Prediction of Best Suitable Crop using Machine Learning Technique
Keywords:
Machine Learning, Linear Regression, Random Forest Regression, Gradient Boosting Regression, Voting, StackingAbstract
The selection of best crop for cultivation, suitable according to agronomic and environmental factors at the particular area is a critical and responsible decision for the farmers. As agriculture in our country play very significant role in nation building by facilitating food export and providing major employment. The use of machine learning technology in agriculture field became boon because its nemours applications such as crop disease and weed detection, smart irrigation system, crop monitoring system and crop yield prediction etc. the study is focused on development of the ensemble regression model by leveraging the robust power of machine learning. Random Forest, Gradient Boosting and Linear Regression are used as base models. The Stacking and Voting techniques are used for development of proposed hybrid Models HVM1and HSM2, the performance matrix RMSE, MSE, MAE and R2 Score evaluated for the HVM1 and HSM2 has been compared. It is observed that the hybrid model HSM2 has highest R2 Score and lowest RMSE, MSE, MAE. The proposed hybrid model HSM2 helps farmer for selecting most suitable crop for cultivation for specific soil nutrients and environmental conditions.
Downloads
References
Ansarifar, Javad, Lizhi Wang, and Sotirios V. Archontoulis. "An interaction regression model for crop yield prediction." Scientific reports 11, no. 1 (2021): 1-14.
Bali, Nishu, and Anshu Singla. "Emerging trends in machine learning to predict crop yield and study its influential factors: A survey." Archives of computational methods in engineering 29, no. 1 (2022): 95-112.
Ip, Ryan HL, Li-Minn Ang, Kah Phooi Seng, J. C. Broster, and J. E. Pratley. "Big data and machine learning for crop protection." Computers and Electronics in Agriculture 151 (2018): 376-383.
Keerthana, Mummaleti, K. J. M. Meghana, Siginamsetty Pravallika, and Modepalli Kavitha. "An ensemble algorithm for crop yield prediction." In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), pp. 963-970. IEEE, 2021.
Medar, Ramesh, Vijay S. Rajpurohit, and Shweta Shweta. "Crop yield prediction using machine learning techniques." In 2019 IEEE 5th international conference for convergence in technology (I2CT), pp. 1-5. IEEE, 2019.
Nischitha, K., Dhanush Vishwakarma, Mahendra N. Ashwini, and M. R. Manjuraju. "Crop prediction using machine learning approaches." International Journal of Engineering Research & Technology (IJERT) 9, no. 08 (2020): 23-26.
Paudel, Dilli, Hendrik Boogaard, Allard de Wit, Sander Janssen, Sjoukje Osinga, Christos Pylianidis, and Ioannis N. Athanasiadis. "Machine learning for large-scale crop yield forecasting." Agricultural Systems 187 (2021): 103016.
Pantazi, Xanthoula Eirini, Dimitrios Moshou, Thomas Alexandridis, Rebecca Louise Whetton, and Abdul Mounem Mouazen. "Wheat yield prediction using machine learning and advanced sensing techniques." Computers and electronics in agriculture 121 (2016): 57-65.
Rashid, Mamunur, Bifta Sama Bari, Yusri Yusup, Mohamad Anuar Kamaruddin, and Nuzhat Khan. "A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction." IEEE access 9 (2021): 63406-63439.
Rezk, Nermeen Gamal, Ezz El-Din Hemdan, Abdel-Fattah Attia, Ayman El-Sayed, and Mohamed A. El-Rashidy. "An efficient IoT based smart farming system using machine learning algorithms." Multimedia Tools and Applications 80 (2021): 773-797.
Sharma, Bhawana, Lokesh Sharma, Chhagan Lal, and Satyabrata Roy. "Explainable Artificial intelligence for intrusion detection in IoT networks: A deep learning based approach." Expert Systems with Applications 238 (2024): 121751.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
