# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 2 Advancements and Techniques Used for Improving Crop Production
A comparative analysis based on accuracy is performed for the two districts dataset of Karnataka, out of which DT has 76.8%. [PERSON], [PERSON] et al. [36] prepared a dataset by combining the collected soil and weather parameters for 20 different crops from the Indian Meteorological Department (IMD), Pune, and Karnataka state. An application was developed to filter the crops using pattern-matching techniques at multiple levels and predict the suitability of crops. [PERSON], [PERSON] et al. [25] proposed a model that applies feature section using the Wrapper method, classification using the partial decision tree algorithm, and hyperparameter tuning using the grid method. Here, the soil features, humidity, and rainfall information of 2200 instances were used for analysis. It is observed that after hyperparameter tuning, an accuracy of 99.31% was obtained. [PERSON], [PERSON] et al. [37] used a soil crop dataset from Kaggle and applied machine learning algorithms, such as RF and KNN, for classification. A total of 2201 records were used from previous historical data for analysis, and predicted suitable crops based on production, and an accuracy of 99.5 was obtained for the random forest method. [PERSON], [PERSON] et al. [38] used a machine learning approach such as DT, RF, Hoeffding Tree, and J48 for classification, and different performance metrics such as precision, F-measure, and recall were implemented. Multiple sensors are installed in different areas of agricultural land, and the real-time collected data from sensors are fused for analysis. The result analysis observed that the performance of random forest is higher than other approaches. [PERSON], [PERSON] et al. [39] suggested a recommendation system using web application implementation for predicting crops using five different machine learning algorithms. The performance of random forest is better than others in prediction with an accuracy of 97.18%, which is applied to a dataset with 1800 entries. [PERSON], [PERSON] et al. [40] proposed a machine-learning sensory device for soil nutrient monitoring and analysis. The real-time data are collected using sensors and analyzed to generate recommended crops and assess the device's capabilities. It has been observed that the Cat Boost classifier, having a 97.5% accuracy, is better than the rest of the applied methods. [PERSON], [PERSON] et al. [41] collected 180 soil samples from specific areas of Assam state and tested the samples to obtain their physical properties. After pre-processing the samples, the data were used for analysis using a decision tree with an accuracy of 94%. Based on water retention capacity, hydraulic conductivity, and particle density, the crops are recommended. [PERSON], [PERSON] et al. [42] used soil and climate data for analysis and predicted the crops for improving yield and profits. DT, RF, KNN, NB, SVC, NN, and logistic regression were applied to a dataset from Kaggle and verified for their performance. A total of 22 different crops were taken as labels for analysis using machine learning methods, and it is observed that the RF and NB were obtained with an accuracy of 99.5%. [PERSON], [PERSON] et al. [43] focused on guiding individuals in suggesting crops and nutrients needed for their growth. Datasets from Kaggle based on crop and soil were analyzed. A total of 2200 samples with 22 labels were analyzed for attributes such as NPK, pH, and rainfall. Also, weed identification and pest identification have been conducted to take necessary measures. [PERSON], [PERSON] et al. [44] conducted research in the Maharashtra and Karnataka region. A mobile application was developed that collects the area and soil type as the input. The SVC, ANN, RF, KNN, and Multiple Linear Regression (MLR) were applied, out of which RF had the best performance with 95% accuracy. Here, based on the global positioning system (GPS), the location has been tracked, which helps identify the rate of rainfall, crop suitability, and fertilizer needs. [PERSON], [PERSON] et al.