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Background: The early and specific detection of abiotic and biotic stresses, particularly their combinations, is a major challenge for maintaining and increasing plant productivity in sustainable agriculture under changing environmental conditions. Optical imaging techniques enable cost-efficient and non-destructive quantification of plant stress states. Monomodal detection of certain stressors is usually based on non-specific/indirect features and therefore is commonly limited in their cross-specificity to other stressors. The fusion of multi-domain sensor systems can provide more potentially discriminative features for machine learning models and potentially provide synergistic information to increase cross-specificity in plant disease detection when image data are fused at the pixel level. Results: In this study, we demonstrate successful multi-modal image registration of RGB, hyperspectral (HSI) and chlorophyll fluorescence (ChlF) kinetics data at the pixel level for high-throughput phenotyping of A. thaliana grown in Multi-well plates and an assay with detached leaf discs of Rosa × hybrida inoculated with the black spot disease-inducing fungus Diplocarpon rosae. Here, we showcase the effects of (i) selection of reference image selection, (ii) different registrations methods and (iii) frame selection on the performance of image registration via affine transform. In addition, we developed a combined approach for registration methods through NCC-based selection for each file, resulting in a robust and accurate approach that sacrifices computational time. Since image data encompass multiple objects, the initial coarse image registration using a global transformation matrix exhibited heterogeneity across different image regions. By employing an additional fine registration on the object-separated image data, we achieved a high overlap ratio. Specifically, for the A. thaliana test set, the overlap ratios (ORConvex) were 98.0 ± 2.3% for RGB-to-ChlF and 96.6 ± 4.2% for HSI-to-ChlF. For the Rosa × hybrida test set, the values were 98.9 ± 0.5% for RGB-to-ChlF and 98.3 ± 1.3% for HSI-to-ChlF. Conclusion: The presented multi-modal imaging pipeline enables high-throughput, high-dimensional phenotyping of different plant species with respect to various biotic or abiotic stressors. This paves the way for in-depth studies investigating the correlative relationships of the multi-domain data or the performance enhancement of machine learning models via multi modal image fusion. [ABSTRACT FROM AUTHOR]

Common buckwheat (Fagopyrum esculentum Moench.) is one of the most important orphan crops worldwide. Various research efforts have been done to improve cultivation methods to enhance important agronomic traits such as productivity and biotic/abiotic resistance. One important aspect is the seed trait, which has not been extensively studied due to the time-consuming and tedious nature of its examination. Despite this, understanding seed traits is crucial for meeting consumer needs and optimizing crop yields. Therefore, the aim of the study is to investigate the inheritance of common buckwheat seed traits--such as shape, size, and coat color--using an image-based approach. This method allows for the analysis of a large number of seeds with a level of accuracy and precision that was previously unattainable. The results indicate that seed coat color is inherited maternally. Notably, the parameters in size had substantial increases acting like overdominance. The number of seeds that were harvested from F1s of each cross differed a lot depending on the cross combinations and pin/thrum type. In addition, seed size had large reduction in F1s from the different seed-sized parents, especially in thrum type. These may show that there could be cross barriers. The results revealed trends of maternal inheritance for seed shape and coat color in buckwheat, an area that has not been extensively studied. These findings could support buckwheat breeding efforts, helping to address market needs and food demands in the face of significant climate change. [ABSTRACT FROM AUTHOR]

Abstract Background The early and specific detection of abiotic and biotic stresses, particularly their combinations, is a major challenge for maintaining and increasing plant productivity in sustainable agriculture under changing environmental conditions. Optical imaging techniques enable cost-efficient and non-destructive quantification of plant stress states. Monomodal detection of certain stressors is usually based on non-specific/indirect features and therefore is commonly limited in their cross-specificity to other stressors. The fusion of multi-domain sensor systems can provide more potentially discriminative features for machine learning models and potentially provide synergistic information to increase cross-specificity in plant disease detection when image data are fused at the pixel level. Results In this study, we demonstrate successful multi-modal image registration of RGB, hyperspectral (HSI) and chlorophyll fluorescence (ChlF) kinetics data at the pixel level for high-throughput phenotyping of A. thaliana grown in Multi-well plates and an assay with detached leaf discs of Rosa × hybrida inoculated with the black spot disease-inducing fungus Diplocarpon rosae. Here, we showcase the effects of (i) selection of reference image selection, (ii) different registrations methods and (iii) frame selection on the performance of image registration via affine transform. In addition, we developed a combined approach for registration methods through NCC-based selection for each file, resulting in a robust and accurate approach that sacrifices computational time. Since image data encompass multiple objects, the initial coarse image registration using a global transformation matrix exhibited heterogeneity across different image regions. By employing an additional fine registration on the object-separated image data, we achieved a high overlap ratio. Specifically, for the A. thaliana test set, the overlap ratios (ORConvex) were 98.0 ± 2.3% for RGB-to-ChlF and 96.6 ± 4.2% for HSI-to-ChlF. For the Rosa × hybrida test set, the values were 98.9 ± 0.5% for RGB-to-ChlF and 98.3 ± 1.3% for HSI-to-ChlF. Conclusion The presented multi-modal imaging pipeline enables high-throughput, high-dimensional phenotyping of different plant species with respect to various biotic or abiotic stressors. This paves the way for in-depth studies investigating the correlative relationships of the multi-domain data or the performance enhancement of machine learning models via multi modal image fusion.

The digitization of pathology departments in hospitals around the world is now a reality. The current commercial solutions applied to digitize histopathological samples consist of a robotic microscope with an RGB-type camera attached to it. This technology is very limited in terms of information captured, as it only works with three spectral bands of the visible electromagnetic spectrum. Therefore, we present an automated system that combines RGB and hyperspectral technology. Throughout this work, the hardware of the system and its components are described along with the developed software and a working methodology to ensure the correct capture of histopathological samples. The software is integrated by the controller of the microscope, which features an autofocus functionality, whole slide scanning with a stitching algorithm, and hyperspectral scanning functionality. As a reference, the time to capture and process a complete sample with 20 regions of high biological interest using the proposed method is estimated at a maximum of 79 min, reducing the time required by a manual operator by at least three times. Both hardware and software can be easily adapted to other systems that might benefit from the advantages of hyperspectral technology. [ABSTRACT FROM AUTHOR]

Approaches for remote sensing can be used to estimate the influence of changes in environmental conditions on terrestrial plants, providing timely protection of their growth, development, and productivity. Different optical methods, including the informative multispectral and hyperspectral imaging of reflected light, can be used for plant remote sensing; however, multispectral and hyperspectral cameras are technically complex and have a high cost. RGB imaging based on the analysis of color images of plants is definitely simpler and more accessible, but using this tool for remote sensing plant characteristics under changeable environmental conditions requires the development of methods to increase its informativity. Our review focused on using RGB imaging for remote sensing the characteristics of terrestrial plants. In this review, we considered different color models, methods of exclusion of background in color images of plant canopies, and various color indices and their relations to characteristics of plants, using regression models, texture analysis, and machine learning for the estimation of these characteristics based on color images, and some approaches to provide transformation of simple color images to hyperspectral and multispectral images. As a whole, our review shows that RGB imaging can be an effective tool for estimating plant characteristics; however, further development of methods to analyze color images of plants is necessary. [ABSTRACT FROM AUTHOR]

