CropGCNN: color space-based crop disease classification using group convolutional neural network.

ABSTRACT: Classifying images is one of the most important tasks in computer vision. Recently, the best performance for image classification tasks has been shown by networks that are both deep and well-connected. These days, most datasets are made up of a fixed number of color images. The input images are taken in red green blue (RGB) format and classified without any changes being made to the original. It is observed that color spaces (basically changing original RGB images) have a major impact on classification accuracy, and we delve into the significance of color spaces. Moreover, datasets with a highly variable number of classes, such as the PlantVillage dataset utilizing a model that incorporates numerous color spaces inside the same model, achieve great levels of accuracy, and different classes

SUBMITTER: Ahmad N 

PROVIDER: S-EPMC11322995 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature