Application of hyperspectral imaging systems and artificial intelligence for quality assessment of fruit, vegetables and mushrooms: A review

Abstract In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.

Applications of machine learning techniques for enhancing nondestructive food quality and safety detection

Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images

Defect Detection in Fruit and Vegetables by Using Machine Vision Systems and Image Processing

Bruising is a kind of subcutaneous tissue injury that does not involve the rupture of fresh fruit epidermis. This damage can occur at any stage from picking, storage and transportation, which can result in fruit deterioration and rejection by the market or consumer. It is, however, very challenging to detect bruising due to no or few visual symptoms. A lot of studies have carried out to establish a feasible methodology to achieve rapid, non-destructive and accurate detection of bruised fruit in the commercial real-time grading line. Scope and approach. This review focuses solely on the non-destructive detection for fruit bruising, involving qualitative and quantitative analysis (e.g., degree, time and volume of bruises), and provides a comprehensive summary of the latest advances in 11 optical non-destructive sensing techniques, covering basic principles, system composition, methods/algorithms, applications, challenges and trends. Key findings and conclusions. Optical sensing technologies, especially imaging, are suitable for bruising detection of fruit. HSI can evaluate the bruises with different of degrees and time periods. Both MRI and X-ray CT imaging can reconstruct 3D fruit for the quantitative analysis of bruise volume and the research of bruise mechanism. SIRI shows impressive depth discrimination and enhanced imaging ability for subcutaneous slight bruises. The fusion technology is valuable, but it needs further exploration. The advanced detection algorithms are crucial to the successfully implementation of technology. In practice, MSI, FI, TI, BSI, SIRI and fusion technologies are worth recommending for online bruise detection. In addition, the study about the mechanism of bruising formation and the postharvest behavior of fruit can help to reduce the bruising incidence of fresh fruit after harvest. 

An overview on optical non-destructive detection of bruises in fruit: Technology, method, application, challenge and trend

Discrimination of common defects in loquat fruit cv. ‘Algerie’ using hyperspectral imaging and machine learning techniques

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Discrimination of common defects in loquat fruit cv. ‘Algerie’ using hyperspectral imaging and machine learning techniques