Challenges of climate change and growth population are exacerbated by noticeable environmental changes, which can increase the range of plant diseases, for instance, net blotch (NB), a foliar disease which significantly decreases barley (Hordeum vulgare L.) grain yield and quality. A resistant germplasm is usually identified through visual observation and the scoring of disease symptoms; however, this is subjective and time-consuming. Thus, automated, non-destructive, and low-cost disease-scoring approaches are highly relevant to barley breeding. This study presents a novel screening method for evaluating NB severity in barley. The proposed method uses an automated RGB imaging system, together with machine learning, to evaluate different symptoms and the severity of NB. The study was performed on three barley cultivars with distinct levels of resistance to NB (resistant, moderately resistant, and susceptible). The tested approach showed mean precision of 99% for various categories of NB severity (chlorotic, necrotic, and fungal lesions, along with leaf tip necrosis). The results demonstrate that the proposed method could be effective in assessing NB from barley leaves and specifying the level of NB severity; this type of information could be pivotal to precise selection for NB resistance in barley breeding. [ABSTRACT FROM AUTHOR]

The demand for more sustainable farming is driving interest in alternative cropping systems, such as strip intercropping. In such systems, two or more crops are grown simultaneously on the same field, offering advantages such as increased biocontrol of weed, pest, diseases, and increasing productivity in resource-limited ecosystems. However, with strip intercropping, complexity increases and quantitative data to study the competition between plants are still limited due to the current manual process of acquiring data. While individual-plant data would facilitate this study, the manual acquisition is not feasible for large-scale experiments. Alternatively, unmanned aerial vehicles (UAV) equipped with high-resolution camera can cover large areas and estimate individual-plant growth from RGB imagery using automated image-processing methods. This study investigated its applicability to monitor the plant-height development of individual cabbage plants in space and time with sufficient accuracy and to identify the potential differences between strip intercropping treatments. Using RGB imagery and structure-from-motion analysis, a digital surface model (DSM) was created. Individual plant-height was calculated from the DSM by estimating the height of the vegetation and the height of the soil. Comparing the height estimations with ground-truth height measurements showed an overall root mean square error (RMSE) of 4.67 cm, which is in the same range as the 4 cm standard deviation between measurements of multiple observers. The UAV-based height estimation of individual plants was used not only to compare the development in a strip intercropping field to that in a monoculture but also to compare with various treatments in the strip intercropping system. The results show that the plants grew faster in intercropping conditions than in monocropping conditions, with a subtle difference between treatments. Our results illustrate that with a UAV-based imaging approach we can go beyond current experimental practice and collect vast amounts of data on individual plants with high spatial and temporal resolution with an accuracy similar to that of manual measurements.

This paper introduces an innovative approach to Wind Turbine Blade (WTB) inspection through the synergistic use of thermal and RGB imaging, coupled with advanced deep learning techniques. We curated a unique dataset of 1000 thermal images of healthy and faulty blades using a FLIR C5 Compact Thermal Camera, which is equipped with Multi-Spectral Dynamic Imaging technology for enhanced imaging. This paper focuses on evaluating 35 deep learning classifiers, with a standout ensemble model combining Vision Transformer (ViT) and DenseNet161, achieving a remarkable 100% accuracy on the dataset. This model demonstrates the exceptional potential of deep learning in thermal diagnostic applications, particularly in predictive maintenance within the renewable energy sector. Our findings underscore the synergistic combination of ViT's global feature analysis and DenseNet161's dense connectivity, highlighting the importance of controlled environments and sophisticated preprocessing for accurate thermal image capture. This research contributes significantly to the field by providing a comprehensive dataset and demonstrating the efficacy of several deep learning models in ensuring the operational efficiency and reliability of wind turbines. [ABSTRACT FROM AUTHOR]

Image processing is the class of methods for face recognition, object detection, image compression, classification, etc. Hyperspectral imaging is the parallel acquisition of spatial and spectral information from an image, making it a useful tool for many applications. It is similar to an RGB color image, but it has a lot more channels and thereby making it challenging to visualize by the human eye. This paper discussed the fundamentals and components of RGB imaging and hyperspectral imaging. We also discussed the challenges of using a hyperspectral imaging system and some comparative study results of RGB imaging and hyperspectral imaging for various application areas. We also discussed the role of machine learning techniques in RGB imaging and hyperspectral image analysis. We also discussed future challenges and the scope of hyperspectral imaging. Though hyperspectral imaging has certain limitations, it can be a powerful image analysis tool for many applications in the future. [ABSTRACT FROM AUTHOR]

Background: The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable. Results: An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed. Conclusion: The technical realization of "Phenomenon" allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time. [ABSTRACT FROM AUTHOR]

Abstract Background The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable. Results An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed. Conclusion The technical realization of “Phenomenon” allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.

Approaches for remote sensing can be used to estimate the influence of changes in environmental conditions on terrestrial plants, providing timely protection of their growth, development, and productivity. Different optical methods, including the informative multispectral and hyperspectral imaging of reflected light, can be used for plant remote sensing; however, multispectral and hyperspectral cameras are technically complex and have a high cost. RGB imaging based on the analysis of color images of plants is definitely simpler and more accessible, but using this tool for remote sensing plant characteristics under changeable environmental conditions requires the development of methods to increase its informativity. Our review focused on using RGB imaging for remote sensing the characteristics of terrestrial plants. In this review, we considered different color models, methods of exclusion of background in color images of plant canopies, and various color indices and their relations to characteristics of plants, using regression models, texture analysis, and machine learning for the estimation of these characteristics based on color images, and some approaches to provide transformation of simple color images to hyperspectral and multispectral images. As a whole, our review shows that RGB imaging can be an effective tool for estimating plant characteristics; however, further development of methods to analyze color images of plants is necessary.

Challenges of climate change and growth population are exacerbated by noticeable environmental changes, which can increase the range of plant diseases, for instance, net blotch (NB), a foliar disease which significantly decreases barley (Hordeum vulgare L.) grain yield and quality. A resistant germplasm is usually identified through visual observation and the scoring of disease symptoms; however, this is subjective and time-consuming. Thus, automated, non-destructive, and low-cost disease-scoring approaches are highly relevant to barley breeding. This study presents a novel screening method for evaluating NB severity in barley. The proposed method uses an automated RGB imaging system, together with machine learning, to evaluate different symptoms and the severity of NB. The study was performed on three barley cultivars with distinct levels of resistance to NB (resistant, moderately resistant, and susceptible). The tested approach showed mean precision of 99% for various categories of NB severity (chlorotic, necrotic, and fungal lesions, along with leaf tip necrosis). The results demonstrate that the proposed method could be effective in assessing NB from barley leaves and specifying the level of NB severity; this type of information could be pivotal to precise selection for NB resistance in barley breeding.

Quality assessments of horticultural products are still often carried out manually in breeding contexts, although computer vision systems have been reported to be able to overcome the limitations of manual assessments, e.g. in automated food processing. Here, a new computer vision workflow for quality trait assessment of bush bean pods (Phaseolus vulgaris) is introduced to replace physical measurements and visual scorings of expert breeders, while increasing consistency, accuracy, and objectivity of the measurements. A closed imaging box was used to take images of bean pods from 40 different varieties to develop and validate computer vision workflows to assess breeding relevant shape and color traits of bean pods. For the detection of beaks and peduncles via a neural network approach (Mask R-CNN) accuracies of 95.5% were reached. Computer vision estimations and manual reference measurements of length and caliber were highly correlated (R=0.99). Also, curvature and brightness of green bean pods well- correlated with visual scorings of expert breeders (R=0.81, R=-0.87). A Random Forest Classifier was trained to distinguish yellow and extremely rare bicolored pods and a cross validation accuracy of 83 ±7% was reached. An additional backlight LED panel enabled non-destructive analysis of seed formation inside the pod and promising results were achieved using a Faster R-CNN model. This new computer vision workflow provides the opportunity to replace well-established manual workflows for quality trait assessment of bush bean pods as it is more objective, reliable, and considerably faster.

