भा.कृ.अ.प. - भारतीय कृषि अनुसंधान संस्थान | ICAR-Indian Agricultural Research Institute

Agricultural Knowledge Management Unit (AKMU)      (3 scientists)

Dr. Mrinmoy Ray

   

Field of Specialisation : Statistical Modelling, Technological Forecasting, Time Series, Artificial Intelligence, Machine Learning/ Deep Learning, Data Science , Computer Vision

Dr. Mrinmoy Ray
Sr Scientist

M.Sc. : Indian Agricultural Research Institute, New Delhi

Ph.D : Indian Agricultural Research Institute, New Delhi

Google Scholar

PG School, Faculty Disciplines : Agricultural Statistics

No. of Students Guided : -    M.Sc :   2+1*(Ongoing)

Rajeev Ranjan Kumar, S. Vishnu Shankar, Ronit Jaiswal, Jaiprakash Bisen, Kapil Choudhary, V. Lavanya, Mrinmoy Ray & K. N. Singh (2025). Modelling Price Dynamics in Indian Potato Markets: A Comprehensive Econometric Approach, Potato Research/ Springer Nature, NA (NAAS rating: 8.30)
Arpan Kumar Maji, Sumanta Das, Sudeep Marwaha, Sudhir Kumar, Suman Dutta, Malini Roy Choudhury, Alka Arora, Mrinmoy Ray, Anbukkani Perumal , Viswanathan Chinusamy (2025). Intelligent decision support for drought stress (IDSDS): An integrated remote sensing and artificial intelligence-based pipeline for quantifying drought stress in plants, Computers and Electronics in Agriculture/Elsevier, 236 & 110477 (NAAS rating: 13.70)
G. H. Harish Nayak, Md. Wasi Alam, B. Samuel Naik, B. S. Varshini, G. Avinash, Rajeev Ranjan Kumar, Mrinmoy Ray & K. N. Singh (2025). Meta-transformer: leveraging metaheuristic algorithms for agricultural commodity price forecasting, Journal of Big Data/Springer, 12 & 1-29 (NAAS rating: 14.6)
Prabina Kumar Meher, Upendra Kumar Pradhan, Mrinmoy Ray, Ajit Gupta, Rajender Parsad, Pushpendra Kumar Gupta (2025). Ensemble of Bayesian alphabets via constraint weight optimization strategy improves genomic prediction accuracy, G3 Genes|Genomes|Genetics/ Oxford Academic, NA (NAAS rating: 8.2)
Rajeev Ranjan Kumar, Mrinmoy Ray, Ronit Jaiswal, Kapil Choudhry & K. N. Singh (2025). Leveraging Artificial Intelligence for Meteorological Drought Modelling and Forecasting, Journal of the Indian Society for Probability and Statistics / Springer, NA (NAAS rating: 6.6)
Ch. Preethi, A. Meena, Santosha Rathod, Balaji Naik B, S. Vishnu Shankar, Amit Saha, Mrinmoy Ray, , Rahul Patil (2025). Improved two stage triangular fuzzy STARMA model for drought forecasting in Southern Telangana, Journal of Hydrology, 665 & 134667 (NAAS rating: 12.3)
G. Avinash, V. Ramasubramanian, Mrinmoy Ray, Ranjit Kumar Paul, Samarth Godara, G.H. Harish Nayak, Rajeev Ranjan Kumar B. Manjunatha, ,Shashi Dahiya,Mir Asif Iquebal (2024). Hidden Markov guided Deep Learning models for forecasting highly volatile agricultural commodity prices, Applied Soft Computing/Elsevier, 158 & 111557 (NAAS rating: 13.2)
Samir Barman,Ramasubramanian V. & Mrinmoy Ray (2023). Improved Sieve Bootstrap based prediction intervals for time series, Communications in Statistics - Simulation and Computation /Taylor and Francis, 53(12) & 6612-6632. (NAAS rating: 6.8)
Tanuj Misra,Alka Arora,Sudeep Marwaha,Ranjeet Ranjan Jha,Mrinmoy Ray,Shailendra Kumar,Sudhir Kumar &Viswanathan Chinnusamy (2022). Yield-SpikeSegNet: An Extension of SpikeSegNet Deep-Learning Approach for the Yield Estimation in the Wheat Using Visual Images, Applied Artficial Intelligence/ Taylor & Francis, 36(1) & 2137642 (NAAS rating: 8.9)

Publications having NAAS rating 6 and above with first or corresponding author only

