Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions

Sabzi, S ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1016/j.chemolab.2021.104404
  3. Publisher: Elsevier B.V , 2021
  4. Abstract:
  5. 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 (mg l−1) 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. © 2021 The Author(s)
  6. Keywords:
  7. Nitrogen ; Artificial neural network ; Controlled study ; Convolutional neural network ; Cucumber ; Deep learning ; Hyperspectral imaging ; Image processing ; Machine learning ; Nonhuman ; Plant leaf ; Process optimization
  8. Source: Chemometrics and Intelligent Laboratory Systems ; Volume 217 , 2021 ; 01697439 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0169743921001726