Deep Learning-Based Approaches for Automated Plant Leaf Detection and Classification

Agricultural, forestry, and environmental monitoring are just a few disciplines that rely heavily on automated plant leaf detection and classification. This study introduces a deep learning-based method for reliable and rapid leaf detection and classification in plants. The first step in the research was to compile a large database of plant leaf photos covering a wide range of species and environmental conditions. Several preprocessing methods are used to improve the quality of the leaf images. These methods include noise reduction, image segmentation, and normalization. The next step is building a deep learning architecture based on a convolutional neural network (CNN) to extract features from leaf photos. The effectiveness of various CNN architectures in detecting and classifying plant leaves is investigated. These architectures include AlexNet, VGGNet, and ResNet, among others. The Kaggle plant disease dataset trains a deep learning model optimized through transfer learning and data augmentation. Optimization of hyperparameters is performed to speed up convergence while maintaining high accuracy. The trained model is then tested on a dataset using metrics like accuracy 0.985, precision 0.936, recall 0.917, and F1 Score 0.987 to determine how well it performed. The usefulness of the suggested deep learning-based strategy is highlighted through comparisons with both existing methods and manual approaches.