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| Filter results4 paper(s) found. |
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1. Improving Winter Wheat Nitrogen Status Monitoring Using Proximal Canopy Sensing and Agrometeorological Information with Machine LearningTimely and accurate diagnosis of winter wheat nitrogen (N) status plays an important role in guiding precision N management. This study aims to combine proximal canopy sensing and agrometeorological information to establish a reliable winter wheat plant N concentration (PNC) monitoring model with seven machine learning (ML) algorithms (Random Forest Regression (RFR), Support Vector Regression (SVR), K-Nearest Neighbors Regression (KNNR), Partial Least Squares Regression (PLSR), Gradient Boosting... X. Chen, Y. Miao, K. Yu, Q. Chang, F. Li |
2. UAV-based Phenotyping of Nitrogen Responses in Winter Wheat: Grain Yield and Nitrogen Use EfficiencyIn the face of escalating global demand for wheat, influenced by burgeoning populations and changing consumption patterns, a profound understanding of determinants like precision nutrient management becomes indispensable. In an on-farm experiment conducted at the Dürnast Research Station in southern Bavaria from 2022 to 2023, we investigated the effects of nitrogen (N) treatments on 18 European winter wheat (Triticum aestivum) cultivars. The field trial design encompassed three distinct... J. Zhang, K. Yu |
3. Evaluating Nitrogen Use Efficiency in Wheat Using UAV Multispectral ImagesNitrogen (N) is one of the most important nutrients for crop growth and development. For crops, nitrogen fertilizer is the guarantee of high yield, but in practical applications, nitrogen fertilizer is often excessive. Therefore precise and rapid assessment of nitrogen use efficiency (NUE) plays a pivotal role in optimizing fertilizer utilization and ensuring responsible use of nitrogen in agriculture. While most of research evaluate NUE from management scales, e.g., from the field, district... J. Wang, K. Yu, S. T.meyer |
4. Detecting Nitrogen Deficiency and Leaf Chlorophyll Content (LCC) Using Sentinel-2 Vegetation IndicesLeaf chlorophyll content (LCC) is a significant indicator of photosynthetic performance and development status of plants. Remote sensing of crop chlorophyll often serves as a basic tool of crop nitrogen fertilization recommendation. The study's objective is to see how remote sensing can better monitor the growth difference of crops, such as LCC. In this study, we investigated the performance vegetation indices in (1) detecting the responses of wheat growth to nitrogen deficiency, and (2) estimating... X. Xu, A. Mokhtari, K. Yu |