Non-Destructive Infield Quality Estimation of Strawberries using Deep Architectures
Cees Jol (Student TU Delft)
Junhan Wen (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Jan van Gemert (TU Delft - Electrical Engineering, Mathematics and Computer Science)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Strawberries are profitable fruits, yet they have a short shelf life. Therefore, it is crucial to anticipate their quality and harvest them at the best time, which is vital not only for finding the appropriate market but also for minimizing food and economic waste. To this end, non-destructive strawberry quality measurements are useful. Much research is conducted on post-harvest strawberries: the fruits were only analyzed after harvesting and thus, these methods cannot be used to find a good time to harvest. Our research targets pre-harvest analysis for supporting the timing decisions of harvests. As such, we used an infield image dataset that was collected during the cultivation of strawberries. The images are labeled by quality assessments and measurements from post-harvest destructive tests. We evaluated deep learning for quality estimation and trained our algorithms to predict the ripeness, firmness, and sweetness of strawberries. Additionally, we applied depth estimation algorithms and shape inpainting models to estimate the size of strawberries using images. Our results demonstrate the feasibility of infield quality attribute prediction.