首页|A rapid field crop data collection method for complexity cropping patterns using UAV and YOLOv3

A rapid field crop data collection method for complexity cropping patterns using UAV and YOLOv3

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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 Fl-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.

deep learningflight altitudecrop surveypeanutsoybeanmaize

Huifang ZHANG、Binyao WANG、Zhonggang TANG、Jiahui XUE、Ruihang CHEN、Hongcheng KAN、Shumiao LU、Lina FENG、Ye HE、Shuhua YI

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Institute of Fragile Eco-environment,Nantong University,Nantong 226019,China

School of Geographic Science,Nantong University,Nantong 226019,China

Nantong Key Laboratory of Spatial Information Technology R & D and Application,School of Geographic Science,Nantong University,Nantong 226019,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of China

4180102342071056

2024

地球科学前沿
高等教育出版社

地球科学前沿

CSTPCD
影响因子:0.585
ISSN:2095-0195
年,卷(期):2024.18(1)