Multispectral imaging is valuable in many vision-related fields as it provides an additional modality to observe the world. Cameras equipped with multispectral filter arrays (MSFAs) are typically impractical for everyday use due to their intractable demosaicking and chromatic reproduction processes, which restrict their applicability beyond academic research. In this work, a novel MSFA design is proposed to enable dual-mode imaging for multispectral cameras. In addition to a conventional multispectral image, the camera is also able to produce a Bayer-formed RGB image from a single shot by grouping and merging adjacent pixels in the proposed MSFA, making it suitable for scenarios where display-ready RGB images are required. Furthermore, a two-stage optimization scheme is implemented to jointly optimize objective functions for both imaging modes. The evaluation results on multiple datasets suggest that the proposed MSFA design is able to simultaneously achieve competitive spectral reconstruction accuracy compared to elaborate multispectral cameras and chromatic accuracy compared to commercial RGB cameras. [ABSTRACT FROM AUTHOR]

Thangka refers to a form of Tibetan Buddhist painting on a fabric, scroll, or Thangka, often depicting deities, scenes, or mandalas. Deep-learning-based super-resolution techniques have been applied to improve the spatial resolution of hyperspectral images (HSIs), especially for the preservation and analysis of Thangka cultural heritage. However, existing CNN-based methods encounter difficulties in effectively preserving spatial information, due to challenges such as registration errors and spectral variability. To overcome these limitations, we present a novel cross-sensor super-resolution (SR) framework that utilizes high-resolution RGBs (HR-RGBs) to enhance the spectral features in low-resolution hyperspectral images (LR-HSIs). Our approach utilizes spatial–spectral integration (SSI) blocks and spatial–spectral restoration (SSR) blocks to effectively integrate and reconstruct spatial and spectral features. Furthermore, we introduce a frequency multi-head self-attention (F-MSA) mechanism that treats high-, medium-, and low-frequency features as tokens, enabling self-attention computations across the frequency dimension. We evaluate our method on a custom dataset of ancient Thangka paintings and demonstrate its effectiveness in enhancing the spectral resolution in high-resolution hyperspectral images (HR-HSIs), while preserving the spatial characteristics of Thangka artwork with minimal information loss. [ABSTRACT FROM AUTHOR]

Spike number (SN) per unit area is one of the major determinants of grain yield in wheat. Development of high-throughput techniques to count SN from large populations enables rapid and cost-effective selection and facilitates genetic studies. In the present study, we used a deep-learning algorithm, i.e., Faster Region-based Convolutional Neural Networks (Faster R-CNN) on Red-Green-Blue (RGB) images to explore the possibility of image-based detection of SN and its application to identify the loci underlying SN. A doubled haploid population of 101 lines derived from the Yangmai 16/Zhongmai 895 cross was grown at two sites for SN phenotyping and genotyped using the high-density wheat 660K SNP array. Analysis of manual spike number (MSN) in the field, image-based spike number (ISN), and verification of spike number (VSN) by Faster R-CNN revealed significant variation (P

Apples damaged by black root mold (BRM) lose moisture, vitamins, and minerals as well as carry dangerous toxins. Determination of the infection degree can allow for customized use of apples, reduce financial losses, and ensure food safety. In this study, red-green-blue (RGB) imaging and hyperspectral imaging (HSI) are combined to detect the infection degree of BRM in apple fruits. First, RGB and HSI images of healthy, mildly, moderately, and severely infected fruits are measured, and those with effective wavelengths (EWs) are screened from HSI by random frog. Second, the statistic and network features of images are extracted by using color moment and convolutional neural network. Meanwhile, random forest (RF), K-nearest neighbor, and support vector machine are used to construct classification models with the above two features of RGB and HSI images of EWs. Optimal results with the 100% accuracy of training set and 96% accuracy of prediction set are obtained by RF with the statistic and network features of the two images, outperforming the other cases. The proposed method furnishes an accurate and effective solution for determining the BRM infection degree in apples. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach to Wind Turbine Blade (WTB) inspection through the synergistic use of thermal and RGB imaging, coupled with advanced deep learning techniques. We curated a unique dataset of 1000 thermal images of healthy and faulty blades using a FLIR C5 Compact Thermal Camera, which is equipped with Multi-Spectral Dynamic Imaging technology for enhanced imaging. This paper focuses on evaluating 35 deep learning classifiers, with a standout ensemble model combining Vision Transformer (ViT) and DenseNet161, achieving a remarkable 100% accuracy on the dataset. This model demonstrates the exceptional potential of deep learning in thermal diagnostic applications, particularly in predictive maintenance within the renewable energy sector. Our findings underscore the synergistic combination of ViT’s global feature analysis and DenseNet161’s dense connectivity, highlighting the importance of controlled environments and sophisticated preprocessing for accurate thermal image capture. This research contributes significantly to the field by providing a comprehensive dataset and demonstrating the efficacy of several deep learning models in ensuring the operational efficiency and reliability of wind turbines.

This article describes a dataset of high-resolution visible-spectrum images of safflower (Carthamus tinctorius L.) plants obtained from a LemnaTec Scanalyser automated phenomics platform along with the associated image analysis output and manually acquired biomass data. This series contains 1832 images of 200 diverse safflower genotypes, acquired at the Plant Phenomics Victoria, Horsham, Victoria, Australia. Two Prosilica GT RGB (red-green-blue) cameras were used to generate 6576 × 4384 pixel portable network graphic (PNG) images. Safflower genotypes were either subjected to a salt treatment (250 mM NaCl) or grown as a control (0 mM NaCl) and imaged daily from 15 to 36 days after sowing. Each snapshot consists of four images collected at a point in time; one of which is taken from above (top-view) and the remainder from the side at either 0°, 120° or 240°. The dataset also includes analysis output quantifying traits and describing phenotypes, as well as manually collected biomass and leaf ion content data. The usage of the dataset is already demonstrated in Thoday-Kennedy et al. (2021) [1]. This dataset describes the early growth differences of diverse safflower genotypes and identified genotypes tolerant or susceptible to salinity stress. This dataset provides detailed image analysis parameters for phenotyping a large population of safflower that can be used for the training of image-based trait identification pipelines for a wide range of crop species.

Common buckwheat ( Fagopyrum esculentum Moench.) is one of the most important orphan crops worldwide. Various research efforts have been done to improve cultivation methods to enhance important agronomic traits such as productivity and biotic/abiotic resistance. One important aspect is the seed trait, which has not been extensively studied due to the time-consuming and tedious nature of its examination. Despite this, understanding seed traits is crucial for meeting consumer needs and optimizing crop yields. Therefore, the aim of the study is to investigate the inheritance of common buckwheat seed traits-such as shape, size, and coat color-using an image-based approach. This method allows for the analysis of a large number of seeds with a level of accuracy and precision that was previously unattainable. The results indicate that seed coat color is inherited maternally. Notably, the parameters in size had substantial increases acting like overdominance. The number of seeds that were harvested from F 1 s of each cross differed a lot depending on the cross combinations and pin/thrum type. In addition, seed size had large reduction in F 1 s from the different seed-sized parents, especially in thrum type. These may show that there could be cross barriers. The results revealed trends of maternal inheritance for seed shape and coat color in buckwheat, an area that has not been extensively studied. These findings could support buckwheat breeding efforts, helping to address market needs and food demands in the face of significant climate change., Competing Interests: Author YC was employed by the company Phytomix Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer RM declared a past co-authorship with the author SM, YSC to the handling editor., (Copyright © 2024 Oh, Park, Kim, Kang, Min Oh, Mansoor and Chung.)

