Muskmelon Maturity Stage Classification Model Based on CNN

Abstract

How to quickly and accurately judge the maturity of muskmelon is very important to consumers and muskmelon sorting staff. This paper presents a novel approach to solve the difficulty of muskmelon maturity stage classification in greenhouse and other complex environments. The color characteristics of muskmelon were used as the main feature of maturity discrimination. A modified 29-layer ResNet was applied with the proposed two-way data augmentation methods for the maturity stages of muskmelon classification using indoor and outdoor datasets to create a robust classification model that can generalize better. The results showed that code data augmentation which is the first way caused more performance degradation than input image augmentation - the second way. This established the effectiveness of the code data augmentation compared to image augmentation. Nevertheless, the two-way data augmentations including the combination of outdoor and indoor datasets to create a classification model revealed an excellent performance of F1 score ∼99%, and hence the model is applicable to computer-based platform for quick muskmelon stages of maturity classification.