Sajad Sabzi

Papers

Total Citations

H-Index

About

Dr. Sajad Sabzi is a leading researcher in agricultural automation and precision farming, with a focus on computer vision and machine learning for fruit detection and quality assessment. His work bridges image processing, spectroscopy, and artificial intelligence to solve real-world challenges in horticulture. Dr. Sabzi’s most cited paper, “Estimation of different ripening stages of Fuji apples using image processing and spectroscopy based on the majority voting method” (2020, 28 citations), introduces a novel hybrid approach combining spectral and visual data to accurately classify fruit maturity—a critical advance for postharvest sorting and reducing food waste. His second highly cited study, “Designing a Fruit Identification Algorithm in Orchard Conditions to Develop Robots Using Video Processing and Majority Voting Based on Hybrid Artificial Neural Network” (2020, 25 citations), tackles the difficult problem of detecting fruits in natural, uncontrolled orchard environments. By integrating video processing with a majority voting neural network, this work lays the foundation for autonomous robots capable of selective harvesting and targeted spraying, directly addressing the uneven lighting and occlusions typical in real fields. With a total of over 50 citations from these two key papers alone, Dr. Sabzi’s contributions are shaping the next generation of intelligent agricultural systems, making him a notable figure in applied AI for sustainable farming.