Fruit Quality Assessment with Densely Connected Convolutional Neural Network

TL;DR

This paper presents a DenseNet-based deep learning approach for accurate fruit quality assessment, achieving near-perfect accuracy on a large dataset, demonstrating its potential for real-world agricultural applications.

Contribution

The study introduces a DenseNet architecture for fruit quality assessment, effectively addressing vanishing gradients and feature reuse, with high accuracy on a large dataset.

Findings

Achieved 99.67% accuracy on fruit quality classification

Demonstrated robustness in fruit classification and quality assessment tasks

Suitable for real-life agricultural applications

Abstract

Accurate recognition of food items along with quality assessment is of paramount importance in the agricultural industry. Such automated systems can speed up the wheel of the food processing sector and save tons of manual labor. In this connection, the recent advancement of Deep learning-based architectures has introduced a wide variety of solutions offering remarkable performance in several classification tasks. In this work, we have exploited the concept of Densely Connected Convolutional Neural Networks (DenseNets) for fruit quality assessment. The feature propagation towards the deeper layers has enabled the network to tackle the vanishing gradient problems and ensured the reuse of features to learn meaningful insights. Evaluating on a dataset of 19,526 images containing six fruits having three quality grades for each, the proposed pipeline achieved a remarkable accuracy of 99.67%. The robustness of the model was further tested for fruit classification and quality assessment tasks where the model produced a similar performance, which makes it suitable for real-life applications.