{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T21:16:20Z","timestamp":1768684580807,"version":"3.49.0"},"reference-count":55,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yunnan Province Major Science and Technology Special Project","award":["202202AE090013-2"],"award-info":[{"award-number":["202202AE090013-2"]}]},{"name":"Yunnan Province Major Science and Technology Special Project","award":["CARS-03"],"award-info":[{"award-number":["CARS-03"]}]},{"name":"National Modern Agricultural Industry Technology System Grant","award":["202202AE090013-2"],"award-info":[{"award-number":["202202AE090013-2"]}]},{"name":"National Modern Agricultural Industry Technology System Grant","award":["CARS-03"],"award-info":[{"award-number":["CARS-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Nitrogen is the main nutrient element in the growth process of white radish, and accurate monitoring of radish leaf nitrogen content (LNC) is an important guide for precise fertilization decisions for radish in the field. Using white radish LNC monitoring as an object, research on radish nitrogen hyperspectral estimation methods was carried out based on leaf hyperspectral and field sample nitrogen data at multiple growth stages using feature selection and integrated learning algorithm models. First, the Vegetation Index (VI) was constructed from hyperspectral data. We extracted sensitive features of hyperspectral data and VI response to radish LNC based on Pearson\u2019s feature-selection approach. Second, a stacking-integrated learning approach is proposed using machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Ridge and K-Nearest Neighbor (KNN) as the base model in the first layer of the architecture, and the Lasso algorithm as the meta-model in the second layer of the architecture, to realize the hyperspectral estimation of radish LNC. The analysis results show the following: (1) The sensitive bands of the radish LNC are mainly centered around 600\u2013700 nm and 1950 nm, and the constructed sensitive VIs are also concentrated in this band range. (2) The Stacking model with spectral features as inputs achieved good prediction accuracy at the radish spectral leaf, with R2 = 0.7, MAE = 0.16, MSE = 0.05 estimated over the whole growth stage of radish. (3) The Lasso algorithm with variable filtering function was chosen as the meta-model, which has a redundant model-selection effect on the base model and helps to improve the quality of the integrated learning framework. This study demonstrates the potential of the stacking-integrated learning method based on hyperspectral data for spectral estimation of nitrogen content in radish at multiple growth stages.<\/jats:p>","DOI":"10.3390\/rs16234479","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T05:02:22Z","timestamp":1732856542000},"page":"4479","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Hyperspectral Estimation of Leaf Nitrogen Content in White Radish Based on Feature Selection and Integrated Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Yafeng","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"},{"name":"School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8473-5631","authenticated-orcid":false,"given":"Xingang","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"},{"name":"School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China"}]},{"given":"Wenbiao","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"}]},{"given":"Yaohui","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China"}]},{"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"}]},{"given":"Lutao","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Big Data, Yunnan Agricultural University, Kunming 650500, China"}]},{"given":"Yang","family":"Meng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"}]},{"given":"Xiangtai","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7957-5055","authenticated-orcid":false,"given":"Hanyu","family":"Xue","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"126241","DOI":"10.1016\/j.eja.2021.126241","article-title":"An Overview of Crop Nitrogen Status Assessment Using Hyperspectral Remote Sensing: Current Status and Perspectives","volume":"124","author":"Fu","year":"2021","journal-title":"Eur. 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