ResNet50-based Classification of Coffee Cherry Maturity using Deep-CNN
S Raveena, R Surendran
Abstract
up of coffee is a part of the daily human routine life. Any small changes in the coffee cherries yield that leads a huge change in coffee quality, smell, and taste. Types and stages of coffee cherries Identification and categorization of varieties and stages necessitate the presence of a farmer or crop pathologist. Manual illness diagnosis might result in mistaken identity and require a lengthy process. This work presents a comparison between the Deep Convolutional Neural Network-based approach and ResNet50 for classifying coffee type and maturity using transfer learning. Coffee cherries of different stages and varieties may be automatically sorted and recognized. Many ways to solve this problem have been proposed with transfer learning emerging as the dominant option due to its outstanding performance. This research work has implemented the Deep CNN Transfer Learning to detect distinct phases in the coffee cherries datasets. The suggested model employs VGG 16 for classifying coffee type and maturity got 96.38% accuracy and using ResNet50 for classifying coffee type and maturity with 99.01% accuracy. Experiments demonstrate that the suggested strategy is practical and that it would be used to detect efficiently and outperform other methodologies.