{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T12:33:21Z","timestamp":1776688401849,"version":"3.51.2"},"reference-count":60,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,5,29]],"date-time":"2019-05-29T00:00:00Z","timestamp":1559088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"This work was funded by the National Natural Science Foundation of China (NSFC) \uff1b Shanghai Engineering Research Center of Efficient Irrigation \uff1b Agricultural Commission of Shanghai Municipality, China.","award":["No. 31601214\uff1bNo. 17DZ2252300\uff1b2017 (1-3) and 2017 (3-4)"],"award-info":[{"award-number":["No. 31601214\uff1bNo. 17DZ2252300\uff1b2017 (1-3) and 2017 (3-4)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Monitoring plant nitrogen (N) in a timely way and accurately is critical for precision fertilization. The imaging technology based on visible light is relatively inexpensive and ubiquitous, and open-source analysis tools have proliferated. In this study, texture- and geometry-related phenotyping combined with color properties were investigated for their potential use in evaluating N in pakchoi (Brassica campestris ssp. chinensis L.). Potted pakchoi treated with four levels of N were cultivated in a greenhouse. Their top-view images were acquired using a camera at six growth stages. The corresponding plant N concentration was determined destructively. The quantitative relationships between the nitrogen nutrition index (NNI) and the image-based phenotyping features were established using the following algorithms: random forest (RF), support vector regression (SVR), and neural network (NN). The results showed the full model based on the color, texture, and geometry-related features outperforms the model based on only the color-related feature in predicting the NNI. The RF full model exhibited the most robust performance in both the seedling and harvest stages, reaching prediction accuracies of 0.823 and 0.943, respectively. The high prediction accuracy of the model allows for a low-cost, non-destructive monitoring of N in the field of precision crop management.<\/jats:p>","DOI":"10.3390\/s19112448","type":"journal-article","created":{"date-parts":[[2019,5,29]],"date-time":"2019-05-29T11:31:28Z","timestamp":1559129488000},"page":"2448","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning for Brassica Campestris ssp. Chinensis L."],"prefix":"10.3390","volume":"19","author":[{"given":"Xin","family":"Xiong","sequence":"first","affiliation":[{"name":"School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Doudou","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5987-1765","authenticated-orcid":false,"given":"Liying","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5209-5270","authenticated-orcid":false,"given":"Danfeng","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"095002","DOI":"10.1088\/1748-9326\/9\/9\/095002","article-title":"Fertilizer nitrogen recovery efficiencies in crop production systems of China with and without consideration of the residual effect of nitrogen","volume":"9","author":"Yan","year":"2014","journal-title":"Environ. 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