Potatoes, often referred to as "earth apples," are globally cultivated crops known for their high vitamin C content, containing three times more vitamin C than apples, along with rich potassium and carbohydrates. While potatoes thrive in cold and harsh environments, they are susceptible to heat stress. Alarmingly, the International Potato Center predicts that ongoing global warming could lead to a significant decline of up to 68% in potato production by 2060. The primary goal of this research is to predict the Crop Water Stress Index (CWSI) in both the temperature gradient and conventional greenhouses and to classify stress conditions with transfer learning
The objective of this study is to measure the growth status of garlic crops in open-field using VIS/NIR hyperspectral imaging system. A hyperspectral imaging system capable of acquiring a wavelength of 400 nm to 1000 nm was used, and the hyperspectral image data were analyzed by PLSR (Partial Least Square Regression), LS-SVM (Least Square Support Vector Machine), CNN (Convolutional Neural). Networks) and Spatial-Spectral Residual network (SSRN). The optimal model was able to classify the difference by fertilization levels with an accuracy of 80 to 99%, and the difference by soil covering with an accuracy of 93-99. These results show that the Vis/NIR hyperspectral imaging system and data can be utilized to predict the growth status of garlic.
Despite the complexity of the factors that lead to loss of seed viability, conventional methods like germination tests, tetrazolium tests are commonly employed to determine it. However, these methods have downsides like being destructive, time consuming and non-representative. Therefore, there is a need to develop a fast, non-destructive and real-time measurement and sorting system of seeds based on viability for industrial purpose. In this study, we seek to utilize HSI and multivariate data analysis techniques to classify viable seeds from non-viable ones and later use it basis to develop an online real-time detection system for sorting these seeds based on viability. For this cause, Data from melon and watermelon seeds were collected using a SWIR HSI system. The performance of the classification models achieved both during calibration and real-time tests were quite impressive and a proof that HSI can be effectively applied to an industrial real-time sorting system.
Although many studies have been conducted to detect melamine in milk powder using near-infrared hyperspectral imaging system, the reproducibility due to moisture content in powder sample and detection limit have not been addressed appropriately. The objective of this study is to develop, based on shortwave infrared (SWIR) hyperspectral imaging, optimal model which is less sensitive to change of moisture content in sample powder. The hyperspectral imaging system consists of a MCT-based camera capable of measuring wavelengths from 1000nm to 2500nm. A halogen-based light source module was used to illuminated samples. The results showed a mixture concentration as low as 50 ppm of melamine in milk could be detected. The detection accuracy using the wavelength region from 1700nm to 2500nm was higher than that of using the wavelength from 1000nm to 1700nm. The MCT-based SWIR hyperspectral imaging system has a good potential for the detection and quantification of adulterants in powder sample.
Low temperature environment affects the growth and yield of watermelon negatively. The conventional visual inspection with human eyes has a limitation for accurate phenotyping of the stress symptom. Spectral imaging technique has been used as a useful phenotyping tool for visualizing physiological responses of plants. In this study, responses of chilling stresses of watermelon leaves were investigated using Vis/NIR hyperspectral imaging (HSI) technique. Sensitive and resistant to chilling tolerance of watermelon plants were exposed to low temperature conditions. HSI of the treated leaves were collected and analyzed with multivariate analysis. The result shows that HSI technique could distinguish between susceptible and resistant plants.
Rice blast (Magnaporthe oryzae) is one of the most devastating rice diseases affecting yield and grain quality. Applications of Fourier-Transform Infrared (FTIR) spectroscopy to detect plant stress responses to pathogens have been widely investigated; however, assessing the difference in basal resistance (PAMP-triggered immunity; compatible reaction) vs. effector-triggered immunity (incompatible reaction) has remained largely elusive. Here, we inoculated 2-week old rice seedlings (varieties Dee Gee Woo Gen and Lemont) with rice blast isolates IC17 (compatible isolate) and IB54 (incompatible isolate). Leaf tissues were collected at 6, 24, 48, 72 hours, and 7 days after inoculation, and then the constituents were extracted with dimethyl sulfoxide for FTIR analysis. Overall, distinctive profiles of compatible and incompatible reactions compared to the controls were observed. The preliminary result suggests a potential application of FTIR spectroscopy as a means for discriminating the basal resistance and effector-triggered immunity.
The objective of this study was to predict the moisture content, soluble solids content, and titratable acidity content in bell peppers during storage, based on hyperspectral imaging (HSI) in the 1000–1500 nm wavelength range. The mean spectra of 148 mature bell peppers were extracted from the hyperspectral images, and multivariate calibration models were built using partial least squares regression to predict MC, SSC, and TA content in bell peppers with different preprocessing techniques. The selected optimum wavelengths were used to create distribution maps for MC, SSC, and TA content of bell peppers. The results revealed that HSI coupled with multivariate analysis can be used successfully to predict the MC, SSC, and TA content in bell peppers.
