Integrating deep learning with non-destructive thermal imaging for precision guava ripeness determination.

Abstract

To mitigate post-harvest losses and inform harvesting decisions at the same time as ensuring fruit quality, precise ripeness determination is essential. The complexity arises in assessing guava ripeness as a result of subtle alterations in some varieties during the ripening process, making visual assessment less reliable. The present study proposes a non-destructive method employing thermal imaging for guava ripeness assessment, involving obtaining thermal images of guava samples at different ripeness stages, followed by data pre-processing. Five deep learning models (AlexNet, Inception-v3, GoogLeNet, ResNet-50 and VGGNet-16) were applied, and their performances were systematically evaluated and compared. VGGNet-16 demonstrated outstanding performance, achieving average precision of 0.92, average sensitivity of 0.93, average specificity of 0.96, average F1-score of 0.92 and accuracy of 0.92 within a training duration of 484 s. The present study presents a scalable and non-destructive approach for guava ripeness determination, contributing to waste reduction and enhancing efficiency in supply chains and fruit production. These initiatives align with environmentally friendly practices in agriculture. © 2024 Society of Chemical Industry.