# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 2 Advancements and Techniques Used for Improving Crop Production
Different machine learning algorithms were applied and verified based on the mean absolute error (MAE), mean squared error (MSE), coefficient of determination (R\({}^{2}\) score), and cross-validation. The RFR has a higher accuracy compared to other approaches. [PERSON], [PERSON] et al. [25] suggested a hybrid method combining a grid search and wrapper feature selection for crop recommendation. Its performance is compared with the C4.5 decision tree and achieves the highest accuracy of 99.31%. The main aim is to assist farmers in crop selection and increase crop yield. [PERSON], [PERSON] et al. [26] conducted a comprehensive study on rainfall, soil condition, and climate using machine learning. The implementation of RF, SVC, DT, and logistic regression has been performed to predict relevant plants. The relevant crop data collected from data.gov.in and Kaggle were analyzed using machine learning methods. [PERSON], [PERSON] et al. [27] evaluate nutrient levels of soil and identify the nutrient requirements for the crop recommended. They proposed a model that implements deep learning techniques along with the voting ensemble technique for nutrient classification, which shows better performance with an accuracy of 0.928. [PERSON], [PERSON] et al. [28] used satellite data from the Ujjain district, Madhya Pradesh, and applied RF, Naive Bayes (NB), and ensemble techniques to analyze different classes of crops. A ground study has been conducted in three different intervals of a crop such as early wheat, mid wheat, and late wheat. It is seen that the RF algorithm implementation of images and its performance is better than others for satellite image data classification. [PERSON], [PERSON] et al. [29] collected a detailed soil mineral dataset with 12 different parameters from Kaggle and analyzed it using machine learning and artificial intelligence (AI) techniques. Seven different techniques such as logistic, Hoeffding Tree, Random Tree, random forest, Repeated Incremental Pruning Tree (REP Tree), and Multilayer Perceptron were applied and analyzed for different performance analysis metrics on crop recommendation. [PERSON], [PERSON] et al. [30] implemented an ensemble of different techniques to recommend crops for better decision making in the selection of crops for cultivation. The RF, DT, and SVC implemented on the Felin dataset, thereby applying the voting classifier and improvised results, shall be obtained. The performance measurement is analyzed based on accuracy, kappa score, and log loss values. [PERSON], [PERSON] et al. [31] implemented regression models for predicting the production rate of crops in different areas of northeast India. The R\({}^{2}\) score, root mean squared error (RMSE), coefficients of variation (CVs), and MAE are verified to identify the suitable prediction model. It predicts the top five crops with the highest average yield and is further analyzed for the most profitable crop. [PERSON], [PERSON] et al. [32] focused on sensory-basedsoil testing to reduce soil degradation. Different machine learning algorithms implemented crop recommendation, in which XGBoost had the highest accuracy of 99.31%. [PERSON], [PERSON] et al. [33] proposed an Arduino controller sensory system to collect soil and weather details and recommend crops in Srilankan cultivation lands. Machine learning techniques applied for crop selection had a high accuracy on a dataset collected from the Agriculture dept. of Sri Lanka. [PERSON], [PERSON] et al. [34] provided a proposal for an agricultural recommendation system, which is given for a reduced loss. The dataset was collected from different districts of Madhya Pradesh on crop production, rainfall, and soil type. Machine learning algorithms such as KNN, RF, DT, and logistic regression are applied to find the best recommendations, out of which RF has better accuracy. [PERSON], [PERSON] et al. [35] used the historical dataset from different regions of Karnataka based on soil weather parameters for analysis. A web page was developed with a pattern-matching approach for recommending crops.