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Title |
Comparative Analysis of Machine Learning Models for Crop’s yield Prediction |
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Author |
Zaheer Ud Din Babar, Riaz UlAmin, Muhammad Nabeel Sarwar, Sidra Jabeen, and Muhammad Abdullah |
| Citation |
Vol. 22 No. 5 pp. 330-334 |
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Abstract |
In light of the decreasing crop production and shortage of food across the world, one of the ?crucial criteria of agriculture nowadays is selecting the right crop for the right piece of land at the ?right time. First problem is that How Farmers can predict the right crop for cultivation because ?famers have no knowledge about prediction of crop. Second problem is that which algorithm is ?best that provide the maximum accuracy for crop prediction. Therefore, in this research Author ?proposed a method that would help to select the most suitable crop(s) for a specific land based ?on the analysis of the affecting parameters (Temperature, Humidity, Soil Moisture) using machine ?learning. In this work, the author implemented Random Forest Classifier, Support Vector ?Machine, k-Nearest Neighbor, and Decision Tree for crop selection. The author trained these ?algorithms with the training dataset and later these algorithms were tested with the test dataset. ?The author compared the performances of all the tested methods to arrive at the best outcome. In ?this way best algorithm from the mention above is selected for crop prediction.? |
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Keywords |
Spinach, Humidity, Standard deviation, Logistic Regression |
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