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This research compares Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Stochastic Gradient Descent (SGD) algorithms for classifying corn leaf diseases using Histogram of Oriented Gradients (HOG) feature extraction. The study finds that the combination of HOG with the SGD algorithm yields the highest accuracy of 71.44%, with additional metrics including an AUC of 79.16% and precision of 70.08%. The automation of disease identification aims to improve efficiency over traditional manual methods, w

Author

wida25

Language

EN