Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

Abstract

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers’ expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects’ attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import–export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods’ application in the detection and classification of insect infestation in fruits and vegetables.

Potential of microbial endophytes to enhance the resistance to postharvest diseases of fruit and vegetables

“…Fruit and vegetables are an important source of nutrition for people and animals, and the consumption of them has increased significantly in recent years, 1 but postharvest diseases caused by pathogenic microorganisms will affect the quality, reduce shelf life, and then lead to huge economic losses. According to a 2015 report by the Food and Agriculture Organization of the United Nations, about 45% of the world's fruit and vegetables are wasted 2 .…”

A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning

“…Deep learning, an emerging non-destructive technique with advantages of automation, speed, accuracy and low cost, has been successfully applied in agriculture and food fields such as pest detection, disease diagnosis, ripeness determination and quality assessment of fruits, vegetables, meat and seafood 1 – 3 . In particular, deep convolutional neural networks (CNN) have become the dominant deep learning approach in image-based recognition, classification, and detection tasks for fruits and vegetables 4 – 6 .…”

Role of artificial intelligence in achieving global food security: a promising technology for future

“…When industries are keen on achieving this task, they have to face the challenge of insect infestations. The non-destructive, advanced technologies related to AI have been introduced to overcome this challenge (Adedeji et al, 2020). Visible light sensors at the wavelength range from 380 to 750 nm had used to detect external or surface features of fruits and vegetables .…”

“…While visible light sensors are applicable for surface detection of insect infestations, the Hyper Spectral Imaging system can be applied to accurate detection of hidden internal damage from insect infestations without sample destruction (Adedeji et al, 2020). For instance, this concept together with ML techniques has been applied to detect codling moth in apple (Rady et al, 2017), fruit fly in jujube fruits (Liu et al, 2016), and pod borer in soybean (Ma et al, 2014).…”