# Crop Prediction Model Using Machine Learning Algorithms
## 2 Literature Review  
Machine learning approaches and algorithms are utilized in crop yield prediction methods to improve the quality of the crop so that the farmer's profit is maximized. The quality of the agricultural sector is improved; hence, the overall economy is enhanced. This issue has been discussed in detail in the literature [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. In [21], a review of machine learning algorithms to predict palm oil yield is discussed. The authors conducted a comparative analysis of the related work, focusing on the suggested approaches' advantages, disadvantages, and limitations. Furthermore, based on the discussion and evaluation of the existing studies, the authors provided a new architecture based on machine learning methods to predict palm oil yield. The authors in [22] focused on crop prediction and yield by studying soil quality, considering that soil properties have a major effect on crop production. The authors studied different soil properties, such as NPK (Nitrogen, Phosphorous, Potassium) levels, temperature, rainfall, moisture, PH value, and humidity. Comparative analyses concern three machine learning algorithms: Naive Bayes, Logistic Regression, and Random Forest. Moreover, the authors conducted a comparison between these algorithms concerning accuracy. A crop production model proposed in [23] aims to manage the produced crop using machine learning algorithms to help farmers in developing countries who are still using traditional methods and cannot recognize the correct market value of their products. The proposed system is based on three scenarios; firstly, choosing the best crops based on the farmer's location; secondly, providing guidance on soil preparation; and thirdly, providing the best way of crop marketing from farmer to consumer. The authors applied Support Vector Regression; Voting Regression techniques; Random Forest Regression algorithms; and proper real climate, weather, and soil data. Due to the scarcity of natural resources around the world, the authors in [24] proposed to utilize supervised machine learning algorithms, such as K-Nearest Neighbor Support Vector Machine, Random Forest, and Artificial Neural Network, to help farmers make the proper decision regarding crop selection and production; therefore, the country's overall economic status will be improved. Observing the growth process of chili and cotton crops using mobile phone images and machine learning techniques is the subject of the study [24]. The authors suggested a prediction method using the following supervised machine learning algorithms: Random Forest, Support Vector Machine, Decision Tree, K-Nearest Neighbor (K-NN), Gaussian Naive Bayes (GNB), and logistic regression (LR). The authors claimed that the study might help in the smart-farming process by recognizing the best machine learning algorithm for better crop prediction and analysis, especially for chili and cotton crops. In [25], a dataset for soil prediction was collected from Tamil Nadu Agricultural University (TNAU), India, which involved 32 districts. Based on the comparative analysis of different machine learning algorithms, such as Naive Bayes, Bayes Net, and Instance-Based Learner (IBK) algorithms, the authors claimed that the provided comparative results help farmers to make the proper decision regarding crop selection and production. The authors in [26] recommended 22 types of crops in this study and proposed a three-step framework: firstly, data preprocessing and feature extraction; secondly, classification; and, finally, performance evaluation. As a result of the comparison, the authors claimed that the best classifier for this problem is Naive Bayes, with an accuracy of 99.45%. This research work would provide a better outcome if the authors performed the classification and performance evaluation for a real problem.