# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 2 Literature Review
Further research is needed to explore the scalability and practical applications of this framework in real-world farming scenarios. While the paper outlines the application of various machine learning methods, it could provide more detailed explanations of the algorithms and their specific configurations for the study. The paper discusses using a dataset for experiments and feature selection using the relief algorithm. More details about the dataset, such as its size, source, and characteristics, would add value. A systematic literature review in [15] analyzed 456 studies on deep learning in crop yield prediction, selecting 44 for analysis. The review found that convolutional neural networks (CNNs) were the most used algorithm for crop yield prediction, with the best performance in terms of root mean square error (RMSE). Challenges in applying deep learning technologies to crop yield prediction include a lack of large training datasets, issues with data preprocessing and feature extraction, and the importance of using remote sensing and weather data. Ensemble learning methods were found to be effective in improving the accuracy of crop yield prediction models. The systematic literature review approach is methodologically sound, but the paper could benefit from a more detailed discussion on the criteria for selecting and evaluating studies. A study in [16] presents an optimized deep learning model for crop yield prediction, achieving 97% accuracy and surpassing existing models. The approach considers various factors such as weather conditions, soil qualities, water levels, and location of the farm and uses a discrete deep belief network with the VGG-Net classification method. The data were collected from India's state agriculture webpage and feature preprocessing and feature extraction using the adaptive shearlet technique. The study demonstrates the potential of the proposed method for improving global food production and helping farmers make better management and financial decisions, with millet, rice, and wheat found to be the optimal crops for a high yield. A more in-depth explanation of the chosen deep learning techniques and the rationale for using the tweak chick swarm optimization algorithm would enhance understanding. The paper claims high accuracy, but a more detailed analysis of the testing process, including datasets and validation methods, is necessary for credibility. The study in [17] aims to predict Irish potato and maize crop yields using machine learning models. Three models, random forest, support vector regression, and polynomial regression were used, with rainfall and temperature used as climate-related predictors. The results showed that RF outperformed SVR and PR in predicting Irish potato yield, while it performed equally well as SVR in predicting maize yield. The study recommends further research on incorporating other variables, such as air humidity, soil moisture, and solar radiation, and exploring other machine learning algorithms, such as artificial neural networks and deep learning, for crop yield prediction. The findings can inform farmers, policymakers, and researchers about crop production and management. The study focuses on specific crops in a particular region, which might limit the generalizability of the results to other crops and regions. The paper could provide a more detailed justification for the selection of the specific machine learning models used. The study in [18] presents an approach using machine learning and deep learning techniques to identify and classify crop diseases automatically. The proposed method uses image processing techniques such as image acquisition, preprocessing, segmentation, feature extraction, SVM, CNN, KNN, and GLCM to identify plant diseases. The proposed method achieved an accuracy of 98.5% in identifying plant diseases and can be used as a tool for early detection and prevention of plant diseases, which can increase crop output by detecting diseases early on and taking appropriate measures to prevent further spread.