Comparison of Classic Classifiers, Metaheuristic Algorithms and Convolutional Neural Networks in Hyperspectral Classification of Nitrogen Treatment in Tomato Leaves

Abstract

Tomato is an agricultural product of great economic importance because it is one of the most consumed vegetables in the world. The most crucial chemical element for the growth and development of tomato is nitrogen (N). However, incorrect nitrogen usage can alter the quality of tomato fruit, rendering it undesirable to customers. Therefore, the goal of the current study is to investigate the early detection of excess nitrogen application in the leaves of the Royal tomato variety using a non-destructive hyperspectral imaging system. Hyperspectral information in the leaf images at different wavelengths of 400–1100 nm was studied; they were taken from different treatments with normal nitrogen application (A), and at the first (B), second (C) and third (D) day after the application of excess nitrogen. We investigated the performance of nine machine learning classifiers, including two classic supervised classifiers, i.e., linear discriminant analysis (LDA) and support vector machines (SVMs), three hybrid artificial neural network classifiers, namely, hybrid artificial neural networks and independent component analysis (ANN-ICA), harmony search (ANN-HS) and bees algorithm (ANN-BA) and four classifiers based on deep learning algorithms by convolutional neural networks (CNNs). The results showed that the best classifier was a CNN method, with a correct classification rate (CCR) of 91.6%, compared with an average of 85.5%, 68.5%, 90.8%, 88.8% and 89.2% for LDA, SVM, ANN-ICA, ANN-HS and ANN-BA, respectively. This shows that modern CNN methods should be preferred for spectral analysis over other classical techniques. These CNN architectures can be used in remote sensing for the precise detection of the excessive use of nitrogen fertilizers in large extensions.