Deep Learning-Based Multisensor Approach for Precision Agricultural Crop Classification Based on Nitrogen Levels

Abstract

Accurate classification of crops at the patch level based on nutrient status, particularly nitrogen (N) levels, is essential for advancing precision agriculture (PA). While recent advancements in remote sensing, scalable computing, and visualization technologies have enabled high-resolution plant monitoring, the spectral similarity among crops remains a challenge for precise classification using remote sensing data. This study introduces a multisensor fusion approach, integrating terrestrial LiDAR point cloud data and WorldView-III multispectral imagery within a deep learning (DL) framework to classify cabbage, eggplant, and tomato across different N levels. By combining structural and spectral information, this method effectively captures N-induced growth variations, leading to improved crop discrimination. Our results demonstrate that applying a deep convolutional neural network (DCNN) model to the fused dataset enhances classification accuracy by 13%–16% compared to using multispectral data alone. The incorporation of LiDAR data plays a key role in capturing canopy structure, significantly improving classification performance. Additionally, our DL approach outperforms traditional machine-learning methods, including the random forest (RF) classifier, reinforcing the advantages of DL for N-sensitive crop classification. By leveraging multisensor integration and DL, this study presents a robust and scalable approach for enhancing crop classification accuracy, with significant potential for advancing PA and site-specific nutrient management.