Publication Date
10-13-2025
Document Type
Article
Publication Title
Remote Sensing
Volume
17
Issue
20
DOI
10.3390/rs17203427
Abstract
Highlights: What are the main findings? A framework combining UAV, satellite, and weather data to detect crop diseases. Ensemble deep learning and reinforcement learning improve classification and severity mapping. What is the implication of the main finding? Enables near real-time, scalable crop disease management to reduce yield losses. Early detection and hotspot mapping optimize water and pesticide use for sustainability. Accurate and timely prediction of diseases in water-intensive crops is critical for sustainable agriculture and food security. AI-based crop disease management tools are essential for an optimized approach, as they offer significant potential for enhancing yield and sustainability. This study centers on maize, training deep learning models on UAV imagery and satellite remote-sensing data to detect and predict disease. The performance of multiple convolutional neural networks, such as ResNet-50, DenseNet-121, etc., is evaluated by their ability to classify maize diseases such as Northern Leaf Blight, Gray Leaf Spot, Common Rust, and Blight using UAV drone data. Remotely sensed MODIS satellite data was used to generate spatial severity maps over a uniform grid by implementing time-series modeling. Furthermore, reinforcement learning techniques were used to identify hotspots and prioritize the next locations for inspection by analyzing spatial and temporal patterns, identifying critical factors that affect disease progression, and enabling better decision-making. The integrated pipeline automates data ingestion and delivers farm-level condition views without manual uploads. The combination of multiple remotely sensed data sources leads to an efficient and scalable solution for early disease detection.
Keywords
crop disease detection, crop disease prediction, crop health analysis, machine learning, Northern Leaf Blight, progression analysis, remote sensing, severity mapping, UAV
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Applied Data Science; Computer Engineering
Recommended Citation
Jerry Gao, Krinal Gujarati, Meghana Hegde, Padmini Arra, Sejal Gupta, and Neeraja Buch. "Integration of UAV and Remote Sensing Data for Early Diagnosis and Severity Mapping of Diseases in Maize Crop Through Deep Learning and Reinforcement Learning" Remote Sensing (2025). https://doi.org/10.3390/rs17203427
