Deep Learning and Image Processing for Efficient Herb Classification

This paper presents a clever way to deal with herb classification using Convolutional Neural Networks (CNNs). We fostered a CNN model prepared on an enormous dataset of spice pictures, fit for characterizing new pictures into one of the 70 herb classes in the dataset. The model’s exhibition was assessed utilizing test precision, model exactness, and model misfortune. The Receiver Operating Characteristic (ROC) and Precision-Recall curves were likewise determined to give a thorough assessment of the model’s exhibition. The outcomes exhibit the viability of the proposed technique in precisely ordering herb pictures, offering a promising methodology for future exploration in this field.