Central Institute of Agricultural Engineering | India
Ramesh Kumar Sahni has established a solid and impactful scholarly record, demonstrated through 40 peer-reviewed research documents that together have garnered 187 citations from 154 citing publications, reflecting wide visibility and meaningful influence within the academic community. With an h-index of 9, his research output shows a strong balance between productivity and sustained citation impact, indicating that several of his works are consistently referenced and contribute enduring value rather than short-term attention. His publications collectively address relevant scientific and applied challenges, offering methodological depth, technological relevance, and interdisciplinary insights that continue to inform and support subsequent research efforts. The citation distribution highlights not only recognition by peers but also the practical and conceptual relevance of his work across related domains. Maintaining a steady stream of quality publications, Ramesh Kumar Sahni’s research profile reflects intellectual consistency, originality, and a commitment to advancing knowledge through well-disseminated findings. Overall, the combination of document volume, citation count, and h-index underscores a credible and growing research impact, positioning his contributions as reliable reference points within the scholarly ecosystem and demonstrating sustained engagement with high-value research outcomes.
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Development of a Sensor-Based Embedded System for Real-Time Variable-Rate Fertilizer Application in Maize Cultivation
– Results in Engineering
Utilising Drones in Agriculture: A Comprehensive Review of Remote Sensing, Image Processing, and Field Applications
– Modern Agriculture
Comparative Evaluation of Drone Spraying and Conventional Gun Spraying for Nano-Urea Application in Maize Cropping Systems
– Drones
Resource Use Efficiency and Environmental Impacts of Traditional and Improved Cultivation Practices in Soybean, Paddy, and Wheat Systems
– Sustainable Energy Technologies and Assessments
A Novel Dynamic Detection Method for Stem and Fruit Identification to Enable Apricot Posture Estimation and Adjustment
– Information Processing in Agriculture