An introduction to artificial intelligence in machine vision for postharvest detection of disorders in horticultural products

Abstract

Horticultural products may suffer from surface, subsurface, and internal disorders. These lower the market value and may lead to consumer dissatisfaction. Today’s postharvest industry has a high interest in implementing technologies that allow efficient sorting of disordered products to avoid food loss, and to improve market value and consumer appreciation. Inspection by machine vision using RGB imaging and more advanced techniques such as spectral cameras, X-ray and magnetic resonance imaging (MRI), is increasingly implemented to non-destructively analyze disorders in horticultural products. However, for objective, robust and reliable detection of symptoms of disorders that are often even not obvious to detect by human vision, powerful data analysis methods are essential. In recent years, image analysis by artificial intelligence (AI) has evolved from traditional machine learning (ML) to advanced deep learning (DL), which is reaching impressive detection abilities. The aim of this article is to introduce recent developments in AI for extracting information on postharvest disorders from complex image data obtained by modern imaging systems to the non-AI expert. The main progress in AI for postharvest applications is the increasing adoption of available convolutional neural networks (CNNs), usually developed and tested with large open source image datasets elsewhere, to postharvest image data. Challenges in the design of DL models are identified, such as the need for large quantities of (labeled) data, the model explainability, and generalizability. Although proposed innovations often result in excellent detection abilities, their usability in an industrial context should be further considered, where uncontrollable parameters may negatively impact the performance.