A COMPARATIVE ANALYSIS OF TRADITIONAL MACHINE LEARNING AND DEEP LEARNING FOR EARLY DISEASE DETECTION FROM LEAF TEXTURES
The imperative for early and accurate detection of plant leaf diseases is paramount in safeguarding global food security and advancing precision agricultural practices. This review paper conducts a systematic comparative analysis of two predominant computational paradigms: Traditional Machine Learning (TML) and Deep Learning (DL). TML methodologies hinge on the extraction of hand-crafted features, such as Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix (GLCM), which are subsequently classified using algorithms including Support Vector Machine (SVM), Random Forest (RF), and XGBoost. In contrast, DL models, particularly Convolutional Neural Networks (CNNs), automate the feature extraction and classification process in an end-to-end manner. This paper synthesizes empirical evidence from contemporary literature (2019-2024) to evaluate these approaches across critical dimensions of performance accuracy, data efficiency, computational resource requirements, and model interpretability. The analysis substantiates that while DL models generally achieve superior accuracy (often exceeding 95%) with ample data, TML methods demonstrate remarkable robustness, computational economy, and comparable efficacy (85-93% accuracy) in scenarios characterized by limited training data. The conclusion delineates the specific contexts in which each paradigm is most advantageous, providing a decisive guide for researchers and practitioners. Furthermore, the paper proposes future research directions involving hybrid models that amalgamate the strengths of both TML and DL for optimal performance in diverse agricultural settings.