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Dipak Kumar Jana

Work place: Gangarampur College, Gangarampur, Dakshin Dinajpur, West Bengal, 733124, India

E-mail: dipakjana@gmail.com

Website: https://orcid.org/0009-0004-0352-9345

Research Interests:

Biography

Dr. Dipak Kumar Jana did Ph.D. from Indian Institute of Engineering Science and Technology, Shibpur and did his post-graduation (M.Sc) in Applied Mathematics with specialization in Operations Research from Vidyasagar University, West Bengal. He has qualified in the National Eligibility Test (NET-CSIR) for Junior Research Fellow (JRF) and GATE. He has been teaching Mathematics both at undergraduate and postgraduate levels. At present, he is working as Principal at Gangarampur College, Gangarampur, Dakshin Dinajpur, W.B., 733124, India, and Dr. Jana was Ex-HoD \& Professor in School of Applied Sciences, Haldia Institute of Technology, Haldia, West. Dr. Jana is a member of the Operational Research Society of India, the Indian Science Congress Association, and the Calcutta Mathematical Society. He has published more than 140 research articles in reputed international journals, including Expert Systems with Applications, Journal of Cleaner Production, Information Sciences, Applied Soft Computing, and several other leading SCI/Scopus-indexed journals. He is the author of 15 books and holds 15 patents. His notable books include A Textbook of Engineering Operations Research, GATE Mathematics (Vol. I & II), Advanced Engineering Mathematics, and Advanced Numerical Methods. He has also served as a reviewer for numerous international journals and mathematics textbooks. Dr. Jana has been the Chair of the International Conference on Engineering Mathematics and Computing (ICEMC) since 2019. In recognition of his research excellence, he was listed among the World's Top 5% Scientists in the 2025 SciRank Global Registry, achieving a global rank of 14,904.

Author Articles
Grape Leaf Doctor: Severity Assessment of Grape Black Measles via DeepLabV3+ Segmentation and Interval Type-2 Fuzzy Logic

By Dipak Kumar Jana Sourav Mandal Sudipta Roy

DOI: https://doi.org/10.5815/ijem.2026.05.05, Pub. Date: 8 Oct. 2026

Automated diagnosis of Grape Black Measles (GBM) disease has become a pivotal aspect of modern agribusiness due to its efficiency and rapidity. Manual segmentation and diagnosis of GBM are intricate tasks due to time and cost constraints. In this study, we introduce Grape Leaf Doctor, a novel method for the automatic detection and severity analysis of GBM, utilizing the response surface approach and interval type 2 fuzzy logic inference (IT2FL). Firstly, we employ the DeepLabV3+ semantic segmentation model based on ResNet50 to perform pixel-level predictions on images of grape leaves affected by fungal lesions. This model enables the identification of “regions of interest” (ROIs) and the calculation of the percentage of infections (POI). Subsequently, the IT2FL rule-based system is constructed to assess the severity of disease damage based on these features. In the IT2FL system, Gaussian and trapezoidal “membership functions” (MFs) are explored for inputs and outputs to facilitate fuzzy inference and defuzzification. The severity of GBM infection is categorized into four levels: ‘Healthy’, ‘Mild’, ‘Medium’, and ‘Severe’. The experimental results on the IT2FL hold-out test dataset show a general classification accuracy of 98.34%, whereas RSM achieves 90.69%. By merging image processing and statistical modeling, the DeepLabV3+ framework of the IT2FL system can efficiently recognize GBM across varying disease risks.

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