Abstract:
A consistent and recurring problem in the agricultural sector is the bruising of fruits, which
leads to many consumers basing their perception on the quality of fruit by the appearance of it.
While external bruises on fruits are detectable, internal bruising is hard to detect using the
available methods and traditional physical assessment methods, as most of these happen during
the harvesting, transportation and storage of fruits. Undetected and internal damage leads to
losses due to wastage, reduced shelf-life and other economic costs. Current industrial-grade
detection methods are expensive and complicated: for example, X-rays and hyper-spectral
imaging are unavailable to the general public. The issue of finding hidden injuries in Guava
fruits at the point of sale needs a workable, reasonably priced, user-friendly solution. This
presents a method for the detection of bruise in guava fruits using machine learning and thermal
imaging. The study employed the YOLOv8 deep learning model for detecting bruises in guava
fruits and classifying the thermal images. Thermal images derived from a publicly available
dataset were preprocessed and used to train the model. The model showed 90% accuracy, which
was depicted through a confusion matrix. Evaluation and testing of the model was carried out
with tests datasets.
