One-Dimensional Convolutional Neural Networks for Hyperspectral Analysis of Nitrogen in Plant Leaves

Featured Application The proposed methodology is able to estimate the amount of nitrogen in plant leaves, using spectral information in the visible (Vis) and near infrared (NIR) ranges, obtaining a mean relative error below 1%. Thus, it will enable the development of portable devices to detect overuse of nitrogen fertilizers in the crops in a fast and non-destructive way. Although it has been tested in cucumber plants, the proposed method can be applied to other types of horticultural crops, repeating the training of the neural network when the new datasets of spectral data and measured nitrogen is available. Accurately determining the nutritional status of plants can prevent many diseases caused by fertilizer disorders. Leaf analysis is one of the most used methods for this purpose. However, in order to get a more accurate result, disorders must be identified before symptoms appear. Therefore, this study aims to identify leaves with excessive nitrogen using one-dimensional convolutional neural networks (1D-CNN) on a dataset of spectral data using the Keras library. Seeds of cucumber were planted in several pots and, after growing the plants, they were divided into different classes of control (without excess nitrogen), N-30% (excess application of nitrogen fertilizer by 30%), N-60% (60% overdose), and N-90% (90% overdose). Hyperspectral data of the samples in the 400-1100 nm range were captured using a hyperspectral camera. The actual amount of nitrogen for each leaf was measured using the Kjeldahl method. Since there were statistically significant differences between the classes, an individual prediction model was designed for each class based on the 1D-CNN algorithm. The main innovation of the present research resides in the application of separate prediction models for each class, and the design of the proposed 1D-CNN regression model. The results showed that the coefficient of determination and the mean squared error for the classes N-30%, N-60% and N-90% were 0.962, 0.0005; 0.968, 0.0003; and 0.967, 0.0007, respectively. Therefore, the proposed method can be effectively used to detect over-application of nitrogen fertilizers in plants.