Batool Alsowaiq, Noura Almusaynid, Esra Albhnasawi, Wadha Alfenais, Suresh Sankaranarayanan, "Machine Learning Enabled Crop Recommendation System for Arid Land," WSEAS Transactions on International Journal of Environmental Engineering and Development, vol. 1, pp. 56-61, 2023, DOI:10.37394/232033.2023.1.7
Batool Alsowaiq, Noura Almusaynid, Esra Albhnasawi, Wadha Alfenais, Suresh Sankaranarayanan. Machine Learning Enabled Crop Recommendation System for Arid Land.
International Journal of Environmental Engineering and Development. 2023;1:56-61. 10.37394/232033.2023.1.7
International Journal of Environmental Engineering and Development
E-ISSN: 2945-1159
Volume 1, 2023
Machine Learning Enabled Crop Recommendation System for Arid Land
Authors: , , , ,
Abstract: The agriculture industry plays a significant role in the economy of many countries, and the population is regarded as an essential profession. To increase agricultural production, crops are recommended based on soil, weather, humidity, rainfall, and other variables which are beneficial to farmers as well as the nation. This paper explores the use of “machine learning” algorithms to recommend crops in for Arid land based on features selected from tropical climate where crops grow effectively. Five “machine learning” models have been validated for recommendation of crops for arid land which resulted in “Random Forest” topping as the best model.
Pages: 56-61 DOI: 10.37394/232033.2023.1.7WSEAS Transactions on International Journal of Environmental Engineering and Development, E-ISSN: 2945-1159, Volume 1, 2023, Art. #7
×
Citation Tools