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

Abstract

BACKGROUNDTo 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.RESULTSVGGNet‐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.CONCLUSIONThe 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.