• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.1-8
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• 2024

Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which makes it possible to simultaneously evaluate both physiological and morphological parameters. Among the physiological and morphological parameters are classifying healthy and diseased plants, assessing the severity of the disease, differentiating the types of pathogens, and identifying the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. Plant diseases cause significant economic losses in agriculture around the world as the symptoms of diseases usually appear when the plants are infected severely. Early detection, quantification, and identification of plant diseases are crucial for the targeted application of plant protection measures in crop production. Hence, this can be done by possible applications of hyperspectral sensors and platforms on different scales for disease diagnosis. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation, and identification of diseases, estimation of disease severity, and phenotyping of disease resistance of genotypes. This review provides a deeper understanding, of basic principles and implementation of hyperspectral sensors that can measure pathogen-induced changes in plant physiology. Hence, it brings together critically assessed reports and evaluations of researchers who have adopted the use of this application. This review concluded with an overview that hyperspectral sensors, as a non-invasive system of measurement can be adopted in early detection, identification, and possible solutions to farmers as it would empower prior intervention to help moderate against decrease in yield and/or total crop loss.

• Electronics and Telecommunications Trends
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• v.34 no.1
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• pp.86-97
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• 2019

Over the past 30 years, significant developments have been made in hyperspectral imaging (HSI) technologies that can provide end users with rich spectral, spatial, and temporal information. Owing to the advances in miniaturization, cost reduction, real-time processing, and analytical methods, HSI technologies have a wide range of applications from remote-sensing to healthcare, military, and the environment. In this study, we focus on the latest trends of HSI technologies, analytical methods, and their applications. In particular, improved machine learning techniques, such as deep learning, allows the full use of HSI technologies in classification, clustering, and spectral mixture algorithms. Finally, we describe the status of HSI technology development for skin diagnostics.

• 한국환경농학회:학술대회논문집
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• 2009.07a
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• pp.119-126
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• 2009

In recent years, research at the Environmental Microbial and Food Safety Laboratory (EMFSL), Agricultural Research Service (ARS) has focused on the development of novel image-based sensing technologies to address agro-food safety concerns, and transformation of these novel technologies into practical instrumentation for industrial implementations. The line-scan-based hyperspectral imaging techniques have often served as a research tool to develop rapid multispectral methods based on only a few spectral bands for rapid online applications. We developed a newer line-scan hyperspectral imaging platform for high-speed inspection on high-throughput processing lines, capable of simultaneous multiple inspection algorithms for different agro-food safety problems such as poultry carcass inspection for wholesomeness and apple inspection for fecal contamination and defect detection. In addition, portable imaging devices were developed for in situ identification of contamination sites and for use by agrofood producer and processor operations for cleaning and sanitation inspection of food processing surfaces. The aim of this presentation is to illustrate recent advances in the above agro.food safety sensing technologies.

• Journal of Biosystems Engineering
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• v.42 no.3
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• pp.199-216
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• 2017

Purpose: This study reviewed the major nondestructive measurement techniques used to assess internal properties of agricultural materials that significantly influence the quality, safety, and value of the products in markets. Methods: Imaging technologies are powerful nondestructive analytical tools that possess specific advantages in revealing the internal properties of products. Results: This review was exploring the application of various imaging techniques, specifically, hyperspectral imaging (HSI), magnetic resonance imaging (MRI), soft X-ray, X-ray computed tomography (XRI-CT), thermal imaging (TI), and ultrasound imaging (UI), to investigate the internal properties of agricultural commodities. Conclusions: The basic instruments used in these techniques are discussed in the initial part of the review. In the context of an investigation of the internal properties of agricultural products, including crops, fruits, vegetables, poultry, meat, fish, and seeds, various extant studies are examined to understand the potential of these imaging technologies. Future trends for these imaging techniques are also presented.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.177-186
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• 2023

Hyperspectral imaging technology is one of the most efficient and fast-growing technologies in recent years. Hyperspectral image (HSI) comprises contiguous spectral bands for every pixel that is used to detect the object with significant accuracy and details. HSI contains high dimensionality of spectral information which is not easy to classify every pixel. To confront the problem, we propose a novel RGB channel Assimilation for classification methods. The color features are extracted by using chromaticity computation. Additionally, this work discusses the classification of hyperspectral image based on Domain Transform Interpolated Convolution Filter (DTICF) and 3D-CNN with Bi-directional-Long Short Term Memory (Bi-LSTM). There are three steps for the proposed techniques: First, HSI data is converted to RGB images with spatial features. Before using the DTICF, the RGB images of HSI and patch of the input image from raw HSI are integrated. Afterward, the pair features of spectral and spatial are excerpted using DTICF from integrated HSI. Those obtained spatial and spectral features are finally given into the designed 3D-CNN with Bi-LSTM framework. In the second step, the excerpted color features are classified by 2D-CNN. The probabilistic classification map of 3D-CNN-Bi-LSTM, and 2D-CNN are fused. In the last step, additionally, Markov Random Field (MRF) is utilized for improving the fused probabilistic classification map efficiently. Based on the experimental results, two different hyperspectral images prove that novel RGB channel assimilation of DTICF-3D-CNN-Bi-LSTM approach is more important and provides good classification results compared to other classification approaches.

