Deep Learning-Based Model for Classification of Bean Nitrogen Status Using Digital Canopy Imaging

Abstract

Laboratory chemical analysis of leaf samples can be costly and time-consuming, making it impractical for assessing crop variability. To address this challenge, researchers have focused on developing non-invasive tools that aid nitrogen (N) management, maximizing profits, minimizing environmental impact, and meeting market demands. This study aimed to develop a computer vision-based classifier system for assessing the N status in bean crops. An experiment was conducted in a greenhouse, involving five treatments (0%, 50%, 100%, 150%, and 200% N of the recommended dose) with six replications, totaling 30 pots containing six seedlings of Phaseolus vulgaris L. beans in four different phenological phases (V4, R5, R6, and R7). Digital RGB images of the bean canopies were captured using a camera at four-week intervals (30, 37, 44, and 51 days after emergence -DAE). The images were manually labeled to create an image database based on N status. Four different computational N status classifiers were developed by training a Convolutional Neural Network (CNN), one for each DAE. The classifiers were evaluated using confusion matrix metrics (accuracy, precision, and recall), resulting in an overall accuracy of about 80% when evaluating nitrogen status at five levels. Improved results were achieved by grouping the saturation classes of the 150% and 200% treatments with the 100% class (>=100% class), yielding an accuracy of 97% for 30 and 44 DAE. Promising results aside, this method opens new possibilities for improvement and application to other treatments, electromagnetic spectrum bands, and crops.

Enhanced U-Net Algorithm for Typical Crop Classification Using Gf-6 WFV Remote Sensing Images

“…This research presents an enhanced U-Net algorithm to improve the accuracy of remote sensing image classification. This algorithm synergistically combines spatial attention and multi-scale features (Baesso et al, 2023). The proposed methodology chiefly employs dilated convolutions across diverse scales to enlarge the receptive field.…”