ICAR Indian Agricultural Statistics Research Institute | India
Dr. Md. Ashraful Haque is a Scientist in the Division of Computer Applications at ICAR-Indian Agricultural Statistics Research Institute, New Delhi, with over five years of professional experience in artificial intelligence applications in agriculture. He earned his M.Sc. (2016) and Ph.D. (2022) in Computer Applications from ICAR-Indian Agricultural Research Institute under the supervision of Dr. Sudeep Marwaha. His research focuses on artificial intelligence, machine learning, deep learning, computer vision, and digital as well as hyperspectral image processing for solving complex agricultural problems such as crop disease detection, pest identification, stress analysis, and livestock image interpretation. He has published 20 journal articles, 4 book chapters, and 2 computational tools, with his research gaining wide recognition (h-index: 8, 339 citations). Dr. Haque has contributed to international collaborations with IIT Delhi, ICAR institutes, and several universities, and has developed impactful digital tools like AIDISC and WIAYFS. He is the recipient of prestigious fellowships and awards, including the Maulana Azad National Fellowship, ICAR-JRF, IARI Ph.D. Fellowship, and Nehru Memorial Gold Medal. With strong expertise in AI-driven agriculture, he continues to innovate toward sustainable farming solutions. His work demonstrates high relevance, impact, and future research potential in smart agriculture.
Featured Publications
“MuSiC V1.0: A Software Solution for Automated Mustard Silique Count Using YOLOv5”
“An enhanced vision transformer network for efficient and accurate crop disease detection”
“PanicleDet: a deep learning-based model for detection of panicle stages in paddy”
“A comparative analysis of deep learning-based techniques for miRNA prediction associated with mRNA sequences”
“ADNet: An Attention Embedded DenseNet121 Model for Weed Classification”
“Convolution Neural Network (CNN)-Based Live Pig Weight Estimation in Controlled Imaging Platform”
“Rice Disease Identification Using Vision Transformer (ViT) Based Network”