Towards a CNN-Based Method for Improving Long-Term Storability of Apples: A Socio-Technical Approach to Pre-Warehouse Fault Detection
Type
conference paper
Date Issued
2024
Author(s)
Abstract
Efficient post-harvest management of apple supply chains is critical to min-imize waste and preserve fruit quality, particularly in the storage phase. This study addresses the inefficiency and inaccuracy of defect and disease assessment in Controlled Atmosphere (CA) storage intake, where rapid and reliable evaluation of large quantities is imperative. Existing literature fo-cuses on machine learning applications in pre-harvest crop monitoring, but practical, post-harvest solutions remain underexplored. We propose the de-velopment of a Convolutional Neural Network (CNN) for detecting apple defects that seamlessly integrates into existing CA storage warehousing op-erations. Employing Action Design Research, this interdisciplinary study collaborates with a medium-sized fruit storage enterprise in Styria, Austria, iteratively refining a CNN model through an Action Design Research (ADR) team. This research not only aims to fill the gap between academic methodologies and practical application but also seeks to enhance opera-tional efficiency, demonstrating the real-world utility of advanced machine learning in agriculture.
Language
English
Event Title
ITAIS2024: XXI CONFERENCE OF THE ITALIAN CHAPTER OF AIS - GROWING IN A DIGITAL AND SUSTAINABLE SOCIETY
Event Location
Piacenza, Italy
Event Date
11-12.10.2024
Division(s)