# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 2 Advancements and Techniques Used for Improving Crop Production
[55] demonstrate the efficacy of neural network models, particularly long short-term memory and one-dimensional convolutional neural networks, in forecasting the crop yield using data from the Monitoring Agricultural Resources (MARS) Crop Yield Forecasting System. Comparative analyses reveal that the long short-term memory (LSTM) recurrent neural network model outperforms the Gradient-Boosted Decision Trees (GBDTs) model for soft wheat in Germany and performs comparably for other case studies. [PERSON], [PERSON] et al. [56] went for classification and disease prediction based on crop images. The model proposes a new plant based on analysis of 4720 thermal images. The CNNand XAI are implemented to classify the crop diseases. A database of paddy crop disease is used for analysis and comparison with the thermal images. It has been observed that the classification with higher accuracy is 98.5% for thermal images compared to the paddy crop dataset. [PERSON], [PERSON] et al. [57] propose the Multi-Attribute Weighted Tree-based Support Vector Machine (MAWT-SVM) approach to predict crop yields. Data were collected for 8 years from 1999 to 2007 on agriculture productivity and meteorological information. The methodology employs z-score normalization, principal component analysis (PCA), and genetic algorithms (GAs) to enhance performance. The results indicate that MAWT-SVM outperforms other methods. It offers a better solution for improving economic growth through optimal crop selection. [PERSON], [PERSON] et al. [58] proposed an ensemble model to enhance crop production accuracy. The dataset used was from the agroecological zone. By leveraging agricultural, environmental, and soil conditions, this approach aids farmers in informed crop selection decisions, employing a multilayered ensemble model to improve prediction performance. The evaluation metrics, including accuracy (97.1%) and F1-score (97.09%), validate the model's effectiveness. [PERSON], [PERSON] et al. [59] developed a deep learning approach by an ensemble of the CNN and recurrent neural network. (RNN) applied to long short-term memory for cocoa yield prediction. The climate data of 31,320 samples were collected from 1988 to 2017 from cocoa-producing areas of southwest Nigeria. Mainly, the ensemble model utilizes the CNN for handling climatic data and the RNN for yield prediction. Benchmarking against other machine learning algorithms, the CNN-RNN with LSTM demonstrates superior performance based on metrics, like the MAE and MSE, highlighting its efficiency for cocoa yield prediction. [PERSON], [PERSON] et al. [60] classified fertilizers based on soil minerals obtained and the number of fertilizers required. The voting classifier and the decision tree were supplied with a sample of 10 data. It has been observed that the voting classifier (VTC) acquired a higher accuracy of 96% compared to the decision tree. [PERSON], [PERSON] et al. [61] briefly discussed variable rate irrigation technologies to reduce water usage and focused on agronomic factors. It has been seen that the author gives importance to different tools to measure soil water status and crop growing conditions, which shall be analyzed using proximal sensing data. [PERSON], [PERSON] et al. [62] suggested a methodology for crop yield prediction, monitoring crops and their market value analysis. A dataset from 64 districts of Bangladesh has been collected for analysis. They applied random forest, Support Vector Machine, and Voting Ensemble Regression. It was found that the voting regression has the highest R\({}^{2}\) value of 82.8% compared to the others. It was observed that there was a seasonal analysis of six crops: Aus Rice, Aman Rice, Boro Rice, Wheat, Maize, and Lentil. [PERSON], [PERSON] et al. [63] suggested an XAI-CROP algorithm that uses local interpretable model agnostic explanations for crop recommendations. They compared the performance of gradient boosting, decision tree, Gaussian Naive Bayes, and Multimodal Naive Bayes with XAI.