DeepLeaf: Automated Leaves Classification Using Convolutional Neural Networks

Abstract

This paper presents a methodology for automated classification of leaves using Convolutional Neural Networks (CNNs). Leaf classification plays a crucial role in various domains such as agriculture, botany, and environmental science. Traditional methods for leaf classification often rely on manual feature extraction and handcrafted classifiers, which can be time-consuming and limited in their accuracy. In this work, we propose a deep learning approach that leverages the power of CNNs to automatically learn discriminative features from leaf images.

The proposed framework consists of several key stages: preprocessing, data augmentation, model architecture design, training, and evaluation. We preprocess the leaf images to enhance their quality and normalize their dimensions. Data augmentation techniques are applied to increase the diversity of the training dataset and improve the generalization capability of the model. The CNN architecture is carefully designed to effectively capture hierarchical features present in leaf images.

We train the CNN using a large dataset of labeled leaf images, employing techniques such as transfer learning to leverage pre-trained models and optimize training efficiency. The trained model is evaluated using various metrics such as accuracy, precision, recall, and F1 score on a separate test dataset. Experimental results demonstrate the effectiveness of the proposed approach in accurately classifying different types of leaves.

Overall, this study showcases the promising capabilities of deep learning techniques for automated leaf classification, paving the way for advanced applications in plant biology and agriculture.