Abstract Background Low temperature freezing stress has adverse effects on wheat seedling growth and final yield. The traditional method to evaluate the wheat injury caused by the freezing stress is by visual observations, which is time-consuming and laborious. Therefore, a more efficient and accurate method for freezing damage identification is urgently needed. Results A high-throughput phenotyping system was developed in this paper, namely, RGB freezing injury system, to effectively and efficiently quantify the wheat freezing injury in the field environments. The system is able to automatically collect, processing, and analyze the wheat images collected using a mobile phenotype cabin in the field conditions. A data management system was also developed to store and manage the original images and the calculated phenotypic data in the system. In this experiment, a total of 128 wheat varieties were planted, three nitrogen concentrations were applied and two biological and technical replicates were performed. And wheat canopy images were collected at the seedling pulling stage and three image features were extracted for each wheat samples, including ExG, ExR and ExV. We compared different test parameters and found that the coverage had a greater impact on freezing injury. Therefore, we preliminarily divided four grades of freezing injury according to the test results to evaluate the freezing injury of different varieties of wheat at the seedling stage. Conclusions The automatic phenotypic analysis method of freezing injury provides an alternative solution for high-throughput freezing damage analysis of field crops and it can be used to quantify freezing stress and has guiding significance for accelerating the selection of wheat excellent frost resistance genotypes.

Multispectral imaging is valuable in many vision-related fields as it provides an additional modality to observe the world. Cameras equipped with multispectral filter arrays (MSFAs) are typically impractical for everyday use due to their intractable demosaicking and chromatic reproduction processes, which restrict their applicability beyond academic research. In this work, a novel MSFA design is proposed to enable dual-mode imaging for multispectral cameras. In addition to a conventional multispectral image, the camera is also able to produce a Bayer-formed RGB image from a single shot by grouping and merging adjacent pixels in the proposed MSFA, making it suitable for scenarios where display-ready RGB images are required. Furthermore, a two-stage optimization scheme is implemented to jointly optimize objective functions for both imaging modes. The evaluation results on multiple datasets suggest that the proposed MSFA design is able to simultaneously achieve competitive spectral reconstruction accuracy compared to elaborate multispectral cameras and chromatic accuracy compared to commercial RGB cameras.

Thangka refers to a form of Tibetan Buddhist painting on a fabric, scroll, or Thangka, often depicting deities, scenes, or mandalas. Deep-learning-based super-resolution techniques have been applied to improve the spatial resolution of hyperspectral images (HSIs), especially for the preservation and analysis of Thangka cultural heritage. However, existing CNN-based methods encounter difficulties in effectively preserving spatial information, due to challenges such as registration errors and spectral variability. To overcome these limitations, we present a novel cross-sensor super-resolution (SR) framework that utilizes high-resolution RGBs (HR-RGBs) to enhance the spectral features in low-resolution hyperspectral images (LR-HSIs). Our approach utilizes spatial–spectral integration (SSI) blocks and spatial–spectral restoration (SSR) blocks to effectively integrate and reconstruct spatial and spectral features. Furthermore, we introduce a frequency multi-head self-attention (F-MSA) mechanism that treats high-, medium-, and low-frequency features as tokens, enabling self-attention computations across the frequency dimension. We evaluate our method on a custom dataset of ancient Thangka paintings and demonstrate its effectiveness in enhancing the spectral resolution in high-resolution hyperspectral images (HR-HSIs), while preserving the spatial characteristics of Thangka artwork with minimal information loss.

Apples damaged by black root mold (BRM) lose moisture, vitamins, and minerals as well as carry dangerous toxins. Determination of the infection degree can allow for customized use of apples, reduce financial losses, and ensure food safety. In this study, red-green-blue (RGB) imaging and hyperspectral imaging (HSI) are combined to detect the infection degree of BRM in apple fruits. First, RGB and HSI images of healthy, mildly, moderately, and severely infected fruits are measured, and those with effective wavelengths (EWs) are screened from HSI by random frog. Second, the statistic and network features of images are extracted by using color moment and convolutional neural network. Meanwhile, random forest (RF), K-nearest neighbor, and support vector machine are used to construct classification models with the above two features of RGB and HSI images of EWs. Optimal results with the 100% accuracy of training set and 96% accuracy of prediction set are obtained by RF with the statistic and network features of the two images, outperforming the other cases. The proposed method furnishes an accurate and effective solution for determining the BRM infection degree in apples.

Rice panicle phenotyping is required in rice breeding for high yield and grain quality. To fully evaluate spikelet and kernel traits without threshing and hulling, using X-ray and RGB scanning, we developed an integrated rice panicle phenotyping system and a corresponding image analysis pipeline. We compared five methods of counting spikelets and found that Faster R-CNN achieved high accuracy (R2 of 0.99) and speed. Faster R-CNN was also applied to indica and japonica classification and achieved 91% accuracy. The proposed integrated panicle phenotyping method offers benefit for rice functional genetics and breeding.

Background: Low temperature freezing stress has adverse effects on wheat seedling growth and final yield. The traditional method to evaluate the wheat injury caused by the freezing stress is by visual observations, which is time-consuming and laborious. Therefore, a more efficient and accurate method for freezing damage identification is urgently needed. Results: A high-throughput phenotyping system was developed in this paper, namely, RGB freezing injury system, to effectively and efficiently quantify the wheat freezing injury in the field environments. The system is able to automatically collect, processing, and analyze the wheat images collected using a mobile phenotype cabin in the field conditions. A data management system was also developed to store and manage the original images and the calculated phenotypic data in the system. In this experiment, a total of 128 wheat varieties were planted, three nitrogen concentrations were applied and two biological and technical replicates were performed. And wheat canopy images were collected at the seedling pulling stage and three image features were extracted for each wheat samples, including ExG, ExR and ExV. We compared different test parameters and found that the coverage had a greater impact on freezing injury. Therefore, we preliminarily divided four grades of freezing injury according to the test results to evaluate the freezing injury of different varieties of wheat at the seedling stage. Conclusions: The automatic phenotypic analysis method of freezing injury provides an alternative solution for high-throughput freezing damage analysis of field crops and it can be used to quantify freezing stress and has guiding significance for accelerating the selection of wheat excellent frost resistance genotypes. [ABSTRACT FROM AUTHOR]

• The RGB imaging system greatly reduces the cost for SSC prediction compared to spectral imaging system. • Stacked autoencoder model can realize the extraction of deep features from RGB images in pixel-level. • The information fusion of channel a* and b* performance higher accuracy for SSC prediction than single channel. Soluble solids content (SSC) is one of the important components for the assessment of fruit quality. Near-infrared (NIR) spectroscopy, multispectral and hyperspectral imaging techniques are widely used methods for the SSC estimation. However, the high prices are unaffordable for small commercial orchards. The objective of this work is to present a computer vision system for the SSC estimation of "Fuji" apples in different ripening stages. Multiple information of the color channels (channel a* and channel b*) in L*a*b* color space translated from RGB images were applied to extract color features. Stacked autoencoder (SAE) algorithm was used to extract color features in pixel-level, and the trained parameters were used as the initial parameters of the prediction model by using back propagation neural network (BPNN) algorithm. The results show that the prediction accuracy of the SAE-BPNN model based on color features in pixel-level is higher than that of the BPNN model based on pure color features in feature-level. Moreover, the model with fusion information of both color channels as inputs can get better prediction results than that of single channel. The SAE-BPNN model with SAE structure of 270-120-30 yielded the best result with R p 2 = 0.5953 and RMSEP = 0.8856%. This research indicates that RGB imaging technology based on multiple information has potential for the SSC prediction, which provides reference values for fruit harvesting and non-destructive evaluation of fruit quality. [ABSTRACT FROM AUTHOR]

