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

Abstract

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), N30% (excess application of nitrogen fertilizer by 30%), N60% (60% overdose), and N90% (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 N30%, N60% and N90% 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.

Comparison of 2D and 3D convolutional neural networks in hyperspectral image analysis of fruits applied to orange bruise detection

“…Given a certain pixel ( x 0 , y 0 ), the set of wavelengths for that point represents a 1D spectrum, F ( x 0 , y 0 , w ) for all w (the captured spectral bands). So, we can create a classification system based on 1D convolutions (Pourdarbani et al., 2021). On the other hand, we can extract for each spectral band, w , the planes of ( x,y ) pixels, producing 2D images that can be used for classification using 2D convolutions.…”

Identification of Armyworm-Infected Leaves in Corn by Image Processing and Deep Learning

“…This means all images in the training set and all test images need to be of size 256 × 256. If the input image is not 256 × 256, it needs to be converted to 256 × 256 before using it for training the network (Pourdarbani et al, 2023;Dahham et al, 2023). AlexNet is a complex model with many parameters, making it computationally expensive and time-consuming to train.…”

Identification of micro- and nanoplastics released from medical masks using hyperspectral imaging and deep learning

“…31,69 Various works in using CNNs for hyperspectral data analysis addressed the problem of extracting and preprocessing reflectance from hyperspectral images. 68,70,71 Typically, the SNV correction stays as an optimal method for preserving the general trend of spectra with unwanted noise reduction, which enhances the classification of neural networks. At the same time, modifications and combinations of traditional preprocessing steps, depending on the material under investigation and spectroscopy conditions, may be used to improve the performance of different models.…”