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Postharvest detection of anthracnose (Colletotrichum asianum) on mango fruit (Mangifera indica L. cv Namdokmai Sithong) using near-infrared response
(2026-12-01)
Junto, Apiwat
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Phanomsophon, Thitima
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Sharma, Sneha
;
Kaewsorn, Kannapot
;
Jongyingcharoen, Jiraporn Sripinyowanich
Anthracnose disease, caused by fungi of the genus Colletotrichum, poses a major threat to mango production and export industries, with Colletotrichum asianum being among the most significant pathogenic species. This work proposes the hypothesis that the simple difference in absorption between anthracnose-infected and noninfected mangoes illustrated by the average near-infrared (NIR) spectra obtained from hyperspectral images could be used for simple differentiation of the two groups. The method of depositing fungal spores by spraying the spores over the fruit surface, not a small area or specific point, allows for the number of spores per unit area to be harmonized and to detect infected or noninfected spores on every pixel of the mango surface using a hyperspectral imaging camera. Important wavelengths for differentiation included water bands of 970, 1190, and 1200 nm which resulted in the greatest difference in absorbance, and bands of chitin, the major component of the fungal cell wall; 1195 nm was the most important band. In addition, the vibration bands of 868 (protein in the fungal cell wall), 1134 (sugar and starch of the mango substrate), 1320 (NIR absorbers in the fungus-sprayed and mango substrate, not specifically defined) and 1069 nm (crystallinity and N-acetyl methyl groups in the fungal chitin and constituents of the mango), differed from each other. These wavelengths can be used for modelling, which can lead to high performance in quantifying the concentration of anthracnose and classifying the strength levels of anthracnose infection. The microbiological mechanism of anthracnose growth on infected mangoes corresponding to changes in the NIR spectrum during the 4 days after spore infection is comprehensively discussed. These results can aid in enhancing early detection and classification techniques for anthracnose-infected mangoes from noninfected mangoes using hyperspectral image sensors.
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Near-infrared hyperspectral and multispectral imaging principies and applications in the quality of fruits and vegetables
(2023-08-21)
Sharma, Sneha
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Sumesh, K. C.
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Shrestha, Bim Prasad
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Sirisomboon, Panmanas
In recent years, imaging technologies (hyperspectral and multispectral) are being widely investigated and applied as a non-destructive, reliable, and accurate technique to monitor the quality and composition of agricultural products. Over two decades, hyperspectral imaging (HSI) has developed as a promising technology for qualitative and quantitative analysis of fruits and vegetables. The ability to integrate spatial and spectral information in the form of a hypercube is one of the significant advantages of HSI over spectroscopy and other imaging technologies. It enables mapping of the spatial variability or distribution of those Parameters within the sample. Like HSI, multispectral imaging (MSI) is also gaining interest in real-time application in the grading and/or packaging of fruits and vegetables. Hundreds of images over a contiguous wavelength in HSI brings algorithmic processing complexities, whereas fewer wavelengths in multispectral imaging reduces algorithmic complexities and enables faster processing with reliable results. In summary, these imaging technologies are powerful and reliable techniques for the analysis of agricultural products. This chapter will focus on the theory and principles of near infrared HSI and MSI technologies, their components, the mode of image acquisition, and image processing techniques. Finally, the recent application of these imaging techniques for predicting physicochemical properties, antioxidants, chemical components, texture, defects, maturity classification, shape, and size of fruits and agricultural products are presented.
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Near-infrared spectroscopy, hyperspectral, multispectral imaging principles and applications in energy properties of biomass
(2023-08-21)
Posom, Jetsada
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Shrestra, Bijendra
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Maraphum, Kanvisit
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Pitak, Lakkana
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Saengprachatanarug, Khwantri
Biomass is renewable energy which is zero neutrality carbon energy. It is used for generating heat energy and electrical energy. Therefore, the use of biomass with high efficiency is important and the quality of biomass related to its energy should be measured before utilization and trading. The measurement of energy indexes of biomass is necessary to the thermal conversion process and the trading of biomass. However, the conventional measurement methods are laborious and take a long time, with a lot of costs. In recent years, near infrared spectroscopy (NIR) and imaging technologies (hyperspectral and multispectral images) have been widely investigated and applied as non-destructive, reliable and accurate techniques to monitor the quality and composition of biomass. This chapter contains the principle of NIR and imaging technique including essential component principles, NIR and imaging technique procedures, novel model development methods and applications. The non-destructive measurement of biomass quality as the real time and non- contact measurement will be represented. Moreover, this chapter will describe the application of NIR and imaging techniques for analysing the energy indexes of biomass, such as heating value or calorific value, proximate data, elemental composition, combustion index, pyrolysis characteristics, mechanical properties and so on.
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Near-infrared hyperspectral imaging combined with machine learning for physicochemical-based quality evaluation of durian pulp
(2023-06-01)
Sharma, Sneha
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Sirisomboon, Panmanas
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K.C, Sumesh
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Terdwongworakul, Anupun
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Phetpan, Kittisak
This research reports on the application of near-infrared hyperspectral imaging (NIR-HSI) system for predicting the physicochemical properties; dry matter (DM), total soluble solids (TSS), and fat content (FC) of durian. Partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and 1D convolution neural network (CNN) models: custom, U-Net, and VGG19; were developed to predict DM, TSS, and FC of durian pulp. Feature wavelengths were selected using a genetic algorithm (GA) and successive projection algorithm (SPA). The selected wavelengths were then validated based on the algorithms for regression model development. GA-PLSR model was compelling to predict the DM and FC in durian pulp, which obtained the coefficient of determination for the test set (r2) and root mean square error of prediction (RMSEP) of 0.97 and 1.12% for DM and 0.86 and 0.64% for FC, respectively. The GA-PLSR model provided the best result for the TSS prediction with r2, and RMSEP of 0.90 and 1.40%, respectively, whereas the SPA-PLSR model based on only thirteen wavelengths attained fair result with the r2 and RMSEP of 0.79 and 2.03%, respectively. The above results show that the pushbroom NIR-HSI system achieved promising results for estimating DM, TSS, and FC in durian pulp. This research identified the featured wavelengths that can be used to develop a portable and reliable HSI or multispectral system to be installed at durian packaging firms for quality inspection and grading.

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