Determination of wheat kernels damaged by Fusarium head blight using monochromatic images of effective wavelengths from hyperspectral imaging coupled with an architecture self-search deep network

Yipeng, Lv; Wenbing, Lv; Han, Kuang-Wei; Tao, Wentao; Zheng, Ling; Weng, Shizhuang; Lingsheng, Huang

doi:10.1016/j.foodcont.2022.108819

Cited by 16 publications

(12 citation statements)

References 35 publications

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“…In another study, texture features classified the healthy and infected grains with PCA and support vector machine (SVM) [47]. In recent studies [48][49][50][51], HSI and multispectral images were employed to discriminate between the scab-Figure 9. Agricultural remote sensors and their application for scab detection (A) to quantify the mycotoxin quantity in wheat seeds, (B) detect the wheat scab through examination of wheat spike, (C) detect and monitor the scab at proximal canopy scale, and (D) monitor the scab at canopy and regional scale.…”

Section: Ars For Scab Detection In Wheat Kernelsmentioning

confidence: 99%

Global Trends and Future Directions in Agricultural Remote Sensing for Wheat Scab Detection: Insights from a Bibliometric Analysis

et al. 2023

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The study provides a comprehensive bibliometric analysis of imaging and non-imaging spectroscopy for wheat scab (INISWS) using CiteSpace. Therefore, we underpinned the developments of global INISWS detection at kernel, spike, and canopy scales, considering sensors, sensitive wavelengths, and algorithmic approaches. The study retrieved original articles from the Web of Science core collection (WOSCC) using a combination of advanced keyword searches related to INISWS. Afterward, visualization networks of author co-authorship, institution co-authorship, and country co-authorship were created to categorize the productive authors, countries, and institutions. Furthermore, the most significant authors and the core journals were identified by visualizing the journal co-citation, top research articles, document co-citation, and author co-citation networks. The investigation examined the major contributions of INISWS research at the micro, meso, and macro levels and highlighted the degree of collaboration between them and INISWS knowledge sources. Furthermore, it identifies the main research areas of INISWS and the current state of knowledge and provides future research directions. Moreover, an examination of grants and cooperating countries shows that the policy support from the People’s Republic of China, the United States of America, Germany, and Italy significantly benefits the progress of INISWS research. The co-occurrence analysis of keywords was carried out to highlight the new research frontiers and current hotspots. Lastly, the findings of kernel, spike, and canopy scales are presented regarding the best algorithmic, sensitive feature, and instrument techniques.

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Section: Ars For Scab Detection In Wheat Kernelsmentioning

confidence: 99%

Global Trends and Future Directions in Agricultural Remote Sensing for Wheat Scab Detection: Insights from a Bibliometric Analysis

et al. 2023

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“…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

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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.

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“…MIs of EWs from hyperspectral images only describe narrow-band information of specific wavelengths, and the wide-band information cannot be described well. And the combination of MI with RGB image which can express wide-band information can enrich the information to optimize the accuracy of identification [13]. Meanwhile, considering the pseudo RGB images transformed by CIE 1931 color matching functions from hyperspectral images can provide the almost identical spatial properties as RGB images [14], RGB images were not specially measured, and pseudo RGB images were directly used to replace RGB to explore the effectiveness in this study.…”

Section: Introductionmentioning

confidence: 99%

RGB image and monochromatic image of hyperspectral image for identification of apple fungi infection

Wenbing

Chang

Zhang

et al. 2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

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Detection of apple fungi infection is significant to provide the customized prevention and control strategies and ensure food safety. In this study, an identification method of infection of Botrytis cinerea and Rhizopus stolonifera was developed using the RGB images and monochromatic images (MIs) of effective wavelengths (EWs) of hyperspectral imaging. RGB images converted by CIE 1931 colour matching functions, and MIs of EWs were screened by random frog from hyperspectral images. U-Net combining data splicing strategy was adopted to segment the region of rot (ROR). Network features of RGB images and MIs of EWs of ROR were extracted by VGG16 and adopted to develop the classification models of fungi infection by using SVM, RF and KNN. The fused features of two-type images obtained the better classification, outperforming the other one-type image, and the optimal accuracy in prediction set of 99.25% was gotten from the SVM model. The proposed method provides the accurate detection of apple fungi infection and is beneficial to improve the quality of apple fruit.

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Determination of wheat kernels damaged by Fusarium head blight using monochromatic images of effective wavelengths from hyperspectral imaging coupled with an architecture self-search deep network

Cited by 16 publications

References 35 publications

Global Trends and Future Directions in Agricultural Remote Sensing for Wheat Scab Detection: Insights from a Bibliometric Analysis

Global Trends and Future Directions in Agricultural Remote Sensing for Wheat Scab Detection: Insights from a Bibliometric Analysis

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

RGB image and monochromatic image of hyperspectral image for identification of apple fungi infection

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