Comparative Analysis of Machine Learning Algorithms for Crop Variety Prediction: Performance Metrics, Data Requirements, and Methodological Insight - Shivani Rastogi (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The prediction of crop variety performance is crucial for agricultural planning and decision- making. Traditional methods often fall short in handling the complexity and volume of data required for accurate predictions. Recently, machine learning algorithms have shown promise in improving prediction accuracy. This study aims to compare various machine learning methods in terms of their performance metrics, data requirements, and methodological strengths and limitations in the context of crop variety prediction. A comprehensive meta- analysis was conducted, reviewing 40 studies that applied different machine learning algorithms, including Convolutional Neural Networks (CNN), Random Forests (RF), Deep Neural Networks (DNN), Support Vector Machines (SVM), and more. Performance metrics such as RMSE, and accuracy were standardized for comparison. The studies covered a range of crops including corn, soybean, rice, and wheat, with test sample sizes varying from 80 to 2500 samples. The results indicate that RF and DNN generally perform well across various metrics, while CNN methods excel particularly in classification tasks. Data requirements and performance varied significantly, with CNN-based methods requiring larger datasets compared to traditional models. This meta-analysis highlights the potential of machine learning algorithms to enhance crop variety prediction accuracy. RF and DNN are robust performers across diverse datasets, while CNNs are particularly effective for specific applications. The study underscores the importance of selecting appropriate algorithms based on the specific prediction task and available data. Future research should focus on optimizing data collection and preprocessing to further improve prediction accuracy and applicability of these methods in real-world agricultural settings.