Journal articles on the topic 'Hyperspectral Phenotyping'
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Huang, Peikui, Xiwen Luo, Jian Jin, Liangju Wang, Libo Zhang, Jie Liu, and Zhigang Zhang. "Improving High-Throughput Phenotyping Using Fusion of Close-Range Hyperspectral Camera and Low-Cost Depth Sensor." Sensors 18, no. 8 (August 17, 2018): 2711. http://dx.doi.org/10.3390/s18082711.
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Hyperspectral sensors, especially the close-range hyperspectral camera, have been widely introduced to detect biological processes of plants in the high-throughput phenotyping platform, to support the identification of biotic and abiotic stress reactions at an early stage. However, the complex geometry of plants and their interaction with the illumination, severely affects the spectral information obtained. Furthermore, plant structure, leaf area, and leaf inclination distribution are critical indexes which have been widely used in multiple plant models. Therefore, the process of combination between hyperspectral images and 3D point clouds is a promising approach to solve these problems and improve the high-throughput phenotyping technique. We proposed a novel approach fusing a low-cost depth sensor and a close-range hyperspectral camera, which extended hyperspectral camera ability with 3D information as a potential tool for high-throughput phenotyping. An exemplary new calibration and analysis method was shown in soybean leaf experiments. The results showed that a 0.99 pixel resolution for the hyperspectral camera and a 3.3 millimeter accuracy for the depth sensor, could be achieved in a controlled environment using the method proposed in this paper. We also discussed the new capabilities gained using this new method, to quantify and model the effects of plant geometry and sensor configuration. The possibility of 3D reflectance models can be used to minimize the geometry-related effects in hyperspectral images, and to significantly improve high-throughput phenotyping. Overall results of this research, indicated that the proposed method provided more accurate spatial and spectral plant information, which helped to enhance the precision of biological processes in high-throughput phenotyping.
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Sarić, Rijad, Viet D. Nguyen, Timothy Burge, Oliver Berkowitz, Martin Trtílek, James Whelan, Mathew G. Lewsey, and Edhem Čustović. "Applications of hyperspectral imaging in plant phenotyping." Trends in Plant Science 27, no. 3 (March 2022): 301–15. http://dx.doi.org/10.1016/j.tplants.2021.12.003.
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Kuska, Matheus Thomas, Anna Brugger, Stefan Thomas, Mirwaes Wahabzada, Kristian Kersting, Erich-Christian Oerke, Ulrike Steiner, and Anne-Katrin Mahlein. "Spectral Patterns Reveal Early Resistance Reactions of Barley Against Blumeria graminis f. sp. hordei." Phytopathology® 107, no. 11 (November 2017): 1388–98. http://dx.doi.org/10.1094/phyto-04-17-0128-r.
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Differences in early plant–pathogen interactions are mainly characterized by using destructive methods. Optical sensors are advanced techniques for phenotyping host–pathogen interactions on different scales and for detecting subtle plant resistance responses against pathogens. A microscope with a hyperspectral camera was used to study interactions between Blumeria graminis f. sp. hordei and barley (Hordeum vulgare) genotypes with high susceptibility or resistance due to hypersensitive response (HR) and papilla formation. Qualitative and quantitative assessment of pathogen development was used to explain changes in hyperspectral signatures. Within 48 h after inoculation, genotype-specific changes in the green and red range (500 to 690 nm) and a blue shift of the red-edge inflection point were observed. Manual analysis indicated resistance-specific reflectance patterns from 1 to 3 days after inoculation. These changes could be linked to host plant modifications depending on individual host–pathogen interactions. Retrospective analysis of hyperspectral images revealed spectral characteristics of HR against B. graminis f. sp. hordei. For early HR detection, an advanced data mining approach localized HR spots before they became visible on the RGB images derived from hyperspectral imaging. The link among processes during pathogenesis and host resistance to changes in hyperspectral signatures provide evidence that sensor-based phenotyping is suitable to advance time-consuming and cost-expensive visual rating of plant disease resistances.
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Sadeghi-Tehran, Pouria, Nicolas Virlet, and Malcolm J. Hawkesford. "A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery." Remote Sensing 13, no. 5 (February 27, 2021): 898. http://dx.doi.org/10.3390/rs13050898.
