About
Aims The Journal of Digital Agriculture and Smart Farming Systems (JDASF) aims to advance the science and practice of integrating information and communication technologies (ICT), the Internet of Things (IoT), and intelligent decision-support systems into agricultural operations. It serves as a premier venue for high-quality, peer-reviewed research that demonstrates how computational methods, sensor networks, and data analytics can enhance productivity, sustainability, and resilience in farming. JDASF encourages submissions that bridge the gap between computer science innovation and real-world agricultural challenges, fostering a multidisciplinary dialogue among technologists, agronomists, and practitioners. Scope The journal covers the full lifecycle of digital agriculture solutions, from conceptual design and prototyping to deployment and field evaluation. Specific topical areas include, but are not limited to: • IoT architectures and wireless sensor networks for crop and livestock monitoring • Machine learning and deep learning for yield prediction, disease detection, and resource optimization • Decision-support systems for irrigation, fertilization, and pest management • Precision agriculture technologies including GPS-guided machinery and variable rate application • Robotics and autonomous systems for planting, harvesting, and weeding • Blockchain and digital traceability for supply chain transparency • Cloud computing, edge computing, and big data platforms for agricultural analytics • Human-computer interaction and user experience design for farming applications • Cyber-physical systems and digital twins of agricultural processes • Socio-technical aspects of technology adoption in smallholder and industrial farming contexts Article Types JDASF welcomes submissions in the following formats: • Original Research Articles (up to 8,000 words) • Review Articles (up to 12,000 words) • Short Communications (up to 3,000 words) • Case Studies and Field Reports (up to 5,000 words) • Technical Notes (describing novel algorithms, prototypes, or datasets) • Perspectives and Opinion Pieces (by invitation only) Audience Researchers and educators in computer science, agricultural engineering, and environmental science; agricultural technology developers and industry professionals; policymakers and extension specialists; graduate students and early-career researchers seeking a platform for cutting-edge digital farming research.