Smart Agriculture for Crop Phenotyping

Dear Colleagues,

Current projections indicate that the world population will increase from 6.9 billion to 9.1 billion by 2050. In the context of climate change and finite agricultural resources, maintaining and enhancing crop yields has become an increasingly formidable challenge. Against this backdrop, crop phenotyping has emerged as a vital field in modern agricultural research, offering innovative solutions to address these pressing issues.

This Special Issue is dedicated to exploring the latest advancements in smart agriculture technologies for crop phenotyping. We aim to integrate interdisciplinary innovations across artificial intelligence (AI), machine learning, hyperspectral imaging, drone-based remote sensing, and robotic automation. By enabling high-throughput, non-invasive phenotyping data collection and intelligent analytics, these cutting-edge technologies empower researchers to decode dynamic crop growth patterns, stress-resilience traits, and genotype-phenotype associations with unparalleled precision. This, in turn, accelerates crop breeding cycles and optimizes field management practices, offering a promising pathway to enhance agricultural productivity and sustainability in an era of global challenges.

We invite original scientific contributions that highlight the latest advancements in smart agriculture technologies for crop phenotyping. Topics of interest include, but are not limited to, the following:

• Development of multi-scale phenotyping systems using drones and ground robots to capture comprehensive and high-resolution data.
• Applications of machine and deep learning in extracting morphological, physiological, and biochemical traits under biotic and abiotic stresses, providing deeper insights into crop performance and resilience.
• Integration of high-throughput phenotyping platforms with AI-powered decision-support tools for precision agriculture, streamlining workflows from data collection to actionable insights.
• Techniques for plant mapping, feature extraction, and stress estimation using UAVs and other remote sensing data, enabling the real-time monitoring and assessment of crop health.
• Case studies showcasing the application of smart phenotyping for resource optimization (water, fertilizers), pest and disease monitoring, and climate adaptation strategies, demonstrating tangible benefits for sustainable agriculture.

Prof. Dr. Wenting Han
Dr. Liyuan Zhang
Guest Editors

Manuscript Submission Information

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