Total volatile basic nitrogen (TVB-N) content is one of the important factors to measure the quality of meat. However, conventional chemical analysis methods for measuring TVB-N contents are time-consuming and labor-intensive, and are destructive procedures. The objective of this study is to investigate the possibility of fluorescence hyperspectral imaging techniques for determination of total volatile basic nitrogen (TVB-N) in beef meat. High intensity LED lights at 365 nm and 405 nm were used as the excitation for acquiring fluorescence images. Prediction algorithms based on simple band-ratio, partial least square discriminant analysis (PLS-DA) have been developed. This study shows that fluorescence hyperspectral imaging system has a good potential for rapid measurement of TVB-N content in meat.
Fishes are a widely used food material in the world. Recently about 4% of the fishes are infected with Kudoa thyrsites in Asian ocean. Kudoa thyrsites is a parasite that is found within the muscle fibers of fishes. The infected fishes can be a reason of food poisoning, which should be sorted out before distribution and consumption. Although Kudoa thyrsites is visible to the naked eye, it could be easily overlooked due to the micro-scale size and similar color with fish tissue. In addition, the visual inspection is labor intensive works resulting in loss of money and time. In this study, a portable microscopic camera was utilized to obtain images of raw fish slices. The optimized image processing techniques with polarized transmittance images provided reliable performance. The result shows that the portable microscopic imaging method can be used to detect parasites rapidly and non-destructively, which could be an alternative to manual inspections.
Bacterial biofilm formed by pathogens on fresh produce surfaces is a food safety concern because the complex extracellular matrix in the biofilm structure reduces the reduction and removal efficacies of washing and sanitizing processes such as chemical or irradiation treatments. Therefore, a rapid and nondestructive method to identify pathogenic biofilm on produce surfaces is needed to ensure safe consumption of fresh, raw produce. This research aimed to evaluate the feasibility of hyperspectral fluorescence imaging for detecting Escherichia.coli (ATCC 25922) biofilms on baby spinach leaf surfaces. Samples of baby spinach leaves were immersed and inoculated with five different levels (from 2.6x104 to 2.6x108 CFU/mL) of E.coli and stored at 4°C for 24 h and 48 h to induce biofilm formation. Following the two treatment days, individual leaves were gently washed to remove excess liquid inoculums from the leaf surfaces and imaged with a hyperspectral fluorescence imaging system equipped with UV-A (365 nm) and violet (405 nm) excitation sources to evaluate a spectral-image-based method for biofilm detection. The imaging results with the UV-A excitation showed that leaves even at early stages of biofilm formations could be differentiated from the control leaf surfaces. This preliminary investigation demonstrated the potential of fluorescence imaging techniques for detection of biofilms on leafy green surfaces.
Meat and bone meal (MBM) has been banned as animal feed for ruminants since 2001 because it is the source of bovine spongiform encephalopathy (BSE). Moreover, many countries have banned the use of MBM as animal feed for not only ruminants but other farm animals as well, to prevent potential outbreak of BSE. Recently, the EU has introduced use of some MBM in feeds for different animal species, such as poultry MBM for swine feed and pork MBM for poultry feed, for economic reasons. In order to authenticate the MBM species origin, species-specific MBM identification methods are needed. Various spectroscopic and spectral imaging techniques have allowed rapid and non-destructive quality assessments of foods and animal feeds. The objective of this study was to develop rapid and accurate methods to differentiate pork MBM from poultry MBM using short-wave infrared (SWIR) hyperspectral imaging techniques. Results from a preliminary investigation of hyperspectral imaging for assessing pork and poultry MBM characteristics and quantitative analysis of poultry-pork MBM mixtures are presented in this paper.
Current meat inspection in slaughter plants, for food safety and quality attributes including potential fecal contamination, is conducted through by visual examination human inspectors. A handheld fluorescence-based imaging device (HFID) was developed to be an assistive tool for human inspectors by highlighting contaminated food and food contact surfaces on a display monitor. It can be used under ambient lighting conditions in food processing plants. Critical components of the imaging device includes four 405-nm 10-W LEDs for fluorescence excitation, a charge-coupled device (CCD) camera, optical filter (670 nm used for this study), and Wi-Fi transmitter for broadcasting real-time video/images to monitoring devices such as smartphone and tablet. This study aimed to investigate the effectiveness of HFID in enhancing visual detection of fecal contamination on red meat, fat, and bone surfaces of beef under varying ambient luminous intensities (0, 10, 30, 50 and 70 foot-candles). Overall, diluted feces on fat, red meat and bone areas of beef surfaces were detectable in the 670-nm single-band fluorescence images when using the HFID under 0 to 50 foot-candle ambient lighting.
