Research on crop classification methods based on machine learning using wavelet transformations

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Due to the growing demand for precise and efficient agricultural monitoring and management systems, interest in crop classification using machine learning has significantly increased in recent years. Conventional machine learning algorithms, however, have limitations when dealing with high-dimensional data. Training time is a crucial factor in developing crop classification models, as it directly impacts the model's efficacy and efficiency. Large volumes of data are often needed to train crop classification models properly, making the training procedure laborious and computationally demanding. In this paper, crop classification model training time is reduced by utilizing wavelet decomposition in combination with traditional machine learning techniques such as SVM, Naive Bayes, RF, and others. The performance of different algorithms before and after utilizing wavelet decomposition was evaluated in order to find the way that is the most efficient while using this methodology. Additionally, the significance of quality loss when applying wavelet coefficients was determined. The results of this paper show that applying wavelet transformation coefficients in combination with classification techniques can achieve accuracy levels that are comparable to those achieved by training on the original images. For example, using the Random Forest model in combination with Daubechies transformation coefficients can achieve an accuracy of 0,89 while significantly reducing training time from 11,15 to 3,49 seconds with Haar transformation providing almost identical results. The paper demonstrates the value of using wavelet transforms for crop classification and highlights the significance of accounting for training time when developing accurate and practical crop classification models that may be useful in developing decision support tools for agricultural applications, where it is crucial to make prompt decisions based on current data.