Prediction of Winter Wheat Growth Trends Based on NDVI Vegetation Index

As modern technology emerged, the level of agricultural remote sensing has been further improved.This paper takes winter wheat as the research object, studying on Liangshan County in Shandong Province, where the planting coverage of winter wheat is high.Image preprocessing is carried out using ArcGIS, combined with ENVI to invoke satellite data in the near-infrared and infrared bands to calculate the NDVI index from the regreening stage to the maturity stage of winter wheat in this area.During the maturity stage of winter wheat, NDRE is used instead of NDVI to solve the problem of inaccurate NDVI measurement in high-density vegetation coverage.The simulation results show that the test data matches the actual winter wheat output value.

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Publication Details

Journal
Proceedings of International Conference on Artificial Life and Robotics
Published
2025-02-13
DOI
https://doi.org/10.5954/icarob.2025.os9-2
Primary Topic
Environmental and Agricultural Sciences
Type
article
Field-Weighted Citation Impact
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Prediction of Winter Wheat Growth Trends Based on NDVI Vegetation Index

Jiahao Xie, Lu Kang, Haoran Gong, Chunli Li et al.
Proceedings of International Conference on Artificial Life and Robotics
Environmental and Agricultural Sciences
article

Prediction of Winter Wheat Growth Trends Based on NDVI Vegetation Index

Jiahao Xie, Lu Kang, Haoran Gong, Chunli Li, Fengzhi Dai
article en

Abstract

As modern technology emerged, the level of agricultural remote sensing has been further improved.This paper takes winter wheat as the research object, studying on Liangshan County in Shandong Province, where the planting coverage of winter wheat is high.Image preprocessing is carried out using ArcGIS, combined with ENVI to invoke satellite data in the near-infrared and infrared bands to calculate the NDVI index from the regreening stage to the maturity stage of winter wheat in this area.During the maturity stage of winter wheat, NDRE is used instead of NDVI to solve the problem of inaccurate NDVI measurement in high-density vegetation coverage.The simulation results show that the test data matches the actual winter wheat output value.

Proceedings of International Conference on Artificial Life and RoboticsVol. 30
Tianjin University of Science and Technology (CN)
Zero hunger, Climate action
Openalex Percentile: Top 80%
Environmental and Agricultural Sciences
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