Analysis of a Crop Recommendation System for Farmers Based on Machine Learning
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 11 |
| Year of Publication : 2024 |
| Authors : Ujwala Ghodeswar, Minal Keote |
10.14445/23488379/IJEEE-V11I11P126
How to Cite?
Ujwala Ghodeswar, Minal Keote, "Analysis of a Crop Recommendation System for Farmers Based on Machine Learning," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 11, pp. 275-283, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I11P126
Abstract:
Farmers must identify a certain soil type crop before sowing seeds. A farmer's ability to determine which crop to plant will boost the production of the land. Farmers risk losing their crops, wasting their time, and losing the money they invested in cultivating them. A system that recommends crops based on machine learning is suggested to make this process easier for farmers. Eight machine learning algorithms are applied to determine the crop for a given plot of land. Some techniques include decision Tree, Naïve Bayes, K Nearest Neighbour, Random Forest, Adaboost, Logistic Regression, Gradient Boosting, and Support Vector Machine. The dataset utilized for the system was obtained from Kaggle. This data collection consists of 2200 rows of varying values of seven features: N, P, K, temperature, humidity, rainfall, soil pH, and one output label. The above algorithms are trained using a 20% test and 80% training data set. Accuracy is calculated from the machine learning algorithm compared to other algorithms, and the Naïve Bayes algorithm gives good accuracy.
Keywords:
Accuracy, Decision tree, Logistic regression, Machine Learning, XGBoost.
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