IEEE JSTSP Special Series on Artificial Intelligence for Smart Agriculture
Manuscript Due: 15 June 2025
Publication Date: January 2026
To address rapidly growing interest in signal acquisition and processing, data sciences, artificial intelligence (AI) for smart agriculture, the IEEE Signal Processing Society (SPS) is launching a new special series on Artificial Intelligence for Smart Agriculture, to be published within the IEEE Journal on Selected Topic in Signal Processing (JSTSP).
Beginning in 2026, JSTSP will feature a series of articles on Artificial Intelligence for Smart Agriculture. This curated collection of articles aim to showcase cutting-edge research, innovative methodologies, and practical applications that harness AI and signal processing technologies to revolutionize agricultural practices. By fostering idea exchanges among researchers, industry experts, and practitioners, this series aspires to be a pivotal platform for advancing the integration of AI in agriculture, ultimately contributing to enhanced sustainability, productivity, and efficiency in the sector.
Accordingly, we invite submissions of high-quality manuscripts in relevant emerging sub-topics. These submissions should be original works that have not been previously published and are not currently under review by any other publication venues. The current focus of this series includes cutting-edge AI and data science topics relevant to the broad area of smart agriculture, include but not limited to:
● in-situ and remote sensing for agriculture: This area covers new sensing mechanisms, signal processing techniques, and AI models for real-time situation awareness on crop health, soil conditions, irrigation and weather patterns.
● multispectral and hyperspectral image processing: This area focuses on the acquisition, processing, compression, modeling building and inferences on multispectral and hyperspectral images which reflect crop and soil conditions beyond human observable features.
● foundational AI models and domain adaptation for agriculture: This area covers language, multimodal, metaverse, digital human, and domain specific foundational models, such as for knowledge extraction, conversation, pest and illness diagnostics, growth prediction, and visualization.
● signal processing in devices, edge and cloud: This area covers computational platforms and optimization technologies for model training, model optimization, and collaborative inference across devices, edge, and cloud.
● agricultural data fusion and reasoning: This area covers data mining, data fusion and application-specific reasoning for climate change, agricultural planning, supply chain optimization, food safety, and sustainability
● agricultural robotics: This area covers signal processing and AI technologies for agricultural robots, such as embodied AI, sensors, situation awareness, path and work planning, precision operation, and resource efficiency.
● autonomous farm vehicles and their collaborations: This area focuses on technologies related to self-driving farm vehicles, high-speed driving on farm land, obstacle identification and avoidance, heterogenous machine collaboration, and swarm intelligence for collaborative farm vehicles.
The special series editorial team reserves the right to recommend submissions that are deemed out of scope or modest fit to be resubmitted to other regular SPS journals for consideration.
While manuscripts can be submitted at any time indicating for this special series, interested authors are strongly encouraged to make their submissions according to the following timetable to be considered for the first 2026 issue. Submission site: https://mc.manuscriptcentral.com/jstsp-ieee
Manuscript Submission: June 15, 2025 First Review Due: August 1, 2025
Revised Manuscript Due: September 1, 2025 Second Review Due: Oct. 1, 2025
Final Decision Due: Oct. 15, 2025 Publication Date: Jan. 2026
Editorial Team
- Xiao-Ping (Steven) Zhang (EIC), Shenzhen International Graduate School, Tsinghua University
- Jie Liu (Lead), State Key Lab on Smart Farm Technologies and Systems of China
- Thirimachos Bourlai, University of Georgia
- Yongyong Chen, State Key Lab on Smart Farm Technologies and Systems of China
- Richard Green, University of Canterbury
- Ranveer Chandra, Microsoft Research Lab
- Shunlin Liang, University of Hong Kong
- Wen Hu, The University of New South Wales
- Petros Spachos, University of Guelph
- Yijia Wang, Northeast Agriculture University
- Kai Zhang, Tsinghua University
- Feng Zhao, Northeast Forestry University
- Rongqiang Zhao, State Key Lab on Smart Farm Technologies and Systems of China
