Image Embedding and Classification using Pre-Trained Deep Learning Architectures

Tea Leaf Disease Classification Using Artificial Intelligence (AI) Models

“…This fact was also proven by high precision, recall, and F1 score values of the SqueeseNet model which is mostly similar to our study results. Moreover, from the results of our study, SqueezeNet, Inception V3, Painters, VGG-16, and DeepLoc are the ascending order of AI models aligned according to the precision value obtained from each model, which the mostly similar pattern was obtained from the study conducted by Tiwari et al (2022).…”

“…The overall performance of the model was assessed using the F1 score, which is an evaluation metric that considers both precision and recall. Therefore it is important to select a model that shows the highest F1, recall, and precision value (Tiwari et al, 2022). From the overall results, the SqueezeNet (local) model was selected as the optimal AI model for tea disease detection using tea leaf images owing to its high precision, F1, and recall values.…”

“…Google's Inception v3 model was trained using ImageNet. Information in the model can recognize 1000 classes in the ImageNet (Tiwari et al, 2022). Squeeznet model is a quick and small image recognition model built with ImageNet.…”

“…It was trained using 1,584 artists' works from 79,433 pictures. A neural network called DeepLoc was trained with 21,882 single-cell images, each of which was uniquely labeled as belonging to one of 15 localization compartments (Tiwari et al, 2022). However, neural network models may not have the same accuracy level.…”