Deep Learning for Plant Species Classification

Plant species classification is a fundamental task in botany, agriculture, and environmental science, providing valuable insights into biodiversity, ecological systems, and agricultural practices. Botanists and the general public could benefit from an automated system for quickly recognizing plant species. The protection of biodiversity necessitates a better understanding of plant species identification [1]. Traditional methods of plant species identification relying on hand-crafted features can be complex and challenging, especially for non-specialists who have trouble remembering specific botanical words. A potential method has emerged for automatic plant species identification utilizing machine learning and deep learning techniques. Deep learning is effective in extracting features because it excels at giving more detailed information about images. This paper presents the implementation of Convolutional Neural Networks (CNNs), a deep learning-based approach that extracts features from leaf images and facilitates the classification of plant species. Deep learning methods, particularly CNNs, have demonstrated superior performance compared to handcrafted feature-based approaches. The ability of CNNs to automatically learn discriminative features from images has greatly improved the accuracy and efficiency of plant species classification. By leveraging deep learning methods, the proposed approach extracts features directly from leaf images, eliminating the need for manual feature engineering. The CNN architecture captures intricate patterns and structures present in the leaf images, enabling robust classification of plant species. The application of CNNs in plant species classification has yielded significant advancements in the field, providing a reliable tool for both experts and non-experts in identifying plant species. The implementation of CNNs facilitates the extraction of features from leaf images, leading to improved accuracy and efficiency.