Non-invasive Grading System for Banana Tiers using RGB Imaging and Deep Learning

What is it about?

A non-invasive classification of bananas is presented in this paper, which grades banana tiers into different categories using digital images of bananas applied with deep learning techniques. The main objective of this paper is to develop a tier-based grading system for clustered fruits such as bananas and classify them in terms of quality (export class, middle class, and reject class), maturity (green, turning yellow, yellow, and overripe), and size (small, medium, and large). The classification models for the different grading parameters are developed using transfer learning and a fine-tuned VGG16 Deep CNN architecture. The system was able to automate the assembly line-like process of classifying bananas with a satisfactory overall accuracy using only a minimal number of image samples.

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Photo by Giorgio Trovato on Unsplash

Photo by Giorgio Trovato on Unsplash

Why is it important?

With 85% to 99% accuracy on maturity estimation and discrimination of mechanical damage, the possible loss will be at least 1% to 15% only, which means that almost half of the percentage loss in the manual method will be reduced if the AI-based system will be used.

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