Vol. 6, No. 1 (2026)
Table of Contents
Hybrid Deep Learning Models for Early Yield Prediction in Triticum aestivum L. Using Multi-Temporal Satellite Imagery
Priya Singh, Kavita Yadav, Arjun Patel
Abstract: Background: Timely and accurate prediction of wheat (Triticum aestivum L.) grain yield well before harvest is essential for precision management, market planning, and food security decision-making, yet conventional prediction approaches relying on single-date imagery or purely empiric...
Explainable Artificial Intelligence Models for Nitrogen Recommendation in Precision Crop Production
Rekha Sharma, Manish Verma
Abstract: Context: Nitrogen is among the most difficult and important fertilizers in modern agriculture, but traditional fertilizer selection techniques often rely on generalized regional recommendations that do not consider field variability and often result in farmers sustaining losses due to...
Artificial Intelligence-Based Multi-Hazard Risk Prediction for Climate-Resilient Agricultural Systems
Shalini Mehta, Harish Jain
Abstract: Climate change is exacerbating the incidence and seriousness of interrelated risks related to agriculture such as drought, flooding, heat stress, frost, and pest and disease outbreaks, posing a danger to food security and crop productivity in areas prone to climate hazards. Traditiona...
Federated Machine Learning Frameworks for Secure Agricultural Decision Support Systems: A Conceptual Review
Nidhi Singh, Vinod Kumar, Rani Jain
Abstract: Background: The digitization of agriculture has resulted in the creation of large, varied datasets that include soil features, climate conditions, plant growth parameters and data about farm management. However, traditional ML approaches require these data to be compiled, raising seri...
Large Language Model-Assisted Intelligent Advisory Systems for Sustainable Farm Management: A Conceptual Review
Asha Jain, Sachin Gupta, Nikita Patel, Ankit Gupta, Simran Patel, Arjun Jain
Abstract: Background: Traditional agricultural extension services, which rely predominantly on in-person agents and static informational materials, increasingly struggle to deliver timely, personalized guidance to a growing and diverse global farming population, particularly in remote and resou...
Autonomous Field Robots for Precision Mechanical Weed Management in Row Crops
Manish Mehta
Abstract: Background: Weed management poses a major agronomic challenge in row crop production throughout the world, requiring a considerable amount of labor and chemicals. The shortage of workers in agriculture combined with the growing environmental awareness about herbicides makes it essenti...
Edge Artificial Intelligence for Real-Time Nutrient Deficiency Detection in Field Crops
Dr. Rajesh Mehta, Dr. Priya Jain, Dr. Amit Singh
Abstract: Background: The problem of nutrient deficiency in crops is one of the main causes which prevent the agricultural sector from achieving optimal results. Meanwhile, conventional methods of diagnosing crops nutrients are characterized by labor-intensiveness, long-time consumption and the...
UAV-Based LiDAR, Hyperspectral Imaging, and GIS Integration for Aboveground Biomass Estimation in Zea mays L.: A Critical Review and Synthesis
Dr. Arven T Solmark, Dr. Linora P Westfield, Dr. Kalen R Dovik
Abstract: AGB (aboveground biomass) is a popular indicator of maize (Zea mays L.) growth status, nutritional sufficiency, and yield potential. Traditional methods for obtaining AGB are time-consuming, difficult, and not well-suited for precision agricultural use. UAV remote sensing has quickly ...
Digital Twin Technology for Monitoring Growth and Resource Use Efficiency in Oryza sativa L.: A Critical Review and Synthesis
Dr. Melric Branswell, Dr. Selvan Ashcrest, Dr. Viraj Solden
Abstract: Rice (Oryza sativa L.) contributes significantly towards feeding half of the world’s population. Nevertheless, rice production faces various challenges, such as reduced water availability, ineffective nutrient use, and balancing increased yields against sustainability. Digital T...
Internet of Agricultural Things (IoAT)-Based Smart Irrigation and Fertigation Management for Sustainable Agriculture: A Critical Review and Synthesis
Dr. Tavin Rajora, Dr. Reyansh Vexford, Dr. Navren Solkar
Abstract: Global agriculture is facing a major challenge: irrigation already accounts for the major share of freshwater withdrawal around the world, but it is characterized by inefficient water and fertilizer use, which contributes to depletion of the resources and deterioration of soil and wat...