Near-infrared hyperspectral imaging evaluation of Fusarium damage and DON in single wheat kernels

Femenias, Antoni; Llorens-Serentill, Enric; Ramos, Antonio J.; Sanchis, Vicente; Marı́n, Sonia

doi:10.1016/j.foodcont.2022.109239

Cited by 13 publications

(10 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hyperspectral images of wheat kernels and flour infected with three Fusarium species ( F. graminearum , F. culmorum , and F. poae ) in 400–1000 nm and 1000–2500 nm ranges were investigated to reveal the different correlation between spectra and this fungal DNA ( Alisaac et al, 2019 ). With HIT in the range of 400–1000 nm, 12 optimal wavelengths were selected and combined with corresponding images to identify Fusarium -damaged wheat kernels by random forest (RF) algorithm, generating an accuracy of classification up to 96.44 % ( Zhang et al, 2020 , Zhang et al, 2020 ), almost the same as that of study performed by Delwiche et al (2019) , higher than that of other three studies (classification accuracy < 90 %) ( Nadimi et al, 2021 , Femenias et al, 2021a , Femenias et al, 2022 ), similar good as that by Ropelewska & Zapotoczny (2018) (accuracy, 85–98 %) and Delwiche et al (2021) (accuracy, 70–96 %), but lower than that from Femenias et al, (2021b) (accuracy > 98.4 %) and Lv et al (2022) (accuracy, 98.31 %), which is probably due to the different spectra, preprocessing, and chemometrics involved in analysis. Based on previous studies, Liu et al, (2022) attempted to use a multispectal imaging system carrying 19 wavelengths to monitor the F. graminearum growth in wheat kernels.…”

Section: Application Of Hit In Wheat Quality Evaluationmentioning

confidence: 98%

Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review

Wang,

Ou,

et al. 2024

Food Chemistry: X

View full text Add to dashboard Cite

Section: Application Of Hit In Wheat Quality Evaluationmentioning

confidence: 98%

Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review

Wang,

Ou,

et al. 2024

Food Chemistry: X

View full text Add to dashboard Cite

“…Similarly, morphological analysis of leaves, seeds, and/or tubers (in the case of the root vegetable crops) has proven to be effective for evaluating quality parameters and disease severity for certain bio-aggressors (Wiwart et al, 2001;Tanabata et al, 2012;Whan et al, 2014;Komyshev et al, 2017;Si et al, 2017;Caraza-Harter and Endelman, 2020;Neilson et al, 2021;Miller et al, 2022). Solutions using more complex equipment to capture images in the VIS-NIR domain (multispectral and hyperspectral cameras) have been also proposed to evaluate moisture and nutrient content, plant health, seed water content composition and structure parameters, and vegetation indexes (Garcia et al, 2021;Mortensen et al, 2021;Femenias et al, 2022;Rangarajan et al, 2022;Ryckewaert et al, 2022;Yipeng et al, 2022;Qi et al, 2023;Solgi et al, 2023). However, simpler approaches may produce inconsistent outputs, while more advanced ones are costly and often impractical or unsuitable for use in real-scale trials.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, the use of hyperspectral imaging for detecting Fusarium sp. in seeds has been previously investigated (Delwiche et al, 2010;Shahin and Symons, 2011;Bauriegel and Herppich, 2014;Barbedo et al, 2015;Femenias et al, 2022;Rangarajan et al, 2022;Yipeng et al, 2022). The methods have been shown to be accurate and have identified more factors involved in FDK.…”

Section: Introductionmentioning

confidence: 99%

Developing affordable high-throughput plant phenotyping methods for breeding of cereals and tuber crops

