Towards Intelligent and Automated Agricultural Production

Dear Colleagues,

The rapid advancement of artificial intelligence, the Internet of Things, big data and automation is reshaping agricultural production systems worldwide. Intelligent and automated agriculture integrates multisource sensing, data-driven decision-making and precision execution to improve crop yield, resource efficiency and environmental sustainability, yet key challenges persist: building robust sensing systems, developing algorithms adaptive to complex field environments, and achieving seamless cyber-physical integration across the full production chain.

The rapid advancement of artificial intelligence, Internet of Things, big data, and automation technologies is reshaping agricultural production systems worldwide. Intelligent and automated agricultural production integrates multisource sensing, data-driven decision-making, and precision execution to enhance crop yield, resource efficiency, and environmental sustainability. However, significant challenges remain in developing robust sensing systems, intelligent algorithms for complex agricultural environments, and seamless cyber-physical integration across the entire production chain.

This Topic aims to bring together cutting-edge research on intelligent and automated agricultural production, covering but not limited to smart sensing and monitoring technologies, cutting-edge agricultural AI algorithms and models, AI-driven crop management and pest/disease detection, agricultural robotics and automated equipment, IoT-based precision agriculture systems with data analytics and decision support, and sustainable automation solutions for horticulture and soil management. We invite original research articles, comprehensive reviews, and case studies that advance the theoretical foundations and practical applications in this interdisciplinary field. Contributions from researchers in computer science, agricultural engineering, plant science, and related disciplines are warmly welcomed.

Specifically, the scope of this Topic includes, but is not limited to, the following:

1. Smart sensing and monitoring technologies—deep learning, hyperspectral imaging and drone-based remote sensing for pest identification, disease diagnosis and field condition monitoring.
2. Agricultural large models and knowledge engineering—multimodal vision–language models, foundation models and self-supervised learning for agricultural visual understanding; agricultural knowledge graphs integrated with large language models to build expert-level diagnostic and consultation systems.
3. Field-adaptive AI—edge AI and lightweight model deployment for real-time monitoring in resource-constrained farmlands; few-shot learning, zero-shot learning and domain adaptation for agricultural scenarios with limited labeled data.
4. AI-driven precision agriculture decision systems—data analytics and intelligent decision support covering yield prediction, irrigation optimization, soil health assessment and automated crop management.
5. Agricultural robotics and IoT infrastructure—autonomous agricultural robots and equipment (fruit/vegetable picking, precision weeding, self-navigating machinery, unmanned field operation platforms); agricultural IoT architecture, wireless sensor networks and low-power in situ farmland monitoring technologies.
6. Smart horticulture and soil management automation—intelligent facility horticulture systems (greenhouse environmental control, soilless culture precision management, automated horticultural production lines); intelligent soil management and conservation tillage equipment (variable-rate fertilization/seeding, in situ soil parameter detection platforms).

We look forward to receiving your valuable contributions to this exciting and impactful Topic.

Dr. Ye Mu
Prof. Dr. Pan Gao
Prof. Dr. Jian Li
Topic Editors