Salinity is a major contributing factor to the degradation of arable land, and reductions in crop growth and yield. To overcome these limitations, the breeding of crop varieties with improved salt tolerance is needed. This requires effective and high-throughput phenotyping to optimize germplasm enhancement. Safflower (Carthamus tinctorius L.), is an underappreciated but highly versatile oilseed crop, capable of growing in saline and arid environments. To develop an effective and rapid phenotyping protocol to differentiate salt responses in safflower genotypes, experiments were conducted in the automated imaging facility at Plant Phenomics Victoria, Horsham, focussing on digital phenotyping at early vegetative growth. The initial experiment, at 0, 125, 250, and 350 mM sodium chloride (NaCl), showed that 250 mM NaCl was optimum to differentiate salt sensitive and tolerant genotypes. Phenotyping of a diverse set of 200 safflower genotypes using the developed protocol defined four classes of salt tolerance or sensitivity, based on biomass and ion accumulation. Salt tolerance in safflower was dependent on the exclusion of Na+ from shoot tissue and the maintenance of K+ uptake. Salinity response identified in glasshouse experiments showed some consistency with the performance of representatively selected genotypes tested under sodic field conditions. Overall, our results suggest that digital phenotyping can be an effective high-throughput approach in identifying candidate genotypes for salt tolerance in safflower.

Wheat is one of the world’s most economically important cereal crop, grown on 220 million hectares. Fusarium head blight (FHB) disease is considered a major threat to durum (Triticum turgidum subsp. durum (Desfontaines) Husnache) and bread wheat (T. aestivum L.) cultivars and is mainly managed by the application of fungicides at anthesis. However, fungicides are applied when FHB symptoms are clearly visible and the spikes are almost entirely bleached (% of diseased spikelets > 80%), by when it is too late to control FHB disease. For this reason, farmers often react by performing repeated fungicide treatments that, however, due to the advanced state of the infection, cause a waste of money and pose significant risks to the environment and non-target organisms. In the present study, we used unmanned aerial vehicle (UAV)-based thermal infrared (TIR) and red-green-blue (RGB) imaging for FHB detection in T. turgidum (cv. Marco Aurelio) under natural field conditions. TIR and RGB data coupled with ground-based measurements such as spike’s temperature, photosynthetic efficiency and molecular identification of FHB pathogens, detected FHB at anthesis half-way (Zadoks stage 65, ZS 65), when the percentage (%) of diseased spikelets ranged between 20% and 60%. Moreover, in greenhouse experiments the transcripts of the key genes involved in stomatal closure were mostly up-regulated in F. graminearum-inoculated plants, demonstrating that the physiological mechanism behind the spike’s temperature increase and photosynthetic efficiency decrease could be attributed to the closure of the guard cells in response to F. graminearum. In addition, preliminary analysis revealed that there is differential regulation of genes between drought-stressed and F. graminearum-inoculated plants, suggesting that there might be a possibility to discriminate between water stress and FHB infection. This study shows the potential of UAV-based TIR and RGB imaging for field phenotyping of wheat and other cereal crop species in response to environmental stresses. This is anticipated to have enormous promise for the detection of FHB disease and tremendous implications for optimizing the application of fungicides, since global food crop demand is to be met with minimal environmental impacts.

Salinity is a major contributing factor to the degradation of arable land, and reductions in crop growth and yield. To overcome these limitations, the breeding of crop varieties with improved salt tolerance is needed. This requires effective and high-throughput phenotyping to optimize germplasm enhancement. Safflower (Carthamus tinctorius L.), is an underappreciated but highly versatile oilseed crop, capable of growing in saline and arid environments. To develop an effective and rapid phenotyping protocol to differentiate salt responses in safflower genotypes, experiments were conducted in the automated imaging facility at Plant Phenomics Victoria, Horsham, focussing on digital phenotyping at early vegetative growth. The initial experiment, at 0, 125, 250, and 350 mM sodium chloride (NaCl), showed that 250 mM NaCl was optimum to differentiate salt sensitive and tolerant genotypes. Phenotyping of a diverse set of 200 safflower genotypes using the developed protocol defined four classes of salt tolerance or sensitivity, based on biomass and ion accumulation. Salt tolerance in safflower was dependent on the exclusion of Na+ from shoot tissue and the maintenance of K+ uptake. Salinity response identified in glasshouse experiments showed some consistency with the performance of representatively selected genotypes tested under sodic field conditions. Overall, our results suggest that digital phenotyping can be an effective high-throughput approach in identifying candidate genotypes for salt tolerance in safflower. [ABSTRACT FROM AUTHOR]

Wheat is one of the world's most economically important cereal crop, grown on 220 million hectares. Fusarium head blight (FHB) disease is considered a major threat to durum (Triticum turgidum subsp. durum (Desfontaines) Husnache) and bread wheat (T. aestivum L.) cultivars and is mainly managed by the application of fungicides at anthesis. However, fungicides are applied when FHB symptoms are clearly visible and the spikes are almost entirely bleached (% of diseased spikelets > 80%), by when it is too late to control FHB disease. For this reason, farmers often react by performing repeated fungicide treatments that, however, due to the advanced state of the infection, cause a waste of money and pose significant risks to the environment and non-target organisms. In the present study, we used unmanned aerial vehicle (UAV)-based thermal infrared (TIR) and red-green-blue (RGB) imaging for FHB detection in T. turgidum (cv. Marco Aurelio) under natural field conditions. TIR and RGB data coupled with ground-based measurements such as spike's temperature, photosynthetic efficiency and molecular identification of FHB pathogens, detected FHB at anthesis half-way (Zadoks stage 65, ZS 65), when the percentage (%) of diseased spikelets ranged between 20% and 60%. Moreover, in greenhouse experiments the transcripts of the key genes involved in stomatal closure were mostly up-regulated in F. graminearum -inoculated plants, demonstrating that the physiological mechanism behind the spike's temperature increase and photosynthetic efficiency decrease could be attributed to the closure of the guard cells in response to F. graminearum. In addition, preliminary analysis revealed that there is differential regulation of genes between drought-stressed and F. graminearum -inoculated plants, suggesting that there might be a possibility to discriminate between water stress and FHB infection. This study shows the potential of UAV-based TIR and RGB imaging for field phenotyping of wheat and other cereal crop species in response to environmental stresses. This is anticipated to have enormous promise for the detection of FHB disease and tremendous implications for optimizing the application of fungicides, since global food crop demand is to be met with minimal environmental impacts. [ABSTRACT FROM AUTHOR]

Aim of study: For baby-leaf lettuces greenhouse cultivations the absence of weeds is a mandatory quality requirement. One of the most promising and innovative technologies in weed research, is the use of Unmanned Aerial Vehicles (or drones) equipped with acquisition systems. The aim of this study was to provide an estimation of the exact weed amount on baby-sized red lettuce beds using a light drone equipped with an RGB microcamera. Area of study: Trials were performed at specialized organic farm site in Eboli (Salerno, Italy), under polyethylene multi-tunnel greenhouse. Material and methods: The RGB images acquired were processed with specific algorithms distinguishing weeds from crop yields, estimating the weeds covered surface and the severity of weed contamination in terms of biomass. A regression between the percentage of the surface covered by weed (with respect to the image total surface) and the weight of weed (with respect to the total harvested biomass) was calculated. Main results: The regression between the total cover values of the 25 calibration images and the total weight measured report a significant linear correlation. Digital monitoring was able to capture with accuracy the highly variable weed coverage that, among the different grids positioned under real cultivation conditions, was in the range 0-16.4% of the total cultivated one. Research highlights: In a precision weed management context, with the aim of improving management and decreasing the use of pesticides, this study provided an estimation of the exact weed amount on baby-sized red lettuce beds using a light drone.

