Classifying Healthy and Defective Fruits with a Multi-Input Architecture and CNN Models
This addresses external quality inspection for fruits, but it is incremental as it combines existing CNN models with a known architecture on a specific dataset.
The study tackled fruit classification into healthy and defective states using a Multi-Input architecture with RGB and silhouette images, achieving 100% accuracy with the MobileNetV2 model.
This study presents an investigation into the utilization of a Multi-Input architecture for the classification of fruits (apples and mangoes) into healthy and defective states, employing both RGB and silhouette images. The primary aim is to enhance the accuracy of CNN models. The methodology encompasses image acquisition, preprocessing of datasets, training, and evaluation of two CNN models: MobileNetV2 and VGG16. Results reveal that the inclusion of silhouette images alongside the Multi-Input architecture yields models with superior performance compared to using only RGB images for fruit classification, whether healthy or defective. Specifically, optimal results were achieved using the MobileNetV2 model, achieving 100\% accuracy. This finding suggests the efficacy of this combined methodology in improving the precise classification of healthy or defective fruits, which could have significant implications for applications related to external quality inspection of fruits.