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(1) Background: Information rich hyperspectral sensing, together with robust image analysis, is providing new research pathways in plant phenotyping. This combination facilitates the acquisition of spectral signatures of individual plant organs as well as providing detailed information about the physiological status of plants. Despite the advances in hyperspectral technology in field-based plant phenotyping, little is known about the characteristic spectral signatures of shaded and sunlit components in wheat canopies. Non-imaging hyperspectral sensors cannot provide spatial information; thus, they are not able to distinguish the spectral reflectance differences between canopy components. On the other hand, the rapid development of high-resolution imaging spectroscopy sensors opens new opportunities to investigate the reflectance spectra of individual plant organs which lead to the understanding of canopy biophysical and chemical characteristics. (2) Method: This study reports the development of a computer vision pipeline to analyze ground-acquired imaging spectrometry with high spatial and spectral resolutions for plant phenotyping. The work focuses on the critical steps in the image analysis pipeline from pre-processing to the classification of hyperspectral images. In this paper, two convolutional neural networks (CNN) are employed to automatically map wheat canopy components in shaded and sunlit regions and to determine their specific spectral signatures. The first method uses pixel vectors of the full spectral features as inputs to the CNN model and the second method integrates the dimension reduction technique known as linear discriminate analysis (LDA) along with the CNN to increase the feature discrimination and improves computational efficiency. (3) Results: The proposed technique alleviates the limitations and lack of separability inherent in existing pre-defined hyperspectral classification methods. It optimizes the use of hyperspectral imaging and ensures that the data provide information about the spectral characteristics of the targeted plant organs, rather than the background. We demonstrated that high-resolution hyperspectral imagery along with the proposed CNN model can be powerful tools for characterizing sunlit and shaded components of wheat canopies in the field. The presented method will provide significant advances in the determination and relevance of spectral properties of shaded and sunlit canopy components under natural light conditions.
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Brugger, Anna, Jan Behmann, Stefan Paulus, Hans-Georg Luigs, Matheus Thomas Kuska, Patrick Schramowski, Kristian Kersting, Ulrike Steiner, and Anne-Katrin Mahlein. "Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range." Remote Sensing 11, no. 12 (June 12, 2019): 1401. http://dx.doi.org/10.3390/rs11121401.
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Previous plant phenotyping studies have focused on the visible (VIS, 400–700 nm), near-infrared (NIR, 700–1000 nm) and short-wave infrared (SWIR, 1000–2500 nm) range. The ultraviolet range (UV, 200–380 nm) has not yet been used in plant phenotyping even though a number of plant molecules like flavones and phenol feature absorption maxima in this range. In this study an imaging UV line scanner in the range of 250–430 nm is introduced to investigate crop plants for plant phenotyping. Observing plants in the UV-range can provide information about important changes of plant substances. To record reliable and reproducible time series results, measurement conditions were defined that exclude phototoxic effects of UV-illumination in the plant tissue. The measurement quality of the UV-camera has been assessed by comparing it to a non-imaging UV-spectrometer by measuring six different plant-based substances. Given the findings of these preliminary studies, an experiment has been defined and performed monitoring the stress response of barley leaves to salt stress. The aim was to visualize the effects of abiotic stress within the UV-range to provide new insights into the stress response of plants. Our study demonstrated the first use of a hyperspectral sensor in the UV-range for stress detection in plant phenotyping.
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Banerjee, Bikram P., Sameer Joshi, Emily Thoday-Kennedy, Raj K. Pasam, Josquin Tibbits, Matthew Hayden, German Spangenberg, and Surya Kant. "High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response." Journal of Experimental Botany 71, no. 15 (March 18, 2020): 4604–15. http://dx.doi.org/10.1093/jxb/eraa143.
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Abstract The development of crop varieties with higher nitrogen use efficiency is crucial for sustainable crop production. Combining high-throughput genotyping and phenotyping will expedite the discovery of novel alleles for breeding crop varieties with higher nitrogen use efficiency. Digital and hyperspectral imaging techniques can efficiently evaluate the growth, biophysical, and biochemical performance of plant populations by quantifying canopy reflectance response. Here, these techniques were used to derive automated phenotyping of indicator biomarkers, biomass and chlorophyll levels, corresponding to different nitrogen levels. A detailed description of digital and hyperspectral imaging and the associated challenges and required considerations are provided, with application to delineate the nitrogen response in wheat. Computational approaches for spectrum calibration and rectification, plant area detection, and derivation of vegetation index analysis are presented. We developed a novel vegetation index with higher precision to estimate chlorophyll levels, underpinned by an image-processing algorithm that effectively removed background spectra. Digital shoot biomass and growth parameters were derived, enabling the efficient phenotyping of wheat plants at the vegetative stage, obviating the need for phenotyping until maturity. Overall, our results suggest value in the integration of high-throughput digital and spectral phenomics for rapid screening of large wheat populations for nitrogen response.
