Prediction of winter wheat nitrogen nutrition index using high-resolution satellite and machine learning
Wheat (Triticum aestivum L.) is the most important cereal crop grown in Spain, and Spain is one of the top wheat-producing countries in EU. Precision fertilization, which customizes the fertilizer dosage based on the variability of the field, is important for the environment, food security, and farm...
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| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525003521 |
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| author | Po-Ting Pan Yamine Bouzembrak Miguel Quemada Bedir Tekinerdogan |
| author_facet | Po-Ting Pan Yamine Bouzembrak Miguel Quemada Bedir Tekinerdogan |
| author_sort | Po-Ting Pan |
| collection | DOAJ |
| description | Wheat (Triticum aestivum L.) is the most important cereal crop grown in Spain, and Spain is one of the top wheat-producing countries in EU. Precision fertilization, which customizes the fertilizer dosage based on the variability of the field, is important for the environment, food security, and farmers’ finances. To provide the fertilizer prescription, assessing crop nitrogen (N) status is required to make site-specific fertilizer applications. Among the nitrogen status index, the nitrogen nutrition index (NNI) is considered the most reliable to monitor the N status. Prior studies have used satellite, UAV, or spectral sensors with mostly adopting linear regression methods to predict NNI. However, no study has investigated the potential of using high-resolution satellite and environmental data with machine learning (ML) to predict winter wheat NNI. Therefore, this study integrated PlanetScope satellite images with weather data while adopting three ML algorithms, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to predict NNI in Spain from 2018 to 2019. The results showed that RF outperformed the SVM and ANN models with an accuracy of 77.08 % and a precision of 0.78. This study also demonstrated weather data improved model performance across all three algorithms with the highest accuracy of 79.12 % in the RF algorithm. Among all three algorithms, the elongation period outperformed the flowering period and across the entire period with an accuracy of 81.25 - 87.5 % and a precision of 0.5 - 0.78. In the end, the N status diagnostic map was generated to reflect the nitrogen requirement and provide a decision support tool for farmers before the fertilizer application. The proposed methodology in this paper can be extended to different crops and different regions for NNI prediction. |
| format | Article |
| id | doaj-art-b991bba4f8d345468eb927eaa93ad7c4 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-b991bba4f8d345468eb927eaa93ad7c42025-08-20T03:32:04ZengElsevierSmart Agricultural Technology2772-37552025-12-011210111910.1016/j.atech.2025.101119Prediction of winter wheat nitrogen nutrition index using high-resolution satellite and machine learningPo-Ting Pan0Yamine Bouzembrak1Miguel Quemada2Bedir Tekinerdogan3Information Technology Group, Wageningen University and Research, Wageningen, The NetherlandsInformation Technology Group, Wageningen University and Research, Wageningen, The Netherlands; Corresponding author.School of Agricultural Engineering, Technical University of Madrid, Madrid 28040, SpainInformation Technology Group, Wageningen University and Research, Wageningen, The NetherlandsWheat (Triticum aestivum L.) is the most important cereal crop grown in Spain, and Spain is one of the top wheat-producing countries in EU. Precision fertilization, which customizes the fertilizer dosage based on the variability of the field, is important for the environment, food security, and farmers’ finances. To provide the fertilizer prescription, assessing crop nitrogen (N) status is required to make site-specific fertilizer applications. Among the nitrogen status index, the nitrogen nutrition index (NNI) is considered the most reliable to monitor the N status. Prior studies have used satellite, UAV, or spectral sensors with mostly adopting linear regression methods to predict NNI. However, no study has investigated the potential of using high-resolution satellite and environmental data with machine learning (ML) to predict winter wheat NNI. Therefore, this study integrated PlanetScope satellite images with weather data while adopting three ML algorithms, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to predict NNI in Spain from 2018 to 2019. The results showed that RF outperformed the SVM and ANN models with an accuracy of 77.08 % and a precision of 0.78. This study also demonstrated weather data improved model performance across all three algorithms with the highest accuracy of 79.12 % in the RF algorithm. Among all three algorithms, the elongation period outperformed the flowering period and across the entire period with an accuracy of 81.25 - 87.5 % and a precision of 0.5 - 0.78. In the end, the N status diagnostic map was generated to reflect the nitrogen requirement and provide a decision support tool for farmers before the fertilizer application. The proposed methodology in this paper can be extended to different crops and different regions for NNI prediction.http://www.sciencedirect.com/science/article/pii/S2772375525003521Remote sensingPrecision fertilizationArtificial intelligenceAgricultural decision support system |
| spellingShingle | Po-Ting Pan Yamine Bouzembrak Miguel Quemada Bedir Tekinerdogan Prediction of winter wheat nitrogen nutrition index using high-resolution satellite and machine learning Smart Agricultural Technology Remote sensing Precision fertilization Artificial intelligence Agricultural decision support system |
| title | Prediction of winter wheat nitrogen nutrition index using high-resolution satellite and machine learning |
| title_full | Prediction of winter wheat nitrogen nutrition index using high-resolution satellite and machine learning |
| title_fullStr | Prediction of winter wheat nitrogen nutrition index using high-resolution satellite and machine learning |
| title_full_unstemmed | Prediction of winter wheat nitrogen nutrition index using high-resolution satellite and machine learning |
| title_short | Prediction of winter wheat nitrogen nutrition index using high-resolution satellite and machine learning |
| title_sort | prediction of winter wheat nitrogen nutrition index using high resolution satellite and machine learning |
| topic | Remote sensing Precision fertilization Artificial intelligence Agricultural decision support system |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525003521 |
| work_keys_str_mv | AT potingpan predictionofwinterwheatnitrogennutritionindexusinghighresolutionsatelliteandmachinelearning AT yaminebouzembrak predictionofwinterwheatnitrogennutritionindexusinghighresolutionsatelliteandmachinelearning AT miguelquemada predictionofwinterwheatnitrogennutritionindexusinghighresolutionsatelliteandmachinelearning AT bedirtekinerdogan predictionofwinterwheatnitrogennutritionindexusinghighresolutionsatelliteandmachinelearning |