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Special Issue on Generative AI in Smart Agriculture: Advances, Applications and ChallengesSubmission Date: 2025-12-31As the demand for sustainable, efficient, and resilient agricultural systems intensifies, the integration of cutting-edge technologies like generative artificial intelligence (GAI) emerges as a transformative force in redefining smart agriculture. GAI, renowned for its ability to create synthetic data, offers novel pathways to address agricultural challenges. The use of GAI in agriculture will greatly reduce the cost of various kinds of data acquisition in agricultural scenarios and accelerate the popularization and development of smart agriculture, promoting the level of intelligence and sustainability in agriculture. However, the present GAI in smart agriculture still faces many difficulties and challenges, such as the quality, availability, and security of generated data. This special issue seeks to explore the frontier of GAI in smart agriculture, highlighting groundbreaking advances, real-world applications, and critical challenges. Guest editors: Dr. Yang Li Shihezi University, Shihezi, China liyang328@shzu.edu.cn Dr. Halit Apaydin Ankara University, Ankara, Turkey apaydin@ankara.edu.tr Dr. Shuangqi Li Hong Kong Polytechnic University, Hong Kong, China shuangqi.li@polyu.edu.hk Special issue information: With the development of artificial intelligence (AI) in agriculture, the traditional agricultural production mode has been changed towards digital, precision, unmanned and intelligent way, improving production efficiency and quality. In this process, various applications in specific agricultural scenarios driven by data-hungry deep learning have gained widespread attention and achievements. What's more exciting is that the era of AI large models has quietly arrived, which will surely bring a new round of changes to the current agricultural practices, especially in terms of generative AI. Generative AI (GAI) technology has been gradually maturing in various fields, which can generate useful images, videos, codes, texts, natural language, etc. The use of GAI in agriculture will greatly reduce the cost of data acquisition in agricultural scenarios and accelerate the popularization and development of smart agriculture, promoting the level of intelligence and sustainability in agriculture. However, the introduction of GAI in smart agriculture will also face many difficulties and challenges, such as the quality, availability, and security of generated data. Further, agricultural large AI models need to consider the filtering and integration of agricultural knowledge, and the optimization and stability of data generation technology are also key issues.This special issue focuses on GAI in smart agriculture, highlighting the advanced technologies, applications, and challenges in the process of GAI-aided efficient agricultural practices. High-quality original research papers and comprehensive review articles are encouraged. The topics of interest include but are not limited to: Multi-modal generated data fusion for deep learning-based agricultural applications Crop disease detection and pest management based on deep learning and generated data Real-time decision-making systems combined with Generative AI for smart agriculture Intelligent irrigation systems using Generative AI and agricultural domain knowledge Combination of physical data and generated data in the agricultural digital twin systems Security and privacy issues in the use of Generative AI in smart agricultural applications Quality evaluation and cleaning of generated data in specific agricultural applications Agricultural scene image and video generation, identification, attack and defense Distributed collaborative learning based on Generative AI and edge/cloud computing Case studies using Generative AI towards efficient smart agricultural applications Manuscript submission information: Submission Deadline: 31 December 2025 To submit your manuscript please go to https://www.editorialmanager.com/compag/default1.aspx and follow the procedures for manuscript submission by selecting our Special Issue Article type as "VSI: Generative AI”. Author Guidelines and Manuscript Submission can be found at: https://www.sciencedirect.com/journal/computers-and-electronics-in-agriculture/publish/guide-for-authors Keywords: Computer vision in agriculture; Decision support in agriculture; Digital agriculture; Crop phenotyping; Fertilizer and irrigation; Pests and diseases; Data augmentation; Animal and plant health monitoring; AI-generated content; Agricultural knowledge Last updated by Dou Sun in 2025-08-03 Special Issue on Wearable Sensors for Animal and Plant Information AcquisitionSubmission Date: 2026-08-01The world is undergoing a revolution in agriculture. AI, Internet of Things (IoT), cloud computing and other innovative technologies are accelerating the development of modern smart agriculture. Information acquisition and processing, which rely on sensors and chips, are key to this revolution towards smart agriculture. This special issue aims to foster innovation and drive progress in modern agriculture by bringing together scholars and researchers with new perspectives on smart wearable sensors and chips in agriculture. Guest editors: Dr. Jianfeng Ping Zhejiang University, Hangzhou, China jfping@zju.edu.cn Dr. Yibin Ying Zhejiang University, Hangzhou, China ybying@zju.edu.cn Dr. Jiandong Hu Henan Agricultural University, Zhengzhou, China jdhu@henau.edu.cn Dr. Mazhar Sher South Dakota State University, Brookings, SD, USA mazhar.sher@sdstate.edu Special issue information: Global food systems are facing unprecedented challenges due to population growth, climate change, and the reduction of arable land. Traditional agriculture, which relies on manual sampling and periodic monitoring, can no longer meet the urgent demand for real-time and precise data acquisition in modern farming. Against this backdrop, emerging intelligent sensing technologies, represented by wearable sensors, have become a critical support to break through information bottlenecks, and drive digital and smart agricultural transformation. Serving as the “perception interface” connecting plants and animals to the digital world, wearable sensors characterized by their flexibility, biocompatibility, and low power consumption can non-invasively or minimally invasively attach to the surfaces or interiors of plants and animals, enabling continuous monitoring and high-resolution data acquisition of their physiological states and behavioral traits. These technologies effectively overcome the limitations of traditional monitoring methods and provide more comprehensive and dynamic information support for the health status of organisms and their environmental responses. With the widespread integration of artificial intelligence and the Internet of Things, the large-scale and multidimensional data generated by wearable sensors can be rapidly mined, modeled, and intelligently analyzed. Leveraging deep learning algorithms and data-driven predictive modeling, these systems enable critical applications including early detection of plant stress, timely pest and disease forecasting, and real-time monitoring of abnormal livestock behavior, shifting agricultural management from reactive responses to proactive perception and active regulation within a closed-loop intelligent control system, thereby promoting the development of an efficient, precise, and sustainable smart agricultural ecosystem. Manuscript submission information: Submission Deadline: 01 August 2026 To submit your manuscript, please go to https://www.editorialmanager.com/compag/default1.aspx and follow the procedures for manuscript submission by selecting our Special Issue Article type as "VSI: Wearable Sensors”. Author Guidelines and Manuscript Submission can be found at: https://www.sciencedirect.com/journal/computers-and-electronics-in-agriculture/publish/guide-for-authors Keywords: Smart agriculture; Agricultural information; Animal information; Plant information; Intellisense; Wearable sensors; Flexible sensors; Implantable sensors; Sensing chips; Artificial intelligence; Information acquisition; Optical sensors; NanosensorsLast updated by Dou Sun in 2025-08-03 (责任编辑：) |