Leaf Image Feature Classification and Recognition with MATLAB Implementation

Building upon MATLAB's capabilities for leaf image feature classification and recognition, researchers can conduct extensive studies in image analysis processing, segmentation, feature extraction, and classification tasks. For instance, various image segmentation algorithms like watershed transformation or region-based segmentation can be implemented using MATLAB's Image Processing Toolbox functions (such as watershed() or regionprops()) to enhance segmentation accuracy and stability. Additionally, developers can explore advanced feature extraction methodologies - including morphological features (using bwlabel() and regionprops() for area, perimeter calculations), texture features (employing gray-level co-occurrence matrix with graycoprops()), and color histogram analysis - to extract more distinctive leaf characteristics. Furthermore, multiple classification algorithms can be implemented through MATLAB's Classification Learner App or programming interfaces, including Support Vector Machines (using fitcsvm() with optimized kernel functions), Neural Networks (implemented with patternnet() or feedforwardnet()), and decision trees, all aimed at improving classification accuracy. Through these systematic explorations and algorithm optimizations, we can progressively refine and enhance MATLAB-based leaf image feature classification and recognition technologies for more robust botanical analysis applications.