Advances in Imaging Technologies for Precision Agriculture

Dear Colleagues,

The escalating global food demand, constant requirement of improving operational efficiencies, and the pressures of agricultural workforce displacement and urbanization have intensified the need for precise and efficient crop management. Traditional approaches to agriculture often lack the spatial and temporal resolution required to monitor crops effectively under variable environmental conditions. This limitation has accelerated the development and adoption of advanced imaging and sensing technologies, ranging from unmanned aerial vehicle (UAV)-mounted high-resolution cameras to multispectral, hyperspectral, LiDAR, and thermal sensors. More recently, these imaging modalities have been coupled with emerging computational approaches, such as generative AI, foundation models, and advanced machine and deep learning frameworks, unlocking new possibilities in plant health monitoring, stress detection, yield estimation, and autonomous decision-making.

Despite these advances, critical information gaps remain. Many imaging solutions struggle with scalability across diverse environments, standardization of data formats, and interoperability between platforms. Integrating multimodal imaging (e.g., combining hyperspectral with LiDAR or thermal data) is still in its early stages. In contrast, robust ground-truth datasets for training and validating AI-driven imaging systems are often limited. Additionally, although generative AI and foundation models hold significant promise, their application in agriculture is still in its early stages, with challenges related to explainability, transferability across crops and regions, and the ethical use of data.

This Special Issue on Advances in Imaging Technologies for Precision Agriculture aims to address these gaps by providing a platform for research that pushes the boundaries of imaging-driven agricultural innovation. Potential contributions include, but are not limited to:

• Development of novel imaging hardware and sensor platforms tailored for crop monitoring.
• Integration of multisource imaging (RGB, multispectral, hyperspectral, thermal, LiDAR) for comprehensive crop analysis.
• Imaging solutions for early disease, pest, and nutrient stress detection under field conditions.
• UAV- and satellite-based imaging frameworks for large-scale, high-throughput monitoring.
• AI-powered image processing, including generative AI, foundation models, and explainable deep learning for agricultural decision support.
• Imaging-based phenotyping pipelines for breeding programs and genotype–environment interaction studies.
• Advances in image-based robotics and automation for precision application of water, nutrients, and crop protection.
• Standardization, calibration, and benchmarking approaches for agricultural imaging systems.

By bringing together contributions across these domains, this Special Issue seeks to advance the scientific discourse on imaging for agriculture, highlight innovations that bridge the gap between laboratory development and field deployment, and accelerate the adoption of imaging technologies that can enable resilient, productive, and sustainable food systems.

Dr. Nitin Rai
Dr. Igathinathane Cannayen
Dr. Sunoj Shajahan
Guest Editors

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• precision agriculture
• generative AI
• foundation models
• multispectral imaging
• hyperspectral imaging
• thermal imaging
• UAV remote sensing
• IoT-driven sensing
• AI-based decision support tools