{"title":"A rapid field crop data collection method for complexity cropping patterns using UAV and YOLOv3","authors":"","doi":"10.1007/s11707-024-1109-y","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Crop type mapping using remote sensing is critical for global agricultural monitoring and food security. However, the complexity of crop planting patterns and spatial heterogeneity pose significant challenges to field data collection, thereby limiting the accuracy of remotely sensed crop mapping. This study proposed a new approach for rapidly collecting field crop data by integrating unmanned aerial vehicle (UAV) images with the YOLOv3 (You Only Look Once version 3) algorithm. The impacts of UAV flight altitude and the number of training samples on the accuracy of crop identification models were investigated using peanut, soybean, and maize as examples. The results showed that the average F1-score for crop type detection accuracy reached 0.91 when utilizing UAV images captured at an altitude of 20 m. In addition, a positive correlation was observed between identification accuracy and the number of training samples. The model developed in this study can rapidly and automatically identify crop types from UAV images, which significantly improves the survey efficiency and provides an innovative solution for acquiring field crop data in large areas.</p>","PeriodicalId":48927,"journal":{"name":"Frontiers of Earth Science","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers of Earth Science","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s11707-024-1109-y","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Crop type mapping using remote sensing is critical for global agricultural monitoring and food security. However, the complexity of crop planting patterns and spatial heterogeneity pose significant challenges to field data collection, thereby limiting the accuracy of remotely sensed crop mapping. This study proposed a new approach for rapidly collecting field crop data by integrating unmanned aerial vehicle (UAV) images with the YOLOv3 (You Only Look Once version 3) algorithm. The impacts of UAV flight altitude and the number of training samples on the accuracy of crop identification models were investigated using peanut, soybean, and maize as examples. The results showed that the average F1-score for crop type detection accuracy reached 0.91 when utilizing UAV images captured at an altitude of 20 m. In addition, a positive correlation was observed between identification accuracy and the number of training samples. The model developed in this study can rapidly and automatically identify crop types from UAV images, which significantly improves the survey efficiency and provides an innovative solution for acquiring field crop data in large areas.
摘要 利用遥感技术绘制作物类型图对于全球农业监测和粮食安全至关重要。然而，作物种植模式的复杂性和空间异质性给田间数据收集带来了巨大挑战，从而限制了遥感作物绘图的准确性。本研究提出了一种快速收集田间作物数据的新方法，将无人机（UAV）图像与 YOLOv3（You Only Look Once version 3）算法相结合。以花生、大豆和玉米为例，研究了无人机飞行高度和训练样本数量对作物识别模型准确性的影响。结果表明，利用无人机在 20 米高空拍摄的图像，作物类型检测精度的平均 F1 分数达到 0.91。本研究开发的模型可从无人机图像中快速自动识别作物类型，从而显著提高了调查效率，为获取大面积田间作物数据提供了创新解决方案。
期刊介绍：
Frontiers of Earth Science publishes original, peer-reviewed, theoretical and experimental frontier research papers as well as significant review articles of more general interest to earth scientists. The journal features articles dealing with observations, patterns, processes, and modeling of both innerspheres (including deep crust, mantle, and core) and outerspheres (including atmosphere, hydrosphere, and biosphere) of the earth. Its aim is to promote communication and share knowledge among the international earth science communities
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