Advanced Leaf Classification Using Multi-Layer Perceptron for Smart Crop Management

Accurate leaf classification plays a vital role in ecological studies and precision agriculture, yet the diversity in leaf morphology and environmental influences presents a significant challenge. To improve accuracy, this study suggests a revolutionary methodology that combines sophisticated preprocessing, segmentation, feature extraction, optimization, and classification algorithms. To improve image quality, preprocessing is first done with bilateral filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE). Then, to ensure accurate border identification, Conditional Random Fields (CRF) are used for segmentation. To extract features, complex texture and shape details are captured using Local Ternary Patterns (LTP), KAZE, and Histogram of Oriented Gradients (HOG). The optimization method, Stochastic Gradient Descent (SGD), refines the feature space for better categorization. Finally, a Multi-Layer Perceptron (MLP) classifier is used, and it produces remarkable results on three different datasets: accuracy of 92.63%, 88.3%, and 94.5%, respectively. This strong architecture shows great promise for disease monitoring and plant identification, providing a flexible and scalable solution for a range of agricultural applications.