| Optimizing Crop Selection Using Machine Learning |
| Paper ID : 1015-ICCITS (R1) |
| Authors |
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Dr. Mohamed Ahmed Hussein * 193 عمارات الشباب - مدينة الشروق - مدينة الشروق |
| Abstract |
| Abstract The agricultural sector is one of the most important economic sectors in Egypt, playing a crucial role in meeting the food needs of the population, it provides job opportunities, promotes rural development, and contributes to economic stability. Egypt’s fertile resources and suitable climate offer significant potential for agriculture, which makes it have great potent After training these models ial in this Sector. In this paper, we have developed a model for selecting crops suitable for Agricultural land that relies on a random forest algorithm to analyze Agricultural data that includes important environmental variables such as soil Content of nitrogen, phosphorus and potassium, temperature, humidity, Acidity value (pH), and rainfall amounts. Several models have been trained on our data, Obtained from various sources such as FAO and Kaggle warehouse. These models include Support Vector Machine (SVM) [1], Decision Tree (DT) [2], Linear Regression (LR) [3], Random Forest(RF) [4]. The trained models were evaluated using accuracy, precision, and recall metrics. These metrics help Identify the model that best fits our data, based on its ability to make Accurate predictions while minimizing excessive errors. |
| Keywords |
| Keywords: Support Vector Machine(SVM), Decision Tree (DT), Random Forest(RF), Linear Regression(LR). |
| Status: Accepted (Oral Presentation) |