Proceedings :: International Society of Precision Agriculture

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Muller Klassmann, J.V

Ma, Y

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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

Nogueira, B

Bender, E

de Carvalho Arruda, D

da Costa Salem, M

Espindola Muller, L

dos Santos Gonçalves Junior, S.R

Eissmann Souza, G

Muller Klassmann, J.V

Gallo, B.B

Bredemeier, C

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. Selection of UAV-based Vegetation Indices for the Prediction of Leaf Chlorophyll Content in Maize Using a Normalized Partial Least Squares Regression (PLSR) Reduction Approach The accurate monitoring of the nutritional status is essential for optimizing nitrogen (N) fertilization and maximizing maize grain yield. Variations in N availability directly affect agronomic parameters such as leaf chlorophyll content, which can be estimated using optical sensors. This study assessed the effects of urease inhibitors and nitrogen application rates on leaf chlorophyll content and predicted total leaf chlorophyll content in maize using relevant vegetation indices under field conditions.... B. Nogueira, E. Bender, D. De Carvalho Arruda, M. Da Costa Salem, L. Espindola Muller, S.R. Dos Santos Gonçalves Junior, G. Eissmann Souza, J.V. Muller Klassmann, B.B. Gallo, C. Bredemeier 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

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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. Selection of UAV-based Vegetation Indices for the Prediction of Leaf Chlorophyll Content in Maize Using a Normalized Partial Least Squares Regression (PLSR) Reduction Approach

The accurate monitoring of the nutritional status is essential for optimizing nitrogen (N) fertilization and maximizing maize grain yield. Variations in N availability directly affect agronomic parameters such as leaf chlorophyll content, which can be estimated using optical sensors. This study assessed the effects of urease inhibitors and nitrogen application rates on leaf chlorophyll content and predicted total leaf chlorophyll content in maize using relevant vegetation indices under field conditions.... B. Nogueira, E. Bender, D. De Carvalho Arruda, M. Da Costa Salem, L. Espindola Muller, S.R. Dos Santos Gonçalves Junior, G. Eissmann Souza, J.V. Muller Klassmann, B.B. Gallo, C. Bredemeier

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