UAV Remote Sensing, Precision Agronomy, and Resource Optimization Strategies
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 96
Special Issue Editors
Interests: intelligent agricultural systems; crop cultivation optimization; UAV-based crop monitoring; nitrogen use efficiency
Interests: multi-scale research on high-yield, efficient; sustainable rice cultivation: physiology; ecology; yield potential-gap analysis; green low-carbon strategies
Special Issue Information
Dear Colleagues,
Intelligent agricultural technologies are driving the transformation of global crop production toward precision and sustainability through multi-source data fusion and smart decision systems. This Special Issue focuses on innovative applications of remote sensing inversion, machine learning, and artificial intelligence in crop production systems. It highlights the use of UAV hyperspectral imaging, satellite remote sensing, and IoT sensor networks for stress diagnosis (e.g., pest/disease infestation, drought response, waterlogging stress) and non-destructive growth monitoring in staple crops such as soybean, rice, and maize. By integrating machine learning algorithms, this issue aims to develop data-driven precision management models to optimize sowing density, variable-rate fertilization, and irrigation scheduling, thereby enhancing water and nutrient use efficiency. Additionally, it covers the construction of multi-scale modeling and disaster early-warning systems, leveraging hybrid physics-informed and data-driven models to analyze regional yield variability and assess risk thresholds of extreme climate events (e.g., heatwaves, frost) on agroecosystems, providing scientific support for agricultural insurance and adaptive policymaking. Current research needs to overcome the "same object-different spectrum" limitations in traditional remote sensing inversion by developing dynamic feature extraction algorithms adaptable to diverse growth stages and cropping patterns, while building cross-crop and cross-climate machine learning models to improve the generalization capability and early-warning accuracy of stress diagnosis.
We invite original research on remote sensing-driven quantitative retrieval of crop parameters, AI-enabled precision water/fertilizer management, multimodal data fusion for early disease/pest detection, and climate-adaptive intelligent strategies, aiming to provide integrated technical solutions for farmers, researchers, and policymakers to bridge the gap between theoretical innovation and large-scale implementation of intelligent agriculture.
Topics of interest include, but are not limited to, the following:
- UAV/satellite-based retrieval of crop biophysical parameters;
- Early diagnosis of crop stresses;
- AI-driven precision fertilization and irrigation decision systems;
- Agroecosystem dynamic monitoring;
- Agricultural adaptation strategies;
- Crop growth modeling and yield prediction;
- Crop phenotyping analysis;
- Interpretable AI with agronomic knowledge integration.
Prof. Dr. Le Xu
Prof. Dr. Shen Yuan
Guest Editors
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Keywords
- intelligent agriculture
- remote sensing inversion
- machine learning
- crop stress diagnosis
- precision farm management
- hyperspectral imaging
- AI-driven decision-making
- yield prediction modeling
- data-driven agriculture
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