Leiva-Sandoval¹

2023

Acta Universitatis Agriculturae Sueciae

View full text Add to dashboard Cite

High-throughput plant phenotyping (HTPP) is a fast, accurate, and non-destructive process for evaluating plants' health and environmental adaptability. HTPP accelerates the identification of agronomic traits of interest, eliminates subjectivism (which is innate to humans), and facilitates the development of adapted genotypes. Current HTPP methods often rely on imaging sensors and computer vision both in the field and under controlled (indoor) conditions. However, their use is limited by the costs and complexity of the necessary instrumentation, data analysis tools, and software. This issue could be overcome by developing more cost-efficient and user-friendly methods that let breeders, farmers, and stakeholders access the benefits of HTPP. To assist such efforts, this thesis presents an ensemble of dedicated affordable phenotyping methods using RGB imaging for a range of key applications under controlled conditions. The affordable Phenocave imaging system for use in controlled conditions was developed to facilitate studies on the effects of abiotic stresses by gathering data on important plant characteristics related to growth, yield, and adaptation to growing conditions and cultivation systems. Phenocave supports imaging sensors including visible (RGB), spectroscopic (multispectral and hyperspectral), and thermal imaging. Additionally, a pipeline for RGB image analysis was implemented as a plugin for the free and easy-to-use software ImageJ. This plugin has since proven to be an accurate alternative to conventional measurements that produces highly reproducible results. A subsequent study was conducted to evaluate the effects of heat and drought stress on plant growth and grain nutrient composition in wheat, an important staple cereal in Sweden. The effects of stress on plant growth were evaluated using image analysis, while stress-induced changes in the abundance of key plant compounds were evaluated by analyzing the nutrient composition of grains via chromatography. This led to the discovery of genotypes whose harvest quality remains stable under heat and drought stress. The next objective was to evaluate biotic stress; for this case, the effect of the fungal disease Fusarium head blight (FHB) that affects grain development in wheat was investigated. For this purpose, seed phenotyping parameters were used to determine the components and settings of a statistical model, which predicts the occurrence of FHB. The results reveal that grain morphology evaluations, such as length and width, were found to be significantly affected by the disease. Another study was carried out to estimate the disease severity of the common scab (CS) in potatoes, a widely popular food source. CS occurs on the tubers and reduces their visual appeal, significantly affecting their market value. Tubers were analyzed by a deep learning-based method to estimate disease lesion areas caused by CS. Results showed a high correlation between the predictions and expert visual scorings of the disease and proved to be a potential tool for the selection of genotypes that fulfill the market standards and resistance to CS. Both case studies highlight the role of imaging in plant health monitoring and its integration into the larger picture of plant health management. The methods presented in this work are a starting point for bridging the gap between costs and accessibility to imaging technology. These are affordable and user-friendly resources for generating pivotal knowledge on plant development and genotype selection. In the future, image acquisition of all the methods can be integrated into the Phenocave system, potentially allowing for a more automated and efficient plant health monitoring process, leading to the identification of tolerant genotypes to biotic and abiotic stresses.

show abstract

Section: Introductionmentioning

confidence: 99%

Novel Methods for Resistance Breeding in Winter Wheat

Zakieh¹

2023

Acta Universitatis Agriculturae Sueciae

View full text Add to dashboard Cite

Breeding for disease resistance in winter wheat is a critical task in the agricultural industry, as plant diseases can significantly impact crop yield and quality. Traditional breeding methods are time-consuming, and disease resistance screenings are often cost and labour-demanding. Therefore, novel breeding tools are being developed to speed up winter wheat genetic gain and increase its genetic diversity. A protocol to characterize winter wheat germplasm for resistance to Fusarium head blight (FHB) under accelerated growth conditions was carried out. The results showed that it is possible to reduce the time necessary to characterize germplasm for FHB resistance by growing up to three generations per year. In a genome-wide association study (GWAS), several markers were identified that were significantly associated with FHB resistance. These markers overlapped with previously known markers contributing to FHB resistance. Novel phenomic methods, the low throughput and affordable SmartGrain and the high throughput Cgrain ValueTM were implemented to predict FHB severity in the tested germplasm. Both methods showed good correlation to visual scoring, suggesting a potential alternative for the traditional visual assessment methods with machine-based methods that offer higher throughput and lower cost. The study also investigated seedling resistance to Septoria tritici blotch (STB) using association mapping and genomic prediction (GP). The study identified 20 QTL for STB seedling resistance of which nine were potentially novel QTL for STB seedling resistance and four overlapped with previously identified genomic regions at the adult stage. The identified QTL could be exploited in winter wheat marker-assisted selection (MAS) against STB and promote the seedling stage for early selection instead of the adult stage. Furthermore, the study investigated the genotypic responses of winter wheat seedlings infected with STB to the fungal biocontrol agent Clonostachys rosea. SNP markers associated with C. rosea biocontrol efficacy and disease resistance were identified, laying the groundwork for further research in genotype-specific-biocontrol compatibility in disease resistance breeding. The thesis provides useful insights into developing novel breeding tools for disease resistance in winter wheat and emphasizes the importance of industry collaboration to transfer knowledge from research to application.

show abstract

Near-infrared hyperspectral imaging evaluation of Fusarium damage and DON in single wheat kernels

Cited by 13 publications

References 34 publications

Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review

Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review

Developing affordable high-throughput plant phenotyping methods for breeding of cereals and tuber crops

Novel Methods for Resistance Breeding in Winter Wheat

Contact Info

Product

Resources

About