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<p>To quickly grasp the area of crop cultivation in mountainous counties, and to solve the problems of difficult, time-consuming and labour-intensive extraction of crop cultivation distribution in mountainous counties, the development of remote sensing technology provides a new type of technological means for the extraction of crop area and the monitoring of the growth trend. Based on the GF-1WFV image, three machine learning algorithms, namely the maximum likelihood method (ML), support vector machine (SVM), and neural network method (NET), were used to analyze the distribution of maize planting areas in Shunping County as an example, which proved that the overall accuracy of the crop planting area in mountainous counties can reach up to 98.16% using ML, Which can be applied to the distribution of agricultural crop planting in mountainous counties. Based on the NDVI data and the image difference method to analyse the changes of maize crop growth in mountainous counties, the results are in line with the growth and development of maize. This study combines remote sensing technology with agricultural production, which is of great practical significance for guiding agricultural production in mountainous counties, taking reasonable crop management measures, and promoting the economic development of the&#xa0;agricultural industry.</p>

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Maize Acreage Extraction and Growth Monitoring in Mountainous Counties Based on GF-1WFV Data

  • 期刊论文

  • 发表日期：

    2025年6月24日

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  • Xinming Song,
  • Li Zhao,
  • Jie Yu,
  • Xiaoqing Li,
  • Jiaxi Xie,
  • Yichun Niu,
  • Lizhu Zhang

摘要

To quickly grasp the area of crop cultivation in mountainous counties, and to solve the problems of difficult, time-consuming and labour-intensive extraction of crop cultivation distribution in mountainous counties, the development of remote sensing technology provides a new type of technological means for the extraction of crop area and the monitoring of the growth trend. Based on the GF-1WFV image, three machine learning algorithms, namely the maximum likelihood method (ML), support vector machine (SVM), and neural network method (NET), were used to analyze the distribution of maize planting areas in Shunping County as an example, which proved that the overall accuracy of the crop planting area in mountainous counties can reach up to 98.16% using ML, Which can be applied to the distribution of agricultural crop planting in mountainous counties. Based on the NDVI data and the image difference method to analyse the changes of maize crop growth in mountainous counties, the results are in line with the growth and development of maize. This study combines remote sensing technology with agricultural production, which is of great practical significance for guiding agricultural production in mountainous counties, taking reasonable crop management measures, and promoting the economic development of the agricultural industry.