# Crop Prediction Model Using Machine Learning Algorithms
## 2 Literature Review
Since crop monitoring is considered the main domain in the smart-farming process and crop diseases are the main reason for yield losses, especially in developing countries, the authors in [27] provided a comprehensive survey on crop monitoring techniques concerning crop yield estimation and disease detection using deep learning models. Based on the results and comparison, the authors claimed that crop monitoring techniques using deep learning methods are more accurate and powerful than some developing countries' traditional methods. In [28], the authors proposed a novel technique based on Support Vector Machines for auxiliary information on real applications of the agriculture sector. The authors claimed that they obtained an accuracy of 91% compared to the existing applications. The farmers can use this proposed methodology to gain a better yield of the crops, and different governmental sectors can use it to improve crop productivity. However, the authors did not suggest any recommendations for fertilizer systems to improve crop management. In [29], a machine learning and Multilayer Perception method, along with the quantity of rainfall, have been suggested by the authors to help farmers in making a proper decision regarding a harvest even before they start planting. Furthermore, the suggested method focuses on the optimal process for the marketing and storage of the crop. Based on the provided results, the authors claimed that the proposed method would be beneficial for farmers to improve agricultural yield outcomes. In [30], the main objective of this research was to predict crop productivity loss by utilizing linear regression methods based on data taken from the previous year's statistics. The models were created using real-world data, and the evaluation process was based on samples. The authors applied both Naive Bayes and Decision Tree algorithms in this evaluation. They claimed that the proposed method improves production and maximizes the farmer's profit. In [31], the authors developed a web-based application for crop yield prediction to be used by farmers. This tool provides farmers with a list of various crops planted previously to predict and learn about the best crop to cultivate in the future. Furthermore, the tool can provide farmers with climate data and information to help them make the best decision regarding market demand and prices. In [32], the author's survey's main objective was to evaluate the performance of different publications from 2016 to 2020 that aimed to predict fungal illnesses on the crops. The authors evaluated different machine learning algorithms utilized in the literature. As per the provided comparative results, the authors concluded that the best performance among all machine learning models, SVM, variations of choice trees, and Naive Bayes has been widely utilized and gained the best results regarding the yearly prediction of crop diseases. In [33], using machine learning, the authors proposed a system for the early prediction of crop diseases in plants by utilizing the Convolutional Neural Network (CNN) method. The dataset that is taken from a village is trained and tested. Different diseases are collected in a database, and the classifier is trained to compare the accuracy and choose the one with high accuracy. The provided model helps farmers predict plant diseases and make the best decision regarding the type of crop to be planted. The authors in [34] recognized problems facing farmers in India regarding crop yield prediction; therefore, they collected datasets published online. To facilitate analyzing and studying data, the dataset was clustered using the K-Means Clustering algorithm, and the Naive Bayes algorithm was used to recognize the best crop to plant. The provided analysis and results show that the proposed system is beneficial for farmers, not only for the early prediction of crop yields but also for selecting the best crop to plan.