In the heart of modern agriculture, where precision meets sustainability, a groundbreaking review published in *Smart Agricultural Technology* is set to revolutionize how we measure and predict soil moisture. Led by Sarowar Morshed Shawon from the University of Science and Technology Chittagong and Multimedia University, this comprehensive study delves into the latest advancements in soil moisture sensing technologies and predictive models, offering a roadmap for the future of precision agriculture.
Soil moisture is a critical factor in agricultural productivity, irrigation scheduling, and climate modeling. Accurate measurement and prediction of soil moisture are essential for ensuring sustainable agriculture and mitigating environmental challenges related to water scarcity and land degradation. The study, which follows PRISMA guidelines, systematically reviews techniques and approaches used in soil moisture sensing technologies and prediction between 2018 and 2025. It identifies the strengths, limitations, and application domains of each method, with a focus on their relevance to environmental sustainability and precision agriculture.
One of the key challenges highlighted in the review is data heterogeneity, sensor calibration, and the generalizability of machine learning (ML) models under diverse soil and climatic conditions. “The heterogeneity of data and the need for accurate sensor calibration are significant hurdles in achieving precise soil moisture measurements,” notes Shawon. “Addressing these challenges is crucial for the development of robust and reliable predictive models.”
The review also underscores the potential of integrating eXplainable AI (XAI), hybrid physical data-driven frameworks, and Federated Learning (FL) for collaborative model training across distributed datasets. “By leveraging these advanced technologies, we can ensure data privacy and enhance the accuracy of soil moisture predictions,” explains Shawon. “This is particularly important for large-scale agricultural operations where data privacy and accuracy are paramount.”
Moreover, the adoption of low-cost IoT-based soil sensors powered by renewable energy and coupled with predictive models is highlighted as a pathway to scalable and environmentally sustainable solutions. “These sensors can provide real-time data, enabling farmers to make informed decisions about irrigation and crop management,” says Shawon. “This not only improves agricultural productivity but also contributes to water conservation and environmental sustainability.”
The commercial impacts of this research are profound. For the agriculture sector, the ability to accurately measure and predict soil moisture can lead to significant cost savings and increased productivity. Farmers can optimize irrigation schedules, reduce water waste, and enhance crop yields, ultimately improving their bottom line. Additionally, the integration of advanced technologies like XAI and FL can provide farmers with valuable insights and recommendations, further enhancing their decision-making capabilities.
Looking ahead, this research is poised to shape future developments in the field of precision agriculture. By consolidating individual insights and outlining research gaps, the review provides a roadmap for advancing soil moisture sensing and prediction technologies. “Our goal is to foster a more sustainable and resilient agricultural ecosystem,” concludes Shawon. “By addressing the challenges and leveraging the opportunities highlighted in this review, we can pave the way for a more prosperous and environmentally friendly future for agriculture.”
Published in *Smart Agricultural Technology* and led by Sarowar Morshed Shawon from the University of Science and Technology Chittagong and Multimedia University, this study offers a comprehensive and unbiased review of the most relevant research in soil moisture sensing and prediction. It provides a roadmap for advancing these technologies toward enhanced environmental monitoring, sustainable agriculture, and resilient ecosystems.