Optimized Machine Learning Algorithms to Enhance Rice Protein Content Monitoring with Multispectral UAV Imagery

Spectral–Textural Feature Fusion Coupled with Machine Learning Improves UAV-Based Estimation of Maize Leaf SPAD Value in Arid Agroecosystems

Rapid and accurate monitoring of leaf nitrogen status is a key step in achieving precise fertilizer management for maize. However, UAV-based multispectral approaches relying solely on vegetation indices (VIs) may suffer from spectral saturation under dense canopies and provide limited information on canopy structural v...

Estimating Nitrogen, Phosphorus, and Potassium Content in Pear Trees Using UAV-Based Multispectral Imagery

Machine learning combined with multispectral remote sensing provides an efficient and non-destructive approach for monitoring fruit tree nutrient status. However, conventional machine learning methods have limited ability to capture complex spectral-feature interactions, while deep neural networks often suffer from ove...

Deep Learning-Based Monitoring of Tea Plant Growth and Nitrogen Status Using UAV Multisource Remote Sensing Features

A UAV-based framework for tea growth parameter estimation is provided by integrating multi-domain information without requiring additional environmental observations and model-derived spatial maps provided insights into fine-scale spatial heterogeneity and potential interannual variations in tea growth parameters acros...

Automated Machine Learning-Driven UAV Remote Sensing for Accurate Winter Wheat Water Content Prediction

Crop water content is a critical indicator of crop growth status, and its efficient and accurate monitoring is essential for agricultural water resource management. Conventional methods for monitoring winter wheat water content, however, rely mainly on destructive sampling and are labor-intensive and time-consuming. To...

Estimation of leaf nitrogen content of typical fruit trees in arid regions based on fusion of UAV multispectral information and texture features

Inversion of Apple Leaf Moisture Content and SPAD Values Using Multispectral Vegetation Indices and Band Reflectance from UAVs

Accurate monitoring of leaf chlorophyll-related status (SPAD) and water status is important for evaluating apple growth and supporting orchard management. In this study, SPAD and leaf water content (LWC) were considered complementary indicators of chlorophyll-related and water-related physiological characteristics, res...