Agriculture practices in monocropping need to become more sustainable and one of the ways to achieve this is to reintroduce intercropping. However, quantitative data to evaluate plant growth in intercropping systems are still lacking. Unmanned aerial vehicles (UAV) have the potential to become a state-of-the-art technique for the automatic estimation of plant growth. Individual plant height is an important trait attribute for field investigation as it can be used to derive information on crop growth throughout the growing season. This study aimed to investigate the applicability of UAV-based RGB imagery combined with the structure from motion (SfM) method for estimating the individual plants height of cabbage, pumpkin, barley, and wheat in an intercropping field during a complete growing season under varying conditions. Additionally, the effect of different percentiles and buffer sizes on the relationship between UAV-estimated plant height and ground truth plant height was examined. A crop height model (CHM) was calculated as the difference between the digital surface model (DSM) and the digital terrain model (DTM). The results showed that the overall correlation coefficient (R2) values of UAV-estimated and ground truth individual plant heights for cabbage, pumpkin, barley, and wheat were 0.86, 0.94, 0.36, and 0.49, respectively, with overall root mean square error (RMSE) values of 6.75 cm, 6.99 cm, 14.16 cm, and 22.04 cm, respectively. More detailed analysis was performed up to the individual plant level. This study suggests that UAV imagery can provide a reliable and automatic assessment of individual plant heights for cabbage and pumpkin plants in intercropping but cannot be considered yet as an alternative approach for barley and wheat.

Summary: In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the main yield‐determining components. An automatic evaluation of this parameter may contribute to the advance of wheat phenotyping and monitoring. There is no standard protocol for wheat ear counting in the field, and moreover it is time consuming. An automatic ear‐counting system is proposed using machine learning techniques based on RGB (red, green, blue) images acquired from an unmanned aerial vehicle (UAV). Evaluation was performed on a set of 12 winter wheat cultivars with three nitrogen treatments during the 2017–2018 crop season. The automatic system uses a frequency filter, segmentation and feature extraction, with different classification techniques, to discriminate wheat ears in micro‐plot images. The relationship between the image‐based manual counting and the algorithm counting exhibited high levels of accuracy and efficiency. In addition, manual ear counting was conducted in the field for secondary validation. The correlations between the automatic and the manual in‐situ ear counting with grain yield were also compared. Correlations between the automatic ear counting and grain yield were stronger than those between manual in‐situ counting and GY, particularly for the lower nitrogen treatment. Methodological requirements and limitations are discussed. Significance Statement: Ear density (ears m–2) is one of the main agronomical yield components of wheat. This study represents a novel contribution to the field of RGB image processing for plant phenotyping using unmanned aerial vehicle (UAV) platforms. By combining high‐resolution RGB imagery with an automatic ear classification and counting system, we have shown that it is possible to assess ear density with high precision from an aerial platform. This is the first published study successfully deploying this approach. [ABSTRACT FROM AUTHOR]

Intraoperative optical imaging is a localization technique for the functional areas of the human brain cortex during neurosurgical procedures. These areas are assessed by monitoring the oxygenated (HbO 2 ) and deoxygenated hemoglobin (Hb) concentration changes occurring in the brain. Sometimes, the functional status of the brain is assessed using metabolic biomarkers: the oxidative state of cytochrome-c-oxidase (oxCCO). A setup composed of a white light source and a hyperspectral or a standard RGB camera could be used to identify the functional areas. The choice of the best spectral configuration is still based on an empirical approach. We propose in this study a method to define the optimal spectral combinations of a commercial hyperspectral camera for the computation of hemodynamic and metabolic brain maps. The method is based on a Monte Carlo framework that simulates the acquisition of the intrinsic optical signal following a neuronal activation. The results indicate that the optimal spectral combination of a hyperspectral camera aims to accurately quantify the HbO 2 ( 0.5 % error), Hb ( 4.4 % error), and oxCCO ( 15 % error) responses in the brain following neuronal activation. We also show that RGB imaging is a low cost and accurate solution to compute Hb maps ( 4 % error), but not accurate to compute HbO 2 ( 48 % error) or oxCCO ( 1036 % error) maps. [ABSTRACT FROM AUTHOR]

Antecedentes: tradicionalmente‚ la calidad de los productos agrícolas se ha evaluado mediante procesos de inspección visual y/o métodos químicos. Sin embargo‚ en muchos casos estos métodos resultan inoportunos e insuficientes debido a que los cambios morfológicos en los alimentos se evidencian en etapas avanzadas de su desarrollo dificultando la restauración de su calidad. Además‚ los análisis químicos requieren de largos tiempos de análisis‚ son costosos y no permiten obtener resultados en tiempo real. En este contexto‚ las técnicas de espectroscopia óptica se presentan como una alternativa rápida‚ confiable‚ económica y ecológica para la evaluación de la calidad y la detección de irregularidades en alimentos. El café es un producto de gran interés debido a su alta demanda a nivel mundial‚ que presenta diferentes modificaciones sensoriales a través de su procesamiento. Por lo tanto‚ estas técnicas resultan especialmente útiles para realizar predicciones sobre su calidad. Objetivo: diseñar‚ probar y estandarizar una metodología de análisis colorimétrico en tiempo real mediante el uso imágenes RGB‚ para cuantificar la calidad física del café cereza antes del beneficiado. Métodos: en este trabajo se ha desarrollado probado y estandarizado una metodología de análisis colorimétrico en tiempo real mediante el uso de imágenes RGB para la cuantificación de la calidad física de la cereza de café. Para ello se ha comparado la información cromática obtenida a partir de imágenes RGB e hiperespectrales con el fin de validar el método de análisis y su accesibilidad desde cualquier cámara convencional. Todo esto se ha logrado mediante el uso de un algoritmo de análisis cromático desarrollado en Python. Resultados: se obtienen mapas colorimétricos en tiempo real para la cuantificación precisa de la calidad de los granos de café diferenciando las cerezas maduras‚ verdes y pintonas. Conclusiones: se espera que los resultados obtenidos al finalizar el proyecto sirvan para entregar a los agricultores una metodología de bajo costo y eficaz que evalúe la calidad y el rendimiento de la cosecha de café. [ABSTRACT FROM AUTHOR]

Antecedentes: las imágenes hiperespectrales (HSI) son un objeto de estudio importante en la espectroscopia dado que permite condensar información espacial y espectral en una sola matriz de datos mediante diversas técnicas‚ por tanto‚ proporciona la suficiente información para identificar o distinguir rasgos característicos en un material. La implementación de HSI se ha desarrollado con todo tipo de técnicas espectroscópicas‚ pero en la industria agrícola para el estudio de posibles contaminantes y su evolución en plantas se hace uso de la espectroscopia infrarroja cercana (NIR) en combinación de las imágenes hiperespectrales (NIR-HSI). Objetivo: desarrollar un método de análisis automático para la discriminación y categorización de la información en HSI y RGB‚ que permita detectar cambios en parámetros colorimétricos (croma‚ tono y brillo) en productos vegetales de interés que expresan cambios de color como respuesta a condiciones de estrés ambiental. Método: se implementa una metodología computacional que permite optimizar‚ analizar‚ depurar y resaltar la información sobre el conjunto de datos que entrega una imagen RGB y HSI. Por otro lado, se implementa una comparativa entre los dos tipos de imágenes‚ asociando así las ventajas y desventajas que presenta cada metodología estudiada. Para el presente trabajo se implementó una cámara hiperespectral de una resolución espectral de 204 bandas es un rango de 397 - 1003 nm (VISNIR) y una resolución espacial de 512 x 512 píxeles‚ por lo tanto‚ la información que se analiza en cada imagen es alrededor de 53M de datos. Resultados: los resultados obtenidos en la implementación del método indican que la información colorimétrica de cada muestra varía dependiendo las condiciones ambientales‚ permitiendo así la clasificación y selección de los componentes de la muestra según su color sin importar el método que se implemente. Conclusiones: la implementación de una metodología que aplique imágenes RGB para la determinación de cambios en parámetros colorimétricos es sustentable siempre y cuando los resultados sean estandarizados mediante una comparativa con una metodología hiperespectral. [ABSTRACT FROM AUTHOR]

