Utilizing Random Forests for the Classification of Pudina Leaves through Feature Extraction with InceptionV3 and VGG19

Abstract

An analysis of the "Pudina Leaf Dataset: Freshness Analysis" reveals distinct classes of dried, fresh, and spoiled mint leaves. Convolutional neural networks, InceptionV3 and VGG19, were used to extract features from the dataset using advanced image processing techniques. The classification task was then performed using a Random Forest machine learning algorithm. In this study, notable results were obtained, proving the effectiveness of the selected methodologies. Mint (Pudina) leaves were classified accurately using InceptionV3-extracted features at 94.8%, demonstrating robust performance in distinguishing freshness states. This deep learning architecture was further shown to be able to capture meaningful patterns within the dataset by utilizing VGG19-extracted features, resulting in an improved accuracy of 96.8%.

Plant leaf classification and freshness analysis can be improved by integrating deep learning architectures with ensemble learning methods, as demonstrated in this study. In addition to demonstrating the suitability of the selected methodologies, these accuracies also provide avenues for further research and refinement with regard to plant leaf quality assessment and analysis.