<![CDATA[In many agricultural settings, particularly in developing regions, access to large, high-quality labeled datasets remains a persistent constraint. This study examines whether reliable crop-disease classification can be obtained when data are scarce, and whether the choice of learning algorithm—rather than the volume of data—is the dominant factor governing accuracy. Using a controlled simulation of 150 samples described by three environmental parameters (temperature, humidity, and soil pH) across eight leaf-disease classes, we train and evaluate five classifiers: K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Support Vector Machine (SVM), and Logistic Regression. Each class is generated from characteristic, overlapping environmental ranges so that a learnable but non-trivial signal exists. Models are assessed on a stratified 30% hold-out set using accuracy, macro precision, recall, and F1-score, with Principal Component Analysis (PCA) used to visualize decision boundaries. At a fixed and small sample size, accuracy varied widely across models (from 51.11% for the Decision Tree to 68.89% for Logistic Regression), all substantially above the 12.50% random baseline for eight balanced classes. Among the three tree-family and instance-based models that motivated the study, Random Forest was the strongest (64.44%). These results support the central premise that, in low-resource conditions, principled model selection contributes more to predictive performance than simply enlarging the dataset.]]>
Copyrights © 2026
