2026-04-22T22:07:42Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/484432021-09-19T18:32:35Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

Sabzi, Sajad Pourdarbani, Razieh Rohban, Mohammad H. García Mateos, Ginés Arribas Sánchez, Juan Ignacio 2021-09-01T09:04:25Z 2021-09-01T09:04:25Z 2021 Chemometrics and Intelligent Laboratory Systems, 2021, vol. 217, 104404 0169-7439 https://uvadoc.uva.es/handle/10324/48443 10.1016/j.chemolab.2021.104404 Producción Científica In recent years, farmers have often mistakenly resorted to overuse of chemical fertilizers to increase crop yield. However, excessive consumption of fertilizers might lead to severe food poisoning. If nutritional deficiencies are detected early, it can help farmers to design better fertigation practices before the problem becomes unsolvable. The aim of this study is to predict the amount of nitrogen (N) content in cucumber (Cucumis sativus L., var. Super Arshiya-F1) plant leaves using hyperspectral imaging (HSI) techniques and three different regression methods: a hybrid artificial neural networks-particle swarm optimization (ANN-PSO); partial least squares regression (PLSR); and unidimensional deep learning convolutional neural networks (CNN). Cucumber plant seeds were planted in 20 different pots. After growing the plants, pots were categorized and three levels of nitrogen overdose were applied to each category: 30%, 60% and 90% excesses, called N30%, N60%, N90%, respectively. HSI images of plant leaves were captured before and after the application of nitrogen excess. A prediction regression model was developed for each individual category. Results showed that mean regression coefficients (R) for ANN-PSO were inside 0.937–0.965, PLSR 0.975–0.997, and CNN 0.965–0.985 ranges, test set. We conclude that regression models have a remarkable ability to accurately predict the amount of nitrogen content in cucumber plants from hyperspectral leaf images in a non-destructive way, being PLSR slightly ahead of CNN and ANN-PSO methods. Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación - Fondo Europeo de Desarrollo Regional (grants RTI2018-098958-B-I00 and RTI2018-098156-B-C53) application/pdf eng Elsevier info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2021 The Authors Attribution-NonCommercial-NoDerivatives 4.0 Internacional Hyperspectral imaging Imágenes hiperespectrales Image processing Procesamiento de imágenes Nitrogen Nitrógeno Plants Plantas Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions info:eu-repo/semantics/article info:eu-repo/semantics/submittedVersion https://www.sciencedirect.com/science/article/pii/S0169743921001726?via%3Dihub SI