Proceedings :: International Society of Precision Agriculture

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Xiaoyu, S

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Authors

Ma, Y

Zhang, J

Pan, D

Wu, Q

Xiaoyu, S

Xu, X

Xiaoyu, S

Wu, Q

Ma, Y

Zhang, J

Dong, P

Xu, X

Xue, H

Xu, X

Yang, G

Xu, Z

Xiaoyu, S

Chen, L

Topics

Precision Crop Protection, Pest, and Plant Health

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Remote and Proximal Sensing of Soils and Crops

Type

Poster

Year

2026

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Filter results3 paper(s) found.
1. Inversion of Potato Chlorophyll Content Based on Radiation Transfer Model and Machine Learning Algorithm Leaf chlorophyll content (LCC) significantly correlates with crop growth conditions, nitrogen content, yield, etc. It is a crucial indicator for elucidating the senescence process of plants and can reflect their growth and nutrition status. However, the performance of traditional LCC inversion models is limited by the quality and scale of training data. It is difficult to satisfy the needs of precision agriculture. 【Objective】Therefore, this study proposes a hybrid modeling framework based... Y. Ma, J. Zhang, D. Pan, Q. Wu, S. Xiaoyu, X. Xu Download paper
2. Study on the Phenological Zoning Method for Winter Wheat in the Huang-Huai-Hai Region of China The impact of global climate change on agricultural phenology is becoming increasingly significant. As a major producer of winter wheat, China's cultivation areas span multiple climate zones. Against the backdrop of climate change, the spatiotemporal differentiation of crop phenology has raised new scientific demands for agricultural zoning. Phenological zoning has guiding significance for variety selection, irrigation management, and pest prediction. However, existing research often relies... S. Xiaoyu, Q. Wu, Y. Ma, J. Zhang, P. Dong, X. Xu Download paper
3. Estimation of Sugarcane Yield Based on Phenological Feature Extraction from Time-Series Sentinel-1 Images and Machine Learning Due to frequently rainy and cloudy weather in the main sugarcane production areas, optical remote sensing data are often missing, and the conventional yield estimation models based on radar remote sensing data lack the support of crop growth mechanisms. This study aims to explore a new yield estimation method for capturing the key dynamic growth features of sugarcane under all-weather conditions. This study takes the sugarcane yield in the dominant area of sugarcane production, Guangxi Zhuang... H. Xue, X. Xu, G. Yang, Z. Xu, S. Xiaoyu, L. Chen Download paper

Filter results3 paper(s) found.

1. Inversion of Potato Chlorophyll Content Based on Radiation Transfer Model and Machine Learning Algorithm

Leaf chlorophyll content (LCC) significantly correlates with crop growth conditions, nitrogen content, yield, etc. It is a crucial indicator for elucidating the senescence process of plants and can reflect their growth and nutrition status. However, the performance of traditional LCC inversion models is limited by the quality and scale of training data. It is difficult to satisfy the needs of precision agriculture. 【Objective】Therefore, this study proposes a hybrid modeling framework based... Y. Ma, J. Zhang, D. Pan, Q. Wu, S. Xiaoyu, X. Xu

Download paper

2. Study on the Phenological Zoning Method for Winter Wheat in the Huang-Huai-Hai Region of China

The impact of global climate change on agricultural phenology is becoming increasingly significant. As a major producer of winter wheat, China's cultivation areas span multiple climate zones. Against the backdrop of climate change, the spatiotemporal differentiation of crop phenology has raised new scientific demands for agricultural zoning. Phenological zoning has guiding significance for variety selection, irrigation management, and pest prediction. However, existing research often relies... S. Xiaoyu, Q. Wu, Y. Ma, J. Zhang, P. Dong, X. Xu

Download paper

3. Estimation of Sugarcane Yield Based on Phenological Feature Extraction from Time-Series Sentinel-1 Images and Machine Learning

Due to frequently rainy and cloudy weather in the main sugarcane production areas, optical remote sensing data are often missing, and the conventional yield estimation models based on radar remote sensing data lack the support of crop growth mechanisms. This study aims to explore a new yield estimation method for capturing the key dynamic growth features of sugarcane under all-weather conditions. This study takes the sugarcane yield in the dominant area of sugarcane production, Guangxi Zhuang... H. Xue, X. Xu, G. Yang, Z. Xu, S. Xiaoyu, L. Chen

Download paper