# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 2 Advancements and Techniques Used for Improving Crop Production
[45] proposed combining the data collection based on a sensory system and historical data. A model was recommended, which was applied with principal component analysis (PCA) and linear discriminant analysis (LDA) for feature extraction and then applied with the ensemble technique, RF, KNN, and the artificial neural network (ANN) method. An ensemble machine learning model was used to analyze datasets and classify crops. Different evaluation metrics were used to verify the best method. It has been observed that RF has the highest accuracy of 84.17% for prediction. [PERSON], [PERSON] et al. [46] presented a Fused Classifier Algorithm (FCA) and an Interfused Machine Learning Algorithm (IMLA) to predict suitable crops in the Karnataka region using agro-climatic parameters. They evaluate various machine learning models and conclude that the IMLA achieves the highest accuracy at 82.7%, outperforming other classifiers. It aims to improve agricultural productivity by aiding farmers in crop selection for optimal yield in rural Karnataka. [PERSON], [PERSON] et al. [47] applied explainable AI to analyze the agri-workers' data to visualize the experienced and naive workers. The physical data are analyzed based on shapely additive explanations and a Light Gradient Boosting Machine (Light GBM). A wearable sensor is used to capture agri-workers' motion pictures and analyze human dynamics in fields. [PERSON], [PERSON] et al. [48] focused on plants to observe characteristics, such as height, leaf shape, leaf count, biomass, etc., using explainable artificial intelligence. It allows for the development of better crop management by identifying water requirements, flowering time, etc. [PERSON], [PERSON] et al. [49] developed body-sensing systems, like wearable sensors, for real-time motion data in agriculture. The data were analyzed using Python, sharing insights with workers and managers. Employed explainable artificial intelligence (XAI) and visualization-developed training methods for agricultural directors based on diverse worker experiences. [PERSON], [PERSON] et al. [50] applied XAI and interpretable machine learning on openly available data to observe the no-tillage effect on crops. They present insights on variable importance, interactions, associations with the response variable, and reasons behind predictions. [PERSON], [PERSON] et al. [51] suggested detecting and locating insect pests in crops using XAI techniques. The visualization of aiding human validation of the results is implemented by the convolution neural network (CNN). Here, analysis was performed on 75,000 images from 102 pest categories in the IP102 dataset. [PERSON], [PERSON] et al. [52] aimed to provide rice growers with precise N-rate recommendations using precision agriculture methods. By constructing a predictive rice yield model integrating soil, remote sensing, and climatic data, machine learning systems were employed to analyze a 5-year dataset. The variation Autoencoder is applied to enhance the model and find the correlation between the variables. [PERSON], [PERSON] et al. [53] applied the SMOTE to balance the dataset and applied different machine learning algorithms, in which Cat Boosting had the highest accuracy for classification. The crop dataset on soil minerals was collected from Kaggle and analyzed for crop recommendation. [PERSON], [PERSON] et al. [54] designed a model for smart controlling of the agriculture system. The sensors are used to collect data and using the Fuzzy Controller, the anomalous behavior is observed and notified to the farmer with suitable solutions. The data for soil temperature and water availability were collected for approx. 8600 rows per year. KNN, SVC, and Naive Bayes were applied for classification, out of which Naive Bayes had the highest accuracy of 99.2%. [PERSON], [PERSON] et al.