# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 5 Conclusions and Future Work  
An extended literature survey that includes many different techniques and methodologies was applied in crop recommendations and used for improving profitability in cropping. In the survey, it is observed that many researchers applied research on using wireless sensors, RFID-based sensors, sensors for monitoring water usage, and the use of satellite images for analysis. Also, they analyzed soil minerals and weather parameters and the suitability of different crops in fields using machine and deep learning tools on different datasets. The objective of the proper use of agriculture fields without wastage of water and minerals is seen.  
We have analyzed the collected crop production dataset at URL: ([[https://data.wordId/thatzprem/agriculture-india](https://data.wordId/thatzprem/agriculture-india)]([https://data.wordId/thatzprem/agriculture-india](https://data.wordId/thatzprem/agriculture-india)) (accessed on 5 January 2024). We applied 13 different classifiers to it to find the most suitable technique that can recommend crops with a higher accuracy. We applied data reduction and confined our dataset in three districts of Odisha state, India. Also, by collecting suggestions from local agriculture officers and experienced farmers, the data were improvised.  
We proposed a model to identify a suitable classifier based on performance analysis, and we can predict the crops without any anomalies. Initially, we applied 13 different classifiers without the SMOTE, and we saw that the accuracy of the SVC is 1.0, the XGB Classifier is 0.97, and the SGDC is 0.95. But, for appropriate prediction, dataset balancing is very important. So, after cleaning and pre-processing in a standard way, we applied the classifiers after the SMOTE to the data. It has been observed that the accuracy of the SVC is reduced to 0.74 and XGBoost to 0.97, and the SGDC is improved to 1.0.  
So, we conclude that the prediction after balancing the dataset shall be more accurate. In the future, we shall extend our work by implementing XAI techniques to optimize and enhance performance. Also, we shall implement a large dataset that shall cover a vast area.  
**Author Contributions:** [PERSON], [PERSON] and N.P. conceived the idea, designed and performed the experiments, analyzed and investigated the results, drafted the manuscript, and revised the final manuscript. [PERSON] and A.R.: visualization; [PERSON]: supervision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 15: Line chart based on precision–recall before and after the SMOTE.  
Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.  
Acknowledgments: I would like to express my special thanks to [PERSON], Professor, Deputy Dean (R&D), Computational Science (Guide), GIET University and the Agriculture Department of GIET University for permitting us to conduct different tests on soils of different crops and interact with Agriculture officers.