| 基于无人机RGB影像估测田间小麦穗数 |
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| 引用本文： | 高姻燕,孙义,李葆春. 基于无人机RGB影像估测田间小麦穗数[J]. 中国农业科技导报, 2022, 24(3): 103-110. DOI: 10.13304/j.nykjdb.2021.0335 |
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| 作者姓名： | 高姻燕 孙义 李葆春 |
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| 作者单位： | 1.甘肃农业大学生命科学技术学院，兰州 730070;2.南通大学地理科学学院，脆弱生态环境研究所，江苏 南通 2260073;3.甘肃农业大学甘肃省干旱生境作物学重点实验室，甘肃省作物遗传改良与种质创新实验室，兰州 730070 |
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| 基金项目： | 国家自然科学基金项目(31860377) |
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| 摘 要： | 单位面积穗数是小麦产量构成的重要因素,利用图像信息处理技术快速、准确地估测田间小麦穗数,可以为小麦长势监测和产量估测提供直接依据.利用无人机路径规划和控制系统(fragmentation monitoring and analysis with aerial photography,FragMAP)获取标准统一、高分辨...
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| 关 键 词： | 航拍 FragMAP 深度学习 YOLOv3 麦穗检测 |
| 收稿时间： | 2021-04-20 |
Estimating of Wheat Ears Number in Field Based on RGB Images Using Unmanned Aerial Vehicle |
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| Yinyan GAO,Yi SUN,Baochun LI. Estimating of Wheat Ears Number in Field Based on RGB Images Using Unmanned Aerial Vehicle[J]. Journal of Agricultural Science and Technology, 2022, 24(3): 103-110. DOI: 10.13304/j.nykjdb.2021.0335 |
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| Authors: | Yinyan GAO Yi SUN Baochun LI |
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| Affiliation: | 1.College of Life Sciences and Technology，Gansu Agricultural University，Lanzhou 730070，China;2.Institute of Fragile Eco?Environment，School of Geographic Science，Nantong University，Jiangsu Nantong 226007，China;3.Gansu Provincial Key Lab of Aridland Crop Science，Gansu Key Lab of Crop Improvement and Germplasm Enhancement，Gansu Agricultural University，Lanzhou 730070，China |
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| Abstract: | Ears number per unit area is the key element of wheat yield， so estimating ears number in field quickly and accurately based on aerial photographs and information extraction could provide the direct support to wheat growth monitoring and production estimates. This study acquired the unified standard and high resolution RGB aerial photographs using unmanned aerial vehicle （UAV） controlled by fragmentation monitoring and analysis with aerial photography （FragMAP）， then obtained the training model by quick and efficient target detection method （YOLOv3） and realized identifying wheat ears automatically， and finally established the estimation model of wheat ears number based on the relationships of the wheat ears number measured by proposed method （FY） and traditional methods. The results showed that the sampling efficiency and monitor area of FY were significantly higher than that of traditional method； the mean accuracy rate of identification based on YOLOv3 model was above 90%； the wheat ears numbers measured by FY and traditional method were significant linear correlation， and the estimate model was established as y=0.816x-14.863 （R2=0.790， P<0.001）. The results showed that the wheat ears estimation based on the unified standard and higher resolution images collected by UAV and deep learning was accurate， and could effectively monitor wheat growth and predicting the wheat yield. |
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| Keywords: | aerial photograph FragMAP deep learning YOLOv3 wheat ears detection |
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