An imaging device to detect fecal contamination in fresh produce fields could allow the producer avoid harvesting fecal contaminated produce. E.coli O157:H7 outbreaks have been associated with fecal contaminated leafy greens. In this study, in-field spectral profiles of bovine fecal matter, soil, and spinach leaves are compared. A common aperture imager designed with two identical monochromatic cameras, a beam splitter, and optical filters was used to simultaneously capture two-spectral images of leaves contaminated with both fecal matter and soil. The optical filters where 10 nm full width half maximum bandpass filters, one at 690 nm and the second at 710 nm. These were mounted in front of the object lenses. New images were created using the ratio of these two spectral images on a pixel by pixel basis. Image analysis results showed that the fecal matter contamination could be distinguished from soil and leaf on the ratio images. The use of this technology has potential to allow detection of fecal contamination in produce fields which can be a source of foodbourne illnesses. It has the added benefit of mitigating cross-contamination during harvesting and processing.
The Cucumber Green Mottle Mosaic Virus (CGMMV) is a globally distributed plant virus. CGMMV-infected plants exhibit severe mosaic symptoms, discoloration, and deformation. Therefore, rapid and early detection of CGMMV infected seeds is very important for preventing disease damage and yield losses. Raman spectroscopy was investigated in this study as a potential tool for rapid, accurate, and nondestructive detection of infected seeds. Raman spectra of healthy and infected seeds were acquired in the 400 cm-1 to 1800 cm-1 wavenumber range and an algorithm based on partial least-squares discriminant analysis was developed to classify infected and healthy seeds. The classification model’s accuracies for calibration and prediction data sets were 100% and 86%, respectively. Results showed that the Raman spectroscopic technique has good potential for nondestructive detection of virus-infected seeds.
Hyperspectral imaging has been shown to be a powerful tool for nondestructive evaluation of biological samples. We recently developed a new line-scan-based shortwave infrared (SWIR) hyperspectral imaging system. Critical sensing components of the system include a SWIR spectrograph, an MCT (HgCdTe) array detector, and a custom-designed illumination source. The system has an effective imaging range from 900 nm to 2500 nm. In this paper, we present SWIR hyperspectral images of plant leaves and fruits, and preliminary SWIR image analysis results.
To achieve comprehensive online quality and safety inspection of fruits, whole-surface sample presentation and imaging regimes must be considered. Specifically, sample presentation method for round objects is under development to achieve effective whole-surface sample evaluation based on the use of a single hyperspectral line-scan imaging device. In this paper, a whole-surface round-object imaging method using hyperspectral line-scan imaging techniques is presented.
Bruise damage on pears is one of the most crucial internal quality factors that needs to be detected in postharvest quality
sorting processes. Development of sensitive detection methods for the defects including fruit bruise is necessary to
ensure accurate quality assessment. Infra-red imaging techniques in the 1000 nm to 1700 nm has good potentials for
identifying and detecting bruises since bruises result in the rupture of internal cell walls due to defects on agricultural
materials. In this study, feasibility of hyperspectral infra-red (1000 - 1700 nm) imaging technique for the detection of
bruise damages underneath the pear skin was investigated. Pear bruises, affecting the quality of fruits underneath the
skin, are not easily discernable by using conventional imaging technique in the visible wavelength ranges. Simple image
combination methods as well as multivariate image analyses were explored to develop optimal image analysis algorithm
to detect bruise damages of pear. Results demonstrated good potential of the infra-red imaging techniques for detection
of bruises damages on pears.
Cuticle cracks on tomatoes are potential sites of pathogenic infection that may cause deleterious consequences both to
consumer health and to fresh and fresh-cut produce markets. The feasibility of hyperspectral near-infrared imaging
technique in the spectral range of 1000 nm to 1700 nm was investigated for detecting defects on tomatoes. Spectral
information obtained from the regions of interest on both defect areas and sound areas were analyzed to determine some
an optimal waveband ratio that could be used for further image processing to discriminate defect areas from the sound
tomato surfaces. Unsupervised multivariate analysis method, such as principal component analysis, was also explored to
improve detection accuracy. Threshold values for the optimized features were determined using linear discriminant
analysis. Results showed that tomatoes with defects could be differentiated from the sound ones, with an overall
accuracy of 94.4%. The spectral wavebands and image processing algorithms determined in this study could be used for
multispectral inspection of defects tomatoes.
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