• Food Science of Animal Resources
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• v.43 no.6
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• pp.1150-1169
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• 2023

Edible insects are gaining popularity as a potential future food source because of their high protein content and efficient use of space. Black soldier fly larvae (BSFL) are noteworthy because they can be used as feed for various animals including reptiles, dogs, fish, chickens, and pigs. However, if the edible insect industry is to advance, we should use automation to reduce labor and increase production. Consequently, there is a growing demand for sensing technologies that can automate the evaluation of insect quality. This study used short-wave infrared (SWIR) hyperspectral imaging to predict the proximate composition of dried BSFL, including moisture, crude protein, crude fat, crude fiber, and crude ash content. The larvae were dried at various temperatures and times, and images were captured using an SWIR camera. A partial least-squares regression (PLSR) model was developed to predict the proximate content. The SWIR-based hyperspectral camera accurately predicted the proximate composition of BSFL from the best preprocessing model; moisture, crude protein, crude fat, crude fiber, and crude ash content were predicted with high accuracy, with R² values of 0.89 or more, and root mean square error of prediction values were within 2%. Among preprocessing methods, mean normalization and max normalization methods were effective in proximate prediction models. Therefore, SWIR-based hyperspectral cameras can be used to create automated quality management systems for BSFL.

• Journal of Animal Science and Technology
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• v.64 no.5
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• pp.813-829
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• 2022

Internal and external defects of eggs should be detected to prevent cross-contamination of intact eggs by abnormal eggs during storage. Emerging detection technologies for abnormal eggs were introduced as an alternative to human inspection. The advanced technologies could rapidly detect abnormal eggs. Abnormal egg detection technologies using acoustic response, machine vision, and spectroscopy have been commercialized in the poultry industry. Non-destructive egg quality assessment methods meanwhile could preserve the value of eggs and improve detection efficiency. In order to improve detection efficiency, it is essential to select a proper algorithm for classifying the types of abnormal eggs. This review deals with the performance of the detection technologies for various types of abnormal eggs in recently published resources. In addition, the discriminant methods and detection algorithms of abnormal eggs reported in the published literature were investigated. Although the majority of the studies were conducted on a laboratory scale, the developed detection technologies for internal and external defects in eggs were technically feasible to obtain the excellent detection accuracy. To apply the developed detection technologies to the poultry industry, it is necessary to achieve the detection rates required from the industry.

• Food Science of Animal Resources
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• v.41 no.4
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• pp.563-588
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• 2021

Increasing meat demand in terms of both quality and quantity in conjunction with feeding a growing population has resulted in regulatory agencies imposing stringent guidelines on meat quality and safety. Objective and accurate rapid non-destructive detection methods and evaluation techniques based on artificial intelligence have become the research hotspot in recent years and have been widely applied in the meat industry. Therefore, this review surveyed the key technologies of non-destructive detection for meat quality, mainly including ultrasonic technology, machine (computer) vision technology, near-infrared spectroscopy technology, hyperspectral technology, Raman spectra technology, and electronic nose/tongue. The technical characteristics and evaluation methods were compared and analyzed; the practical applications of non-destructive detection technologies in meat quality assessment were explored; and the current challenges and future research directions were discussed. The literature presented in this review clearly demonstrate that previous research on non-destructive technologies are of great significance to ensure consumers' urgent demand for high-quality meat by promoting automatic, real-time inspection and quality control in meat production. In the near future, with ever-growing application requirements and research developments, it is a trend to integrate such systems to provide effective solutions for various grain quality evaluation applications.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2380-2387
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• 2023

Hyperspectral data and its ability to explore the minerals and their associated rocks have a remarkable application in mineral exploration and lithological characterization. The present study aims to explore the radiation shielding aspects of the iron ore in Kerala with the aid of the Hyperion hyperspectral dataset. The reflectance-spectra obtained from the laboratory conditions as well as from the image show various absorptions. The results from the spectra are validated with geochemical data and GPS points. The Monte Carlo simulation employed to evaluate the radiation shielding ability. Raising the oxygen ions caused a noteworthy decrease in the µ values of the studied rocks which is accompanied by an increase in Δ_0.5 and Δ_eq values. The Δ_0.5 and Δ_eq values increased by factors of approximately 77 % with raising the oxygen ions between 44.32 and 47.57 wt.%. The µ values varies with the oxygen concentrations, where the µ values decreased from 2.531 to 0.925 cm^-1 (at 0.059 MeV), from 0.381to 0.215 cm^-1 (at 0.662 MeV), and from 0.279 to 0.158 cm^-1 (at 1.25 MeV) with raising the oxygen ions from 44.32 to 47.43 wt.%.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.107-119
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• 2019

As the development of ICT and integration technology, many changes and innovations in agriculture field are implemented. The agricultural sector has shifted from a traditional industry to a new industrial form called the 6th industry combined with various advanced technologies such as ICT and IT. Various approaches have been attempted to analyze and predict crops based on spatial information. In particular, a variety of research has been carried out recently for crop cultivation and smart farms using drones. The goal of this study was to establish an agricultural drought monitoring system using drones to produce scientific and objective indicators of drought. A soil moisture sensor was installed in the drought area and checked the actual soil moisture. The soil moisture data was used by the reference value to compare and analyze the temperature and NDVI established by drones. The soil temperature by the drone thermal image sensor and the NDVI by the drone hyperspectral was analyzed the correlation between crop condition and soil moisture in study area. To verify this, the actual soil moisture was calculated using the soil moisture measurement sensor installed in the target area and compared with the drone performance. This study using drone drought monitoring system may enhance to promote the crop data and to save time and economy.