# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 2 Advancements and Techniques Used for Improving Crop Production
[14] discussed the design and understanding of precision agriculture systems depending on 5S technology. Precision agriculture leads to the overall development of agriculture, like the utilization of resources and fertilizer without wastage. In proper times, we have to use them, and requirements that are needed by crops should be provided so that the production rate will increase, which can benefit the farmers compared to traditional farming. So, smart farming can replace traditional farming, which will improve the production rate of crops; ultimately, a farmer will obtain benefits using precision agriculture. [PERSON] et al. [15] focused on finding the crop price based on fuel price, crop production, rainfall, and temperature. The authors considered four major crop data for analysis. Machine learning methods have been applied, and the root mean square is chosen as the measuring parameter. It has been observed that the tree-based models forecast better than others. [PERSON] et al. [16] used a previous dataset for crop yield estimation. The SVC and RF are chosen for identifying crops and recommending their fertilizer needs. Crop data over the last five years were collected from different sources and analyzed. The soil classification, yield prediction, and fertilizer needs are three steps that have been proposed to implement. [PERSON] et al. [17] suggested a crop recommendation platform using cloud services. To find the best recommended details, KNN, DT, RF, XG-Boost, and SVC were applied. The experimental results have been analyzed based on accuracy, precision, recall, and F1-score. Here, the dataset has 2200 records that contain the soil and weather parameters such as temperature, humidity, pH, NPK, and rainfall details. [PERSON], [PERSON] et al. [18] presented TerraSAR-X satellite images of crops analyzed using the random forest, classification, and regression tree (CART) methods. Crop classification was applied and, overall, it has been observed that the performance of RF is better compared to CART. [PERSON], [PERSON] et al. [19] proposed a system for crop suggestions to farmersbased on soil parameters and season. Data analysis has been conducted using linear regression, a neural network, KNN, Naive Bayes (NB), and SVC and compared based on their performances, out of which neural networks have the highest of 89.88% accuracy. [PERSON], [PERSON] et al. [20] applied the ANN and random forest regression (RFR) tools for crop yield prediction. The data on 4 years on winter rapeseeds from Serbia has been used for analysis using both methods, out of which the prediction capability of the RFR is better than the other. A high correlation between oil and seed crops has been observed. [PERSON], [PERSON] et al. [21] proposed an ensemble model by integrating Generalized Poisson Models (GPMs), CNNs, and SVCs for analyzing sugarcane production. A benchmark Godavari dataset of 5 years was used for analysis. It is observed that the CNN obtained the highest accuracy of 89.53%. [PERSON], [PERSON] et al. [22] performed an ensemble of the SVC and ANN, and by applying majority voting, the recommendation is obtained. It has higher accuracy compared to the SVC and ANN. The dataset collected from the polytest laboratories of Maharashtra had a variety of crops such as cotton, ground nut, banana, paddy, sugar cane, coriander, etc. [PERSON], [PERSON] et al. [23] used 28,242 instances with a 7-feature dataset. The climate condition, rainfall, and crop type are important parameters for the dataset used. Different ensemble approaches were applied from prediction, out of which the Ada Boost Regressor with a decision tree had the highest accuracy of 95.7%. [PERSON], [PERSON] et al. [24] conducted a study on maize, groundnut, and Bengal gram crops in Telangana state. The dataset from the Information Technology, Electronics, and Communications Department (ITE&C) was analyzed. A dataset of different weather throughout the year has been used.