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

Wieme, Jana; Mollazade, Kaveh; Malounas, Ioannis; Zude-Sasse, Manuela; Zhao, Ming; Gowen, Aoife; Argyropoulos, Dimitrios; Fountas, Spyros; Beek, Jonathan Van

doi:10.1016/j.biosystemseng.2022.07.013

Cited by 82 publications

(38 citation statements)

References 109 publications

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“…One of the most important aspects of the egg industry is the use of comprehensive quality analysis methods to guarantee sustainable marketing and consumption of eggs and egg products. Unfortunately, egg quality analysis methods require expert professionals to do a substantial number of tasks manually, which is time‐consuming, tedious, expensive, susceptible to human error, and, most importantly, often destructive (Wieme et al., 2022). Therefore, extremely accurate, fast, and non‐contact technologies are needed to autonomously inspect and separate undesirable eggs.…”

Section: An Overview Of I40 Technologiesmentioning

confidence: 99%

“…Recently, HSI has become industrially known for its use in process monitoring, product quality control, and raw material analysis (Kamruzzaman, 2023). It is one of the efficient optical methods used to assess the quality of various products, including meat (Kamruzzaman, 2023), poultry (Li et al., 2023; Yoon et al., 2011), fish (Cheng & Sun, 2014; Qin et al., 2020), and fruits and vegetables (Çetin et al., 2022; Tung et al., 2018; Wieme et al., 2022). Several research projects on egg characterization using HSI technology have been done.…”

Section: Application Of I40 Technologies In the Egg Industrymentioning

confidence: 99%

“…The MV and intelligent automation have enabled the meat and egg processing sector to increase the rate of processing and packing (Chowdhury & Morey, 2020; Innocente et al., 2009; Kamruzzaman, 2023). Spectral imaging devices driven by AI have been successfully shown to increase in multiple studies in terms of increased efficiency, improved quality control, reduced costs, and enhanced safety (Debauche et al., 2020; Puertas & Vázquez, 2020; Singh et al., 2020; Soltani et al., 2015; Wieme et al., 2022).…”

Section: Application Of I40 Technologies In the Egg Industrymentioning

confidence: 99%

“…Spectral imaging devices driven by AI have been successfully shown to increase in multiple studies in terms of increased efficiency, improved F I G U R E 7 An artificial intelligence-based cloud computing system for the smart egg industry: transfer of sensing data into a data center-machine learning of data and transfer to the consumer using an app server-end-user quality check to help with purchasing decision. quality control, reduced costs, and enhanced safety (Debauche et al, 2020;Puertas & Vázquez, 2020;Singh et al, 2020;Wieme et al, 2022).…”

Section: Ai Iot and Cloud Computingmentioning

confidence: 99%

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Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Ahmed,

Hossainy,

Khaliduzzaman

et al. 2023

Comp Rev Food Sci Food Safe

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The egg is considered one of the best sources of dietary protein, and has an important role in human growth and development. With the increase in the world's population, per capita egg consumption is also increasing. Ground‐breaking technological developments have led to numerous inventions like the Internet of Things (IoT), various optical sensors, robotics, artificial intelligence (AI), big data, and cloud computing, transforming the conventional industry into a smart and sustainable egg industry, also known as Egg Industry 4.0 (EI 4.0). The EI 4.0 concept has the potential to improve automation, enhance biosecurity, promote the safeguarding of animal welfare, increase intelligent grading and quality inspection, and increase efficiency. For a sustainable Industry 4.0 transformation, it is important to analyze available technologies, the latest research, existing limitations, and prospects. This review examines the existing non‐destructive optical sensing technologies for the egg industry. It provides information and insights on the different components of EI 4.0, including emerging EI 4.0 technologies for egg production, quality inspection, and grading. Furthermore, drawbacks of current EI 4.0 technologies, potential workarounds, and future trends were critically analyzed. This review can help policymakers, industrialists, and academicians to better understand the integration of non‐destructive technologies and automation. This integration has the potential to increase productivity, improve quality control, and optimize resource management toward sustainable development of the egg industry.

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Section: An Overview Of I40 Technologiesmentioning

confidence: 99%

Section: Application Of I40 Technologies In the Egg Industrymentioning

confidence: 99%

Section: Application Of I40 Technologies In the Egg Industrymentioning

confidence: 99%

Section: Ai Iot and Cloud Computingmentioning

confidence: 99%

See 2 more Smart Citations

Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Ahmed,

Hossainy,

Khaliduzzaman

et al. 2023

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

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“…Even the greater the number of bands, and consequently the smaller their width, greater is the ability to identify specific components or elements of a culture based on its reflectance characteristics. Using hyperspectral technology, images can be collected in a wavelength range of 400-2500 nm with variable spectral resolution, expressed in nm, and high spatial resolution [63]. The calibration of the hyperspectral sensor is made by detecting the reflective curves of a Spectralon ® panel through a spectrometer, which is considered a Lambertian reflector.…”

Section: Technologies and Sensors For Vineyard Monitoringmentioning

confidence: 99%

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

Ferro

Catania

2023

Horticulturae

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The potential of precision viticulture has been highlighted since the first studies performed in the context of viticulture, but especially in the last decade there have been excellent results have been achieved in terms of innovation and simple application. The deployment of new sensors for vineyard monitoring is set to increase in the coming years, enabling large amounts of information to be obtained. However, the large number of sensors developed and the great amount of data that can be collected are not always easy to manage, as it requires cross-sectoral expertise. The preliminary section of the review presents the scenario of precision viticulture, highlighting its potential and possible applications. This review illustrates the types of sensors and their operating principles. Remote platforms such as satellites, unmanned aerial vehicles (UAV) and proximal platforms are also presented. Some supervised and unsupervised algorithms used for object-based image segmentation and classification (OBIA) are then discussed, as well as a description of some vegetation indices (VI) used in viticulture. Photogrammetric algorithms for 3D canopy modelling using dense point clouds are illustrated. Finally, some machine learning and deep learning algorithms are illustrated for processing and interpreting big data to understand the vineyard agronomic and physiological status. This review shows that to perform accurate vineyard surveys and evaluations, it is important to select the appropriate sensor or platform, so the algorithms used in post-processing depend on the type of data collected. Several aspects discussed are fundamental to the understanding and implementation of vineyard variability monitoring techniques. However, it is evident that in the future, artificial intelligence and new equipment will become increasingly relevant for the detection and management of spatial variability through an autonomous approach.

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A short note on deep contextual spatial and spectral information fusion for hyperspectral image processing: Case of pork belly properties prediction

Mishra,

Albano‐Gaglio,

Font‐i‐Furnols

2024

Journal of Chemometrics

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This study demonstrates a new approach to process hyperspectral images where both the contextual spatial information as well as the spectral information are used to predict sample properties. The deep contextual spatial information is extracted using the deep feature extraction from pretrained resnet‐18 deep learning architecture, while the spectral information was readily available as the average pixel values. To fuse the information in a complementary way, a multiblock modeling approach called sequential orthogonalized partial least squares was used. The sequential model guarantees that the information learned is complementary from spatial and spectral domains. The potential of the approach is demonstrated to predict several physical and chemical properties in pork bellies.

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

Cited by 82 publications

References 109 publications

Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

A short note on deep contextual spatial and spectral information fusion for hyperspectral image processing: Case of pork belly properties prediction

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