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Behmann, Jan, Anne-Katrin Mahlein, Stefan Paulus, Heiner Kuhlmann, Erich-Christian Oerke, and Lutz Plümer. "Calibration of hyperspectral close-range pushbroom cameras for plant phenotyping." ISPRS Journal of Photogrammetry and Remote Sensing 106 (August 2015): 172–82. http://dx.doi.org/10.1016/j.isprsjprs.2015.05.010.
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Oerke, Erich-Christian, Katja Herzog, and Reinhard Toepfer. "Hyperspectral phenotyping of the reaction of grapevine genotypes toPlasmopara viticola." Journal of Experimental Botany 67, no. 18 (August 27, 2016): 5529–43. http://dx.doi.org/10.1093/jxb/erw318.
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Ribes, Mathieu, Gaspard Russias, Denis Tregoat, and Antoine Fournier. "Towards Low-Cost Hyperspectral Single-Pixel Imaging for Plant Phenotyping." Sensors 20, no. 4 (February 19, 2020): 1132. http://dx.doi.org/10.3390/s20041132.
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Hyperspectral imaging techniques have been expanding considerably in recent years. The cost of current solutions is decreasing, but these high-end technologies are not yet available for moderate to low-cost outdoor and indoor applications. We have used some of the latest compressive sensing methods with a single-pixel imaging setup. Projected patterns were generated on Fourier basis, which is well-known for its properties and reduction of acquisition and calculation times. A low-cost, moderate-flow prototype was developed and studied in the laboratory, which has made it possible to obtain metrologically validated reflectance measurements using a minimal computational workload. From these measurements, it was possible to discriminate plant species from the rest of a scene and to identify biologically contrasted areas within a leaf. This prototype gives access to easy-to-use phenotyping and teaching tools at very low-cost.
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Ma, Dongdong, Tanzeel U. Rehman, Libo Zhang, Hideki Maki, Mitchell R. Tuinstra, and Jian Jin. "Modeling of Environmental Impacts on Aerial Hyperspectral Images for Corn Plant Phenotyping." Remote Sensing 13, no. 13 (June 28, 2021): 2520. http://dx.doi.org/10.3390/rs13132520.
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Aerial imaging technologies have been widely applied in agricultural plant remote sensing. However, an as yet unexplored challenge with field imaging is that the environmental conditions, such as sun angle, cloud coverage, temperature, and so on, can significantly alter plant appearance and thus affect the imaging sensor’s accuracy toward extracting plant feature measurements. These image alterations result from the complicated interaction between the real-time environments and plants. Analysis of these impacts requires continuous monitoring of the changes through various environmental conditions, which has been difficult with current aerial remote sensing systems. This paper aimed to propose a modeling method to comprehensively understand and model the environmental influences on hyperspectral imaging data. In 2019, a fixed hyperspectral imaging gantry was constructed in Purdue University’s research farm, and over 8000 repetitive images of the same corn field were taken with a 2.5 min interval for 31 days. Time-tagged local environment data, including solar zenith angle, solar irradiation, temperature, wind speed, and so on, were also recorded during the imaging time. The images were processed for phenotyping data, and the time series decomposition method was applied to extract the phenotyping data variation caused by the changing environments. An artificial neural network (ANN) was then built to model the relationship between the phenotyping data variation and environmental changes. The ANN model was able to accurately predict the environmental effects in remote sensing results, and thus could be used to effectively eliminate the environment-induced variation in the phenotyping features. The test of the normalized difference vegetation index (NDVI) calculated from the hyperspectral images showed that variance in NDVI was reduced by 79%. A similar performance was confirmed with the relative water content (RWC) predictions. Therefore, this modeling method shows great potential for application in aerial remote sensing applications in agriculture, to significantly improve the imaging quality by effectively eliminating the effects from the changing environmental conditions.
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Ma, Dongdong, Liangju Wang, Libo Zhang, Zhihang Song, Tanzeel U. Rehman, and Jian Jin. "Stress Distribution Analysis on Hyperspectral Corn Leaf Images for Improved Phenotyping Quality." Sensors 20, no. 13 (June 30, 2020): 3659. http://dx.doi.org/10.3390/s20133659.
