Produce Quality and Pesticide Residue Estimation Using Light Sensing
Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
While produce quality estimation across various stages in the value chain is essential to
tackle food loss and waste, determining pesticide residue in fresh produce can alleviate
the threat to human health and the environment. Light sensing offers a non-invasive
and cost-effective method to establish unique fingerprints for fresh produce. During a
12-day produce decomposition period, it was established that light reflectivity is effective
for the quality estimation of vegetables. The AdaBoost classification model with blue
light reflectivity value, vegetable items and luminosity as input features achieved a
performance accuracy of 92.4%. While measuring reflectivity intensity, it is important
to account for varying lighting conditions (luminosity). Notwithstanding the success of
predicting the quality of fresh produce, light sensing failed in pesticide residue estimation.
Description
Keywords
Light sensing, produce quality, pesticide residue, machine learning