Enhanced Estimation of Rice Leaf Nitrogen Content via the Integration of Hybrid Preferred Features and Deep Learning Methodologies

• Published in: Agronomy (Basel) Vol. 14; no. 6; p. 1248
• Main Authors: Peng, Yiping, Zhong, Wenliang, Peng, Zhiping, Tu, Yuting, Xu, Yanggui, Li, Zhuxian, Liang, Jianyi, Huang, Jichuan, Liu, Xu, Fu, Youqiang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Accuracy; Agricultural practices; Agricultural sciences; Algorithms; Artificial neural networks; Correlation coefficient; Correlation coefficients; Crop management; Cultivation; Data mining; Deep learning; Drone aircraft; feature optimization; Fertilization; Fertilizers; Grain cultivation; Hyperspectral imaging; leaf nitrogen content (LNC); Least squares method; Leaves; Machine learning; Methods; Neural networks; Nitrogen; Performance prediction; Regression analysis; Rice; Spatial distribution; Statistical analysis; Statistical methods; Statistical models; Support vector machines; Sustainable agriculture; UAV hyperspectral; Unmanned aerial vehicles; Variables; Vegetation; China
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy14061248

Efficiently obtaining leaf nitrogen content (LNC) in rice to monitor the nutritional health status is crucial in achieving precision fertilization on demand. Unmanned aerial vehicle (UAV)-based hyperspectral technology is an important tool for determining LNC. However, the intricate coupling between spectral information and nitrogen remains elusive. To address this, this study proposed an estimation method for LNC that integrates hybrid preferred features with deep learning modeling algorithms based on UAV hyperspectral imagery. The proposed approach leverages XGBoost, Pearson correlation coefficient (PCC), and a synergistic combination of both to identify the characteristic variables for LNC estimation. We then construct estimation models of LNC using statistical regression methods (partial least-squares regression (PLSR)) and machine learning algorithms (random forest (RF); deep neural networks (DNN)). The optimal model is utilized to map the spatial distribution of LNC at the field scale. The study was conducted at the National Agricultural Science and Technology Park, Guangzhou, located in Baiyun District of Guangdong, China. The results reveal that the combined PCC-XGBoost algorithm significantly enhances the accuracy of rice nitrogen inversion compared to the standalone screening approach. Notably, the model built with the DNN algorithm exhibits the highest predictive performance and demonstrates great potential in mapping the spatial distribution of LNC. This indicates the potential role of the proposed model in precision fertilization and the enhancement of nitrogen utilization efficiency in rice cultivation. The outcomes of this study offer a valuable reference for enhancing agricultural practices and sustainable crop management.