Digital assessment of post-harvest Nendran banana for faster grading: CNN-based ripeness classification model

Abstract

Banana (Musa spp.) is an extensively favored fruit owing to its affordability and considerable nutritional richness. Ensuring the quality of bananas is essential for meeting consumer expectations and international export standards. Post-harvest handling, particularly grading, plays a pivotal role in sorting and classifying bananas. It is crucial for quality assurance, aligning with consumer preferences, gaining market access, differentiating prices based on quality, minimizing waste, optimizing packaging efficiency and enhancing the overall effectiveness of the supply chain. The manual grading is accountable for a considerable range of post-harvest losses. The Convolutional Neural Network (CNN) is widely recognized as a state-of-the-art computer vision technique for classification tasks. In this investigation, a CNN-based deep learning approach is introduced for the ripening classification of the Nendran banana. This study focused on the development and evaluation of a CNN model using a dataset of 4320 images. Pre-existing Deep Learning (DL) models (VGG16, VGG19, InceptionV3, ResNet50 and EfficientNetB0) were employed for comparison, and the developed model achieved 95 % accuracy. A web application titled 'Banana Ripeness Identification App’, was developed using the proposed model. Notably, the proposed model outperformed existing DL models, emphasizing its superior classification accuracy. The study concluded that the 9-layer CNN model is highly effective and surpasses established DL architectures, which can serve as a foundation for the advancement of efficient classification methods for bananas based on their ripeness, consequently enhancing post-harvest management.