RGB optical imaging is a marker-free, contactless, and non-invasive technique that is able to monitor hemodynamic brain response following neuronal activation using task-based and resting-state procedures. Magnetic resonance imaging (fMRI) and functional near infra-red spectroscopy (fNIRS) resting-state procedures cannot be used intraoperatively but RGB imaging provides an ideal solution to identify resting-state networks during a neurosurgical operation. We applied resting-state methodologies to intraoperative RGB imaging and evaluated their ability to identify resting-state networks. We adapted two resting-state methodologies from fMRI for the identification of resting-state networks using intraoperative RGB imaging. Measurements were performed in 3 patients who underwent resection of lesions adjacent to motor sites. The resting-state networks were compared to the identifications provided by RGB task-based imaging and electrical brain stimulation. Intraoperative RGB resting-state networks corresponded to RGB task-based imaging (DICE:0.55±0.29). Resting state procedures showed a strong correspondence between them (DICE:0.66±0.11) and with electrical brain stimulation. RGB imaging is a relevant technique for intraoperative resting-state networks identification. Intraoperative resting-state imaging has several advantages compared to functional task-based analyses: data acquisition is shorter, less complex, and less demanding for the patients, especially for those unable to perform the tasks.

Aphanomyces root rot (ARR) is a soil-borne disease that results in severe yield losses in lentil. The development of resistant cultivars is one of the key strategies to control this pathogen. However, the evaluation of disease severity is limited to visual scores that can be subjective. This study utilized image-based phenotyping approaches to evaluate Aphanomyces euteiches resistance in lentil genotypes in greenhouse (351 genotypes from lentil single plant/LSP derived collection and 191 genotypes from recombinant inbred lines/RIL using digital Red-Green-Blue/RGB and hyperspectral imaging) and field (173 RIL genotypes using unmanned aerial system-based multispectral imaging) conditions. Moderate to strong correlations were observed between RGB, multispectral, and hyperspectral derived features extracted from lentil shoots/roots and visual scores. In general, root features extracted from RGB imaging were found to be strongly associated with disease severity. With only three root traits, elastic net regression model was able to predict disease severity across and within multiple datasets (R2 = 0.45–0.73 and RMSE = 0.66–1.00). The selected features could represent visual disease scores. Moreover, we developed twelve normalized difference spectral indices (NDSIs) that were significantly correlated with disease scores: two NDSIs for lentil shoot section – computed from wavelengths of 1170, 1160, 1270, and 1280 nm (0.12 ≤ |r| ≤ 0.24, P < 0.05) and ten NDSIs for lentil root sections – computed from wavelengths in the range of 630–670, 700–840, and 1320–1530 nm (0.10 ≤ |r| ≤ 0.50, P < 0.05). Root-derived NDSIs were more accurate in predicting disease scores with an R2 of 0.54 (RMSE = 0.86), especially when the model was trained and tested on LSP accessions, compared to R2 of 0.25 (RMSE = 1.64) when LSP and RIL genotypes were used as train and test datasets, respectively. Importantly, NDSIs – computed from wavelengths of 700, 710, 730, and 790 nm – had strong positive correlations with disease scores (0.35 ≤r ≤ 0.50, P < 0.0001), which was confirmed in field phenotyping with similar correlations using vegetation index with red edge wavelength (normalized difference red edge, 0.36 ≤ |r| ≤ 0.57, P < 0.0001). The adopted image-based phenotyping approaches can help plant breeders to objectively quantify ARR resistance and reduce the subjectivity in selecting potential genotypes.

Established methods for delineating anthropogenic and natural strata during fieldwork are based on the visual and tactile perception of excavators. Modern image analysis techniques can help to ensure objectivity and reproducibility when documenting sections and plana. Within this study we examine the unsupervised classification of digital images as a technique for delimiting layers and identifying stratigraphic features. Assessing the potential of this approach, we exemplarily captured soil profiles with high-contrast stratigraphy, located in the area of a historical vineyard (Brandenburg, Germany). Reproducible analyses were carried out using open-source software, allowing for the future advancement of the methodology utilized and providing a basis for the analysis of more complex stratigraphic sequences. We compare clustering results of high-resolution RGB and hyperspectral images (470–830 nm, 37 bands). Multiple pre-processing and processing steps are carried out to evaluate their influence. Our results render the semi-automatic analysis of RGB images helpful for stratigraphic interpretation. [ABSTRACT FROM AUTHOR]

Background: The sustainable management of agricultural resources requires the integration of cutting‐edge science with the observation and identification of crops. This assists experts to make correct decisions. The aim of this study is to assess the robustness of a commonly used deep learning tool, VGG16, in improving the categorization of wheat kernels. Two fusion methodologies were considered simultaneously. We performed experiments on visible light (RGB), short wave infrared (SWIR), and visible‐near infrared (VNIR) datasets, including 40 classes, with 200 samples in each class, giving 8000 samples in total. Results: After making simulations with 6400 training and 1600 testing samples, we achieved excellent performance scores, with 98.19% and 100% accuracy rates, respectively. Conclusion: The wheat identification system developed here serves as an effective identification framework and supports the view that deep learning tools can adequately discriminate between different types of wheat kernels. The proposed automated system would be useful for improving economic growth and in reducing the labor force, leading to greater efficiency and higher productivity in the wheat industry. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

The genetic diversity of sweet potato [Ipomoea batatas (L.) Lam.] and its wild relatives has been collected and conserved in germplasm collections worldwide and explored employing several tools. The characterization of crops diversity through morphological tools produce useful information. However, the use of conventional morphological descriptions exhibits limitations due to the use of subjective and categorical parameters that affect phenotypic description and diversity estimation. In order to increase the efficiency to discriminate different phenotypes not detected by conventional morphological descriptors, new phenomic approaches were used. Seventy sweet potato accessions collected in the northern coast of Colombia were characterized by forty-nine parameters from conventional sweet potato descriptors and data obtained by RGB imaging and colourimetry. Field descriptions, RGB imaging-colourimetry and both databases integrated were analysed using Gower's general similarity coefficient for clustering. Estimation of genotype similarity was significantly improved when quantitative data obtained by RGB imaging and colourimetry analysis were included. Variations in traits such as flesh and periderm colour in roots, leaves, vein colour and leaf shape that were not detected by field descriptors, were efficiently discriminated by measuring pixel values from images, estimation of shape descriptors (circularity, solidity, area) and colourimetry data. Expected high correlations were found for field parameters (number of lobes, lobe type, and central lobe shape) and image data (circularity, roundness and solidity). The combination of RGB imaging and colourimetry benefits the quality of morphological characterizations, resulting in a cost-effective process that is able to identify polymorphisms and target traits for diversity estimation and breeding. [ABSTRACT FROM AUTHOR]