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High-throughput imaging technologies have been developing rapidly for agricultural plant phenotyping purposes. With most of the current crop plant image processing algorithms, the plant canopy pixels are segmented from the images, and the averaged spectrum across the whole canopy is calculated in order to predict the plant’s physiological features. However, the nutrients and stress levels vary significantly across the canopy. For example, it is common to have several times of difference among Soil Plant Analysis Development (SPAD) chlorophyll meter readings of chlorophyll content at different positions on the same leaf. The current plant image processing algorithms cannot provide satisfactory plant measurement quality, as the averaged color cannot characterize the different leaf parts. Meanwhile, the nutrients and stress distribution patterns contain unique features which might provide valuable signals for phenotyping. There is great potential to develop a finer level of image processing algorithm which analyzes the nutrients and stress distributions across the leaf for improved quality of phenotyping measurements. In this paper, a new leaf image processing algorithm based on Random Forest and leaf region rescaling was developed in order to analyze the distribution patterns on the corn leaf. The normalized difference vegetation index (NDVI) was used as an example to demonstrate the improvements of the new algorithm in differentiating between different nitrogen stress levels. With the Random Forest method integrated into the algorithm, the distribution patterns along the corn leaf’s mid-rib direction were successfully modeled and utilized for improved phenotyping quality. The algorithm was tested in a field corn plant phenotyping assay with different genotypes and nitrogen treatments. Compared with the traditional image processing algorithms which average the NDVI (for example) throughout the whole leaf, the new algorithm more clearly differentiates the leaves from different nitrogen treatments and genotypes. We expect that, besides NDVI, the new distribution analysis algorithm could improve the quality of other plant feature measurements in similar ways.
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Fan, Jiahao, Jing Zhou, Biwen Wang, Natalia de Leon, Shawn M. Kaeppler, Dayane C. Lima, and Zhou Zhang. "Estimation of Maize Yield and Flowering Time Using Multi-Temporal UAV-Based Hyperspectral Data." Remote Sensing 14, no. 13 (June 25, 2022): 3052. http://dx.doi.org/10.3390/rs14133052.
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Maize (Zea mays L.) is one of the most consumed grains in the world. Within the context of continuous climate change and the reduced availability of arable land, it is urgent to breed new maize varieties and screen for the desired traits, e.g., high yield and strong stress tolerance. Traditional phenotyping methods relying on manual assessment are time-consuming and prone to human errors. Recently, the application of uncrewed aerial vehicles (UAVs) has gained increasing attention in plant phenotyping due to their efficiency in data collection. Moreover, hyperspectral sensors integrated with UAVs can offer data streams with high spectral and spatial resolutions, which are valuable for estimating plant traits. In this study, we collected UAV hyperspectral imagery over a maize breeding field biweekly across the growing season, resulting in 11 data collections in total. Multiple machine learning models were developed to estimate the grain yield and flowering time of the maize breeding lines using the hyperspectral imagery. The performance of the machine learning models and the efficacy of different hyperspectral features were evaluated. The results showed that the models with the multi-temporal imagery outperformed those with imagery from single data collections, and the ridge regression using the full band reflectance achieved the best estimation accuracies, with the correlation coefficients (r) between the estimates and ground truth of 0.54 for grain yield, 0.91 for days to silking, and 0.92 for days to anthesis. In addition, we assessed the estimation performance with data acquired at different growth stages to identify the good periods for the UAV survey. The best estimation results were achieved using the data collected around the tasseling stage (VT) for the grain yield estimation and around the reproductive stages (R1 or R4) for the flowering time estimation. Our results showed that the robust phenotyping framework proposed in this study has great potential to help breeders efficiently estimate key agronomic traits at early growth stages.
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Rehman, Tanzeel U., Libo Zhang, Dongdong Ma, and Jian Jin. "Common Latent Space Exploration for Calibration Transfer across Hyperspectral Imaging-Based Phenotyping Systems." Remote Sensing 14, no. 2 (January 11, 2022): 319. http://dx.doi.org/10.3390/rs14020319.
