ARCHIVES

1. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
2. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147, 70-
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3. Khan, Y., Kumar, V., Setiya, P., & Satpathi, A. (2023). Comparison of phenological weather indices based statistical, machine learning and
hybrid models for soybean yield forecasting in Uttarakhand. Journal of Agrometeorology, 25(3), 425-431.
4. Setiya, P., Satpathi, A., Nain, A. S., & Das, B. (2022). Comparison of weather-based wheat yield forecasting models for different districts of
Uttarakhand using statistical and machine learning techniques. Journal of Agrometeorology, 24(3), 255-261.
5. Pant, J., Pant, P., Pant, R. P., Bhatt, A., Pant, D., & Juyal, A. (2021). Soil quality prediction for determining soil fertility in Bhimtal Block
of Uttarakhand (India) using machine learning. International Journal of Analysis and Applications, 19(1), 91-109.
6. Malik, A., Kumar, A., Salih, S. Q., Kim, S., Kim, N. W., Yaseen, Z. M., & Singh, V. P. (2020). Drought index prediction using advanced
fuzzy logic model: Regional case study over Kumaon in India. Plos one, 15(5), e0233280.
7. Pargaien, S., Prakash, R., & Prakash, V. (2023). Change of agriculture area over the last 20 years: A case study of Nainital District,
Uttarakhand, India. Journal of Resources and Ecology, 14(5), 983-990.
8. Chaudhary, N., Mallick, K., Danodia, A., Pandey, K., & Nagendra, S. R. (2026). Satellite-based Crop Discrimination with Machine Learning
on GEE Platform: Insights from Udham Singh Nagar. Journal of Geomatics, 20(1), 118-127.
9. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020
statement: an updated guideline for reporting systematic reviews. bmj, 372.10. Setiya, P., Nain, A. S., & Satpathi, A. (2024). Comparative analysis of SMLR, ANN, Elastic net and LASSO based models for rice crop yield
prediction in Uttarakhand. Mausam, 75(1), 191-196.
11. Sah, S., Haldar, D., Singh, R. N., Das, B., & Nain, A. S. (2024). Rice yield prediction through integration of biophysical parameters with
SAR and optical remote sensing data using machine learning models. Scientific reports, 14(1), 21674.
12. Van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review.
Computers and electronics in agriculture, 177, 105709.
13. Paudel, D., Boogaard, H., De Wit, A., Janssen, S., Osinga, S., Pylianidis, C., & Athanasiadis, I. N. (2021). Machine learning for large-scale
crop yield forecasting. Agricultural Systems, 187, 103016.
14. Khaki, S., & Wang, L. (2019). Crop yield prediction using deep neural networks. Frontiers in plant science, 10, 621.
15. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural systems, 153, 69-80.
16. Navarro, E., Costa, N., & Pereira, A. (2020). A systematic review of IoT solutions for smart farming. Sensors, 20(15), 4231.
17. Elijah, O., Rahman, T. A., Orikumhi, I., Leow, C. Y., & Hindia, M. N. (2018). An overview of Internet of Things (IoT) and data analytics in
agriculture: Benefits and challenges. IEEE Internet of things Journal, 5(5), 3758-3773.
18. Ayaz, M., Ammad-Uddin, M., Sharif, Z., Mansour, A., & Aggoune, E. H. M. (2019). Internet-of-Things (IoT)-based smart agriculture:
Toward making the fields talk. IEEE access, 7, 129551-129583.
19. Bhatt, C. K., & Nain, A. S. (2024). Integration of Sentinel-1A SAR data with CERES-RICE model for predicting rice yield in Udham Singh
Nagar, Uttarakhand. MAUSAM, 75(3), 649-658.
20. Hegde, A. S., Ranjan, R., & Hegde, S. S. (2024). Crop classification and cropping intensity estimation using geospatial technology in the
upper Gangetic plains of Uttarakhand. Heliyon, 10(22).
21. Sharma, V., & Ghosh, S. K. (2023). Evaluating the potential of 8 band Planetscope dataset for crop classification using random forest and
gradient tree boosting by Google Earth Engine. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information
Sciences, 48, 325-330.
22. Umutoni, L., & Samadi, V. (2024). Application of machine learning approaches in supporting irrigation decision making: A review.
Agricultural Water Management, 294, 108710.
23. Islam, M., Bijjahalli, S., Fahey, T., Gardi, A., Sabatini, R., & Lamb, D. W. (2024). Destructive and non-destructive measurement approaches
and the application of AI models in precision agriculture: a review. Precision Agriculture, 25(3), 1127-1180.
24. Pai, D. G., Balachandra, M., & Kamath, R. (2025). Explainable AI in agriculture: Review of applications, methodologies, and future
directions. Engineering Research Express, 7(3), 032202.
25. Erike, A., Ikerionwu, C., Azubogu, A., & Obodoagwu, V. (2025). Is AI for illiterate farmers? A systematic literature review of AI and
machine learning applications and challenges for precision agriculture. Discover Artificial Intelligence, 5(1), 204.