  1. Ray, M.*, Rai A., Singh, K. N., V., Ramasubramanian and Kumar, A. (2017).Technology forecasting using time series intervention based trend impact analysis for wheat yield scenario in India. Technological Forecasting and Social Change, 118,128-133. [NAAS:18.9]
  2. Ray, M.*, V., Ramasubramanian, Kumar, A. and Rai, A. (2014). Application of time series intervention modelling for modelling and forecasting cotton yield. Statistics and Applications, 12 (1&2), 61-70. [NAAS:6.3]
  3. Kumar, R.R., Shankar, S.V., Jaiswal, R., Ray, M.*, Budhlakoti, N., & Singh, K .N.(2025). Advances in Deep Learning for Medical Image Analysis: A Comprehensive Investigation. Journal of Statistical Theory and Practice, 19, Article 9. https://doi.org/10.1007/s42519-024-00422-2 [NAAS:6.60]
  4. Nayak, G.H.H., Alam, M.W., Avinash, G., Kumar, R.R., Ray, M.*, Barman, S., Singh, K.N., Naik, B.S., Alam, N.M., Pal, P., Rathod, S., & Bisen, J. (2024). Transformer based deep learning architecture for time series forecasting. Software Impacts, 22, 100716. https://doi.org/10.1016/j.simpa.2024.100716 [NAAS:7.30]
  5. Nayak, G.H.H., Alam, M.W., Singh, K.N., Avinash, G., Kumar, R.R., Ray, M.*, &Deb, C.K. (2024). Exogenous variable driven deep learning models for improved price forecasting of TOP crops in India. Scientific Reports, 14, 17203. https://doi.org/10.1038/s41598-024-68040-3 [NAAS:9.80]
  6. Ray, M.*, Rai, A., Singh, K. N. and V., Ramasubramanian. (2017).Modeling and forecasting of hybrid rice yield using a grey model improved by the genetic algorithm. International Journal of Agricultural and Statistical Sciences. 13 (2), 563-566. [NAAS:6.30]
  7. Ray, M.*, Singh, K. N., Ramasubramanian, V., Paul, R. K., Mukherjee, A. and Rathod, S. (2020). Integration of Wavelet Transform with ANN and WNN for Time Series Forecasting: an Application to Indian Monsoon Rainfall. National Academy Science Letter. 43, 509-513. [NAAS:7.20]
  8. Saha, A., Singh, K. N., Ray, M.* and Rathod, S. (2022). Fuzzy rule–based weighted space–time autoregressive moving average models for temperature forecasting.Theoretical and Applied Climatology, 150, 1321-1335. [NAAS:8.80]
  9. Ray, M.*, Ramasubramanian, V., Singh, K.N. et al. (2022). Technology Forecasting for Envisioning Bt Technology Scenario in Indian Agriculture. Agricultural Research, 11, 747–757. https://doi.org/10.1007/s40003-022-00612-z [NAAS:7.40]
  10. Harish Nayak G. H., Alam, W., Singh, K.N. Avinash, G., Ray, M.* and Kumar, R.R. (2024) Modelling Monthly Rainfall of India through Transformer-based Deep Learning Architecture, Modeling Earth Systems and Environment, https://doi.org/10.1007/s40808-023-01944-7 [NAAS:8.70]

Patent / Technologies / Methodologies / System etc. (Upto Five) :

  1. R package TDSTNN: Time Delay Spatio Temporal Neural Network (https://cran.rproject.org/web/packages/TDSTNN/index.html ) [ Downloaded more than 1,200 times as of 09 May 2025.]
  1. R package TSSVM: Time Series Forecasting using SVM Model (https://cran.rproject.org/web/packages/TSSVM/index.html ) [ Downloaded more than 4500 times as of 09 May 2025.]
  1. R Package GreyModel: Fitting and Forecasting of Grey Model (https://cran.rproject.org/web/packages/GreyModel/index.html ) [ Downloaded more than 12000 times as of 09 May 2025.]
  1. R package tsfngm: Time Series Forecasting using Nonlinear Growth Models (https://cran.rproject.org/web/packages/tsfngm/index.html ) [ Downloaded more than 9500 times as of 09 May 2025.]
  1. Artificial Intelligence based Disease Identification System for Crops (AI-DISC) a compressive AI-powered mobile application for automatic identification of disease and insect-pest of different crops. Available at https://play.google.com/store/apps/details?id=com.ai.ai_disc&hl=en_IN&pli=1. ( Certified by ICAR)

Awards / Recognitions / Fellowship (Upto Five) :

  1. Jawaharlal Nehru Award for P.G. Outstanding Doctoral Thesis Research in Agricultural and Allied Sciences 2018 – Social Sciences by ICAR
  1. कृषि बिज्ञान गौरव (मानद उपाधि) [KRISHI VIGYAN GAURAV (Honorary Title)]
  1. ICAR-IARI merit fellowship for Ph.D.
  1. ICAR Junior Research Fellowship for M.Sc.