Aphanomyces root rot (ARR) is a soil-borne disease that results in severe yield losses in lentil. The development of resistant cultivars is one of the key strategies to control this pathogen. However, the evaluation of disease severity is limited to visual scores that can be subjective. This study utilized image-based phenotyping approaches to evaluate Aphanomyces euteiches resistance in lentil genotypes in greenhouse (351 genotypes from lentil single plant/LSP derived collection and 191 genotypes from recombinant inbred lines/RIL using digital Red-Green-Blue/RGB and hyperspectral imaging) and field (173 RIL genotypes using unmanned aerial system-based multispectral imaging) conditions. Moderate to strong correlations were observed between RGB, multispectral, and hyperspectral derived features extracted from lentil shoots/roots and visual scores. In general, root features extracted from RGB imaging were found to be strongly associated with disease severity. With only three root traits, elastic net regression model was able to predict disease severity across and within multiple datasets (R 2 = 0.45–0.73 and RMSE = 0.66–1.00). The selected features could represent visual disease scores. Moreover, we developed twelve normalized difference spectral indices (NDSIs) that were significantly correlated with disease scores: two NDSIs for lentil shoot section – computed from wavelengths of 1170, 1160, 1270, and 1280 nm (0.12 ≤ | r | ≤ 0.24, P < 0.05) and ten NDSIs for lentil root sections – computed from wavelengths in the range of 630–670, 700–840, and 1320–1530 nm (0.10 ≤ | r | ≤ 0.50, P < 0.05). Root-derived NDSIs were more accurate in predicting disease scores with an R 2 of 0.54 (RMSE = 0.86), especially when the model was trained and tested on LSP accessions, compared to R 2 of 0.25 (RMSE = 1.64) when LSP and RIL genotypes were used as train and test datasets, respectively. Importantly, NDSIs – computed from wavelengths of 700, 710, 730, and 790 nm – had strong positive correlations with disease scores (0.35 ≤ r ≤ 0.50, P < 0.0001), which was confirmed in field phenotyping with similar correlations using vegetation index with red edge wavelength (normalized difference red edge, 0.36 ≤ | r | ≤ 0.57, P < 0.0001). The adopted image-based phenotyping approaches can help plant breeders to objectively quantify ARR resistance and reduce the subjectivity in selecting potential genotypes. [ABSTRACT FROM AUTHOR]

As drought and heat stress are major challenges for crop productivity under future climate changes, tolerant cultivars are highly in demand. This study investigated the potential of existing Nordic wheat genotypes to resist unfavorable conditions. Four genotypes were selected based on their heat sensitivity (heat-sensitive: LM19, SF1; heat-tolerant: LM62, NS3). At the tillering stage, the plants were subjected to four treatments under controlled conditions: control, drought, heat and combined drought and heat stress. The morpho-physiological performance was quantified during the early and late phase of stress, as well as the recovery phase. We applied an integrative image-based phenotyping approach monitoring plant growth dynamics by structural Red Green Blue (RGB) imaging, photosynthetic performance by chlorophyll fluorescence imaging and transpiration efficiency by thermal infrared imaging. The results demonstrated that the selected genotypes were moderately affected in their photosynthetic efficiency and growth under drought stress, whereas heat and combined stress caused rapid reductions in photosynthesis and growth. Furthermore, drought stress had a major impact on canopy temperature. The NS3 genotype was the most robust genotype, as indicated by its improved response under all stress treatments due to its relatively small biomass. However, the genotypes showed different tolerance to individual and combined stress.

Fungal infections cause the considerable losses in apple production and quality. Given its ability to perceive external and internal characteristics, hyperspectral imaging (HSI) performs well in detecting fungal infections in apples. However, high cost, time-consuming operation, and complicated processing limit the widespread application of HSI. This study proposes a novel detection method, that uses the hyperspectral transformation (HT) of RGB images, for detecting fungal infection in apples. Wiener estimation, pseudo-inverse and kernel regression (KR) were adopted to perform HT to transform the responses of RGB images into pseudo reflectance spectra (PRS). And the effects of RGB responses, band ranges and spectral resolution were further discussed. KR obtained the optimal HT and the resultant PRS in the range of 400–1000 nm and with a resolution of approximately 2.5 nm were close to the original reflectance spectra of HSI with a mean root mean square error of 0.050, the mean relative error of 12.17 %, and peak signal-to-noise ratio of 72.66. The obtained PRS were then combined with machine learning to classify the category and degree of fungal infection in apple fruit. The best category recognition was obtained by random forest and PRS. The accuracy of the calibration set (ACC C), validation set (ACC V) and prediction set (ACC P) were 93.31 %, 80.95 % and 83.33 %, respectively. The best determinations of degree of infection by Botrytis cinerea and Rhizopus stolonifer were obtained using k-nearest neighbor, convolutional neural network and PRS, resulting in an ACC P of 90 % for each model. The proposed method achieved a convenient, low-cost, and accurate detection of fungal infection types and degree in apple fruit and is expected to be useful for analysing the quality of other agricultural products. [Display omitted] • HT was used to acquire hyperspectral data from RGB data. • Effect of band ranges, spectral resolution and RGB responses were discussed in HT. • PRS obtained by KR is nearly identical to original reflectance spectra. • Transformed data can be effectively used to detect fungal infection. [ABSTRACT FROM AUTHOR]

Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use.

This study characterizes the scatter‐specific tissue contrast that can be obtained by high spatial frequency (HSF) domain imaging and cross‐polarization (CP) imaging, using a standard color imaging system, and how combining them may be beneficial. Both HSF and CP approaches are known to modulate the sensitivity of epi‐illumination reflectance images between diffuse multiply scattered and superficially backscattered photons, providing enhanced contrast from microstructure and composition than what is achieved by standard wide‐field imaging. Measurements in tissue‐simulating optical phantoms show that CP imaging returns localized assessments of both scattering and absorption effects, while HSF has uniquely specific sensitivity to scatter‐only contrast, with a strong suppression of visible contrast from blood. The combination of CP and HSF imaging provided an expanded sensitivity to scatter compared with CP imaging, while rejecting specular reflections detected by HSF imaging. ex vivo imaging of an atlas of dissected rodent organs/tissues demonstrated the scatter‐based contrast achieved with HSF, CP and HSF‐CP imaging, with the white light spectral signal returned by each approach translated to a color image for intuitive encoding of scatter‐based contrast within images of tissue. The results suggest that visible CP‐HSF imaging could have the potential to aid diagnostic imaging of lesions in skin or mucosal tissues and organs, where just CP is currently the standard practice imaging modality. [ABSTRACT FROM AUTHOR]

Complementary technique to preoperative fMRI and electrical brain stimulation (EBS) for glioma resection could improve dramatically the surgical procedure and patient care. Intraoperative RGB optical imaging is a technique for localizing functional areas of the human cerebral cortex that can be used during neurosurgical procedures. However, it still lacks robustness to be used with neurosurgical microscopes as a clinical standard. In particular, a robust quantification of biomarkers of brain functionality is needed to assist neurosurgeons. We propose a methodology to evaluate and optimize intraoperative identification of brain functional areas by RGB imaging. This consist in a numerical 3D brain model based on Monte Carlo simulations to evaluate intraoperative optical setups for identifying functional brain areas. We also adapted fMRI Statistical Parametric Mapping technique to identify functional brain areas in RGB videos acquired for 12 patients. Simulation and experimental results were consistent and showed that the intraoperative identification of functional brain areas is possible with RGB imaging using deoxygenated hemoglobin contrast. Optical functional identifications were consistent with those provided by EBS and preoperative fMRI. We also demonstrated that a halogen lighting may be particularity adapted for functional optical imaging. We showed that an RGB camera combined with a quantitative modeling of brain hemodynamics biomarkers can evaluate in a robust way the functional areas during neurosurgery and serve as a tool of choice to complement EBS and fMRI. • SPM technique adapted to RGB videos is relevant to identify functional brain areas. • A numerical 3D brain model can help to design and evaluate intraoperative optical devices. • RGB imaging with Halogen lighting are adapted for intraoperative brain mapping. [ABSTRACT FROM AUTHOR]