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             additional factors such as economic variables, market trends,   [5]   Sabharwal, A., & Reddy, S. R. N. (2024). Optimizing
             and disease forecasts to refine the recommendation system   Crop  Predictions:  A  Machine  Learning  Approach
             further.  Expanding  the  dataset  and  integrating  current   Incorporating  Environmental  Parameters  and
             weather  information  could  also  enhance  the  model's   Nutrient   Management.   Proceedings   of   the
             precision and applicability across diverse regions. Continued   International Conference on Agricultural Innovations,
             progress in these technologies has the potential to steer the   58(1), 10-15.
             agricultural sector  toward  more sustainable and  efficient   [6]   A.  Sabharwal  and  S.  Reddy,  "Optimizing  Crop
             farming practices.
                                                                     Predictions:   A   Machine   Learning   Approach
             Reference                                               Incorporating  Environmental  Parameters  and
             [1]   Ruchirawya,  T.  H.,  Bandara,  P.,  &  Weerasooriya,  T.   Nutrient Management," 2024 International Conference
                  (2020). Crop Recommendation System. International   on  Computational  Intelligence  and  Computing
                  Journal of Computer Applications, 175(22), 22-28.    Applications (ICCICA), Samalkha, India, 2024, pp. 75-
             [2]   Pravallika, N., Nikhil, Y., Yogi, M., & Lalit, M. (2023).   79, doi: 10.1109/ICCICA60014.2024.10584911
                  Crop  Recommendation  System  Using  Machine   [7]   Sharma,  A.,  Arya,  A.,  &  Joshi,  A.  (2024).  Crop
                  Learning: A Comparative Study. KEC Conference, 1(1),   Recommendation  System.  International  Journal  of
                  2-8, DOI: https://doi.org/10.3126/injet.v1i2.66708   Research Publication and Reviews, 5(4), 50-55. DOI:
             [3]   Sharma, V., Vats, S., Rawat, P., & Bajaj, M. (2023). Crop   10.55248/gengpi.2024.5.5.1095-1098.
                  Recommendation System: A Review. In Advances in   [8]   Usha Kosarkar, Gopal Sakarkar, Shilpa Gedam (2022),
                  Smart Agriculture (pp. 45-60). Springer.           “An Analytical Perspective on Various Deep Learning
             [4]   Kumar,  R.  B.,  Kempegowda,  B.,  Celso,  A.  B.,  &   Techniques for Deepfake Detection”, 1st International
                                                                     Conference  on  Artificial  Intelligence  and  Big  Data
                  Sushmitha, R. (2019). Crop Recommendation System
                                                                     Analytics (ICAIBDA), 10th & 11th June 2022, 2456-
                  for  Precision  Agriculture.  International  Journal  of
                                                                     3463,     Volume      7,     PP.     25-30,
                  Computer Sciences and Engineering, 7(5), 1-5.
                                                                     https://doi.org/10.46335/IJIES.2022.7.8.5






















































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