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Hyperspectral imaging has increasingly been used in high-throughput plant phenotyping systems. Rapid advancement in the field of phenotyping has resulted in a wide array of hyperspectral imaging systems. However, sharing the plant feature prediction models between different phenotyping facilities becomes challenging due to the differences in imaging environments and imaging sensors. Calibration transfer between imaging facilities is crucially important to cope with such changes. Spectral space adjustment methods including direct standardization (DS), its variants (PDS, DPDS) and spectral scale transformation (SST) require the standard samples to be imaged in different facilities. However, in real-world scenarios, imaging the standard samples is practically unattractive. Therefore, in this study, we presented three methods (TCA, c-PCA, and di-PLSR) to transfer the calibration models without requiring the standard samples. In order to compare the performance of proposed approaches, maize plants were imaged in two greenhouse-based HTPP systems using two pushbroom-style hyperspectral cameras covering the visible near-infrared range. We tested the proposed methods to transfer nitrogen content (N) and relative water content (RWC) calibration models. The results showed that prediction R2 increased by up to 14.50% and 42.20%, while the reduction in RMSEv was up to 74.49% and 76.72% for RWC and N, respectively. The di-PLSR achieved the best results for almost all the datasets included in this study, with TCA being second. The performance of c-PCA was not at par with the di-PLSR and TCA. Our results showed that the di-PLSR helped to recover the performance of RWC, and N models plummeted due to the differences originating from new imaging systems (sensor type, spectrograph, lens system, spatial resolution, spectral resolution, field of view, bit-depth, frame rate, and exposure time) or lighting conditions. The proposed approaches can alleviate the requirement of developing a new calibration model for a new phenotyping facility or to resort to the spectral space adjustment using the standard samples.
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Wang, Jian, Bizhi Wu, Markus V. Kohnen, Daqi Lin, Changcai Yang, Xiaowei Wang, Ailing Qiang, et al. "Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature." Plant Phenomics 2021 (March 30, 2021): 1–14. http://dx.doi.org/10.34133/2021/9765952.
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High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding. However, manual measurements within breeding programs are time consuming and have high cost and low throughput, which limit the application in large-scale field phenotyping. In this study, we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders. In total, 13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield. Using an Unmanned Aerial Vehicle (UAV) platform equipped with a hyperspectral camera to capture images over multiple time series, a rice yield classification model based on the XGBoost algorithm was proposed. Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not. The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice. Thus, we developed a low-cost, high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency.
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Leucker, Marlene, Anne-Katrin Mahlein, Ulrike Steiner, and Erich-Christian Oerke. "Improvement of Lesion Phenotyping in Cercospora beticola–Sugar Beet Interaction by Hyperspectral Imaging." Phytopathology® 106, no. 2 (February 2016): 177–84. http://dx.doi.org/10.1094/phyto-04-15-0100-r.
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Cercospora leaf spot (CLS) caused by Cercospora beticola is the most destructive leaf disease of sugar beet and may cause high losses in yield and quality. Breeding and cultivation of disease-resistant varieties is an important strategy to control this economically relevant plant disease. Reliable and robust resistance parameters are required to promote breeding progress. CLS lesions on five different sugar beet genotypes incubated under controlled conditions were analyzed for phenotypic differences related to field resistance to C. beticola. Lesions of CLS were rated by classical quantitative and qualitative methods in combination with noninvasive hyperspectral imaging. Calculating the ratio of lesion center to lesion margin, four CLS phenotypes were identified that vary in size and spatial composition. Lesions could be differentiated into subareas based on their spectral characteristics in the range of 400 to 900 nm. Sugar beet genotypes with lower disease severity typically had lesions with smaller centers compared with highly susceptible genotypes. Accordingly, the number of conidia per diseased leaf area on resistant plants was lower. The assessment of lesion phenotypes by hyperspectral imaging with regard to sporulation may be an appropriate method to identify subtle differences in disease resistance. The spectral and spatial analysis of the lesions has the potential to improve the screening process in breeding for CLS resistance.
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Jiménez, Juan de la Cruz, Luisa Leiva, Juan A. Cardoso, Andrew N. French, and Kelly R. Thorp. "Proximal sensing of Urochloa grasses increases selection accuracy." Crop and Pasture Science 71, no. 4 (2020): 401. http://dx.doi.org/10.1071/cp19324.
