AI Deep Driven Framework for Automated Identification and Classification of Plant Species from Leaf Images Using Transfer Learning Techniques
TL;DR
An AI Deep Driven framework for automated identification and classification of plant species from leaf images using transfer learning techniques that combines image acquisition, preprocessing, leaf-region enhancement, data augmentation, transfer learning, fine-tuning, confidence estimation, and class-wise evaluation in a single pipeline is proposed.
Abstract
Accurate plant species identification is important for biodiversity documentation, agriculture, forestry, ecological monitoring, and educational applications. Conventional identification commonly depends on expert knowledge and manual comparison of leaf morphology, which can be slow and difficult when species have similar visual characteristics or when images are captured under uncontrolled field conditions. This paper proposes an AI Deep Driven framework for automated identification and classification of plant species from leaf images using transfer learning techniques. The framework combines image acquisition, preprocessing, leaf-region enhancement, data augmentation, transfer learning, fine-tuning, confidence estimation, and class-wise evaluation in a single pipeline. Pre-trained convolutional neural network architectures such as ResNet50, EfficientNet, and MobileNet are considered as feature-learning backbones, with a task-specific classification head adapted to the target plant-species classes. The use of transfer learning reduces the need to train a deep network entirely from scratch and enables reuse of general visual representations learned from large image collections. The proposed methodology is designed to address variations in illumination, scale, orientation, background, blur, and intra-species leaf appearance. LeafSnap is considered as a suitable benchmark because it contains laboratory and field images covering 185 tree species, including 23,147 laboratory images and 7,719 field images. The framework evaluates performance using accuracy, precision, recall, macro F1-score, confusion matrix, and inference time. This paper presents the architecture, methodology, evaluation protocol, limitations, and future scope of the proposed system. Numerical experimental results are intentionally not fabricated; they should be populated after the proposed models are trained and tested on the selected dataset.