College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China
2.Chinese Academy of Tropical Agricultural Sciences, Institute of Agricultural Machinery, Zhanjiang, China
3.College of Engineering, South China Agricultural University, Guangzhou, China
4.Guangdong Academy of Agricultural Sciences, Institute of Fruit Tree Research, Guangzhou, China
关键词: Training;Production management;Accuracy;Machine vision;Imaging;Flowering plants;Data models
会议名称: International Conference on Machine Vision, Image Processing and Imaging Technology
主办单位:
页码: 51-56
摘要: Automatic picking of litchi is an important means to effectively improve the yield and quality of litchi. At present, branch and leaf occlusion is the critical factor that makes the picking robot unable to locate and pick fruit accurately. In this paper, a method based on YOLOv7 to calculate the optimal leaf-picking amount is proposed to optimize the problem. First of all, three groups of litchi trees were treated with different leaf-picking treatments at flowering stage. Then, the images of litchi s’ fruits and leaves are collected regularly to build a data set to train the YOLOv7 model. Finally, it comes to a conclusion that the optimal leaf-picking amount at litchi flowering stage was determined, by comparing the fruit recognition accuracy and fruit data under different leaf-picking amounts. According to the litchi test conclusions, the model’s AP values under the three situations of no picking, three pairs of leaves, and six pairs of leaves were 30.16%, 70.62%, and 82.75%, respectively. In addition, the yield of single litchi tree were 30kg, 37kg, 35kg. Therefore, considering the accuracy of the algorithm and the fruit data, the optimal leaf picking quantity is determined as 3 pairs of leaves. The results show that the method is feasible and can provide information support for litchi production management.
分类号: tp391-53
- 相关文献
[1]Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens. Liu, Tianfei,Qu, Hao,Luo, Chenglong,Shu, Dingming,Wang, Jie,Liu, Tianfei,Lund, Mogens Sando,Su, Guosheng,Liu, Tianfei,Qu, Hao,Luo, Chenglong,Shu, Dingming,Wang, Jie. 2014
[2]Plant Identification Based on Artificial Intelligence. Hou Chun Sheng,Zhou Ting. 2011