RT info:eu-repo/semantics/article
T1 Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions
A1 Sabzi, Sajad
A1 Pourdarbani, Razieh
A1 Rohban, Mohammad H.
A1 García Mateos, Ginés
A1 Arribas Sánchez, Juan Ignacio
K1 Hyperspectral imaging
K1 Imágenes hiperespectrales
K1 Image processing
K1 Procesamiento de imágenes
K1 Nitrogen
K1 Nitrógeno
K1 Plants
K1 Plantas
AB 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.
PB Elsevier
SN 0169-7439
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/48443
UL https://uvadoc.uva.es/handle/10324/48443
LA eng
NO Chemometrics and Intelligent Laboratory Systems, 2021, vol. 217, 104404
NO Producción Científica
DS UVaDOC
RD 19-abr-2024