# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms  
[PERSON]  
1 School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar Pin 751024, India; [EMAIL_ADDRESS] or [EMAIL_ADDRESS]  
[PERSON]  
1 School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar Pin 751024, India; [EMAIL_ADDRESS] or [EMAIL_ADDRESS]  
[PERSON]  
2 School of Engineering, GIST University, Gunupur Pin 765022, India 22 School  
important role in farming. [PERSON], [PERSON] et al. [1] performed an extensive review of crop recommendations and productions based on soil and water management. It has been observed that crop selection is an important factor in increasing crop yield and reducing the risk of crop loss, and the efficient use of resources like water, pesticides, and fertilizers leads to a better agricultural outcome. New farmers mainly take opinions from fellow experienced local farmers for crop selection. However, they may be misled due to human errors and their relationships with others. So, the new farmers may obtain lots of confusing information on crop selection, estimating the crop production and their profits. This motivated us to believe that handy and appropriate guidance could be provided to the new farmers using an application. If there is heavy production of a particular crop, then it may lead to losses during its sales. The objective of this research is to identify and recommend crops that are not only more suitable for optimal production but also have higher market values.  
[PERSON], [PERSON] et al. [2] discussed identifying suitable crops based on the season, geographical location, and soil mineral properties. So, we have analyzed area-wise and season-wise crop production datasets so that suitable crops can be listed for a specific season and a particular land. [PERSON], [PERSON] et al. [3] analyzed crop datasets based on season and productivity. So, we collected datasets from different sources, such as Indian agriculture websites, based on these factors. Again, a data collection process was also conducted through interactions with experienced farmers based on their crop cultivation plans. The locally collected data were combined with the cloud dataset in a balanced way. Then, this dataset was analyzed for crop recommendations by applying suitable machine learning methods. These algorithms are used to help farmers make decisions, which will improve agriculture's sustainability and profitability.  
**Contribution:**  
The major contributions of this paper are as follows:  
* Analyzing the area-wise crop data for the Odisha state of India;
* Implementing 13 different classifiers and evaluating them using the SMOTE;
* Identifying the best classifier SGDC and prediction of suitable crops.  
The rest of the paper is structured as follows. Section 2 contains a review of different advancements and techniques used for improving crop productions and their comparative analysis; Section 3 presents the details of the materials and methods implemented, the proposed model, and the process of execution; Section 4 shows the experimental results; and finally, Section 5 explains the conclusions and future work concisely.