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In the American tropics, livestock production is highly restricted by forage availability. In addition, the breeding and development of new forage varieties with outstanding yield and high nutritional quality is often limited by a lack of resources and poor technology. Non-destructive, high-throughput phenotyping offers a rapid and economical means of evaluating large numbers of genotypes. In this study, visual assessments, digital colour images, and spectral reflectance data were collected from 200 Urochloa hybrids in a field setting. Partial least-squares regression (PLSR) was applied to relate visual assessments, digital image analysis and spectral data to shoot dry weight, crude protein and chlorophyll concentrations. Visual evaluations of biomass and greenness were collected in 68 min, digital colour imaging data in 40 min, and hyperspectral canopy data in 80 min. Root-mean-squared errors of prediction for PLSR estimations of shoot dry weight, crude protein and chlorophyll were lowest for digital image analysis followed by hyperspectral analysis and visual assessments. This study showed that digital colour image and spectral analysis techniques have the potential to improve precision and reduce time for tropical forage grass phenotyping.
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Lu, Yuzhen, Kitt G. Payn, Piyush Pandey, Juan J. Acosta, Austin J. Heine, Trevor D. Walker, and Sierra Young. "Hyperspectral Imaging with Cost-Sensitive Learning for High-Throughput Screening of Loblolly Pine (Pinus taeda L.) Seedlings for Freeze Tolerance." Transactions of the ASABE 64, no. 6 (2021): 2045–59. http://dx.doi.org/10.13031/trans.14708.
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HighlightsA hyperspectral imaging approach was developed for freeze-tolerance phenotyping of loblolly pine seedlings.Image acquisition was conducted before and periodically after artificial freezing of the seedlings.A hyperspectral data processing pipeline was developed to extract the spectra from seedling segments.Cost-sensitive support vector machine (SVM) was used for classifying stressed and healthy seedlings.Post-freeze scanning of seedlings on day 41 achieved the highest screening accuracy of 97%.Abstract. Loblolly pine (Pinus taeda L.) is a commercially important timber species planted across a wide temperature gradient in the southeastern U.S. It is critical to ensure that the planting stock is suitably adapted to the growing environment to achieve high productivity and survival. Long-term field studies, although considered the most reliable method for assessing cold hardiness of loblolly pine, are extremely resource-intensive and time-consuming. The development of a high-throughput screening tool to characterize and classify freeze tolerance among different genetic entries of seedlings will facilitate accurate deployment of highly productive and well-adapted families across the landscape. This study presents a novel approach using hyperspectral imaging to screen loblolly pine seedlings for freeze tolerance. A diverse population of 1549 seedlings raised in a nursery were subjected to an artificial mid-winter freeze using a freeze chamber. A custom-assembled hyperspectral imaging system was used for in-situ scanning of the seedlings before and periodically after the freeze event, followed by visual scoring of the frozen seedlings. A hyperspectral data processing pipeline was developed to segment individual seedlings and extract the spectral data. Examination of the spectral features of the seedlings revealed reductions in chlorophylls and water concentrations in the freeze-susceptible plants. Because the majority of seedlings were freeze-stressed, leading to severe class imbalance in the hyperspectral data, a cost-sensitive learning technique that aims to optimize a class-specific cost matrix in classification schemes was proposed for modeling the imbalanced hyperspectral data, classifying the seedlings into healthy and freeze-stressed phenotypes. Cost optimization was effective for boosting the classification accuracy compared to regular modeling that assigns equal costs to individual classes. Full-spectrum, cost-optimized support vector machine (SVM) models achieved geometric classification accuracies of 75% to 78% before and within 10 days after the freeze event, and up to 96% for seedlings 41 days after the freeze event. The top portions of seedlings were more indicative of freeze events than the middle and bottom portions, leading to better classification accuracies. Further, variable selection enabled significant reductions in wavelengths while achieving even better accuracies of up to 97% than full-spectrum SVM modeling. This study demonstrates that hyperspectral imaging can provide tree breeders with a valuable tool for improved efficiency and objectivity in the characterization and screening of freeze tolerance for loblolly pine. Keywords: Cost-sensitive learning, Freeze tolerance, Hyperspectral imaging, Plant phenotyping, Support vector machine.
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Jin, Xiuliang, Zhenhai Li, and Clement Atzberger. "Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”." Remote Sensing 12, no. 6 (March 13, 2020): 940. http://dx.doi.org/10.3390/rs12060940.
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High-throughput crop phenotyping is harnessing the potential of genomic resources for the genetic improvement of crop production under changing climate conditions. As global food security is not yet assured, crop phenotyping has received increased attention during the past decade. This spectral issue (SI) collects 30 papers reporting research on estimation of crop phenotyping traits using unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV) imagery. Such platforms were previously not widely available. The special issue includes papers presenting recent advances in the field, with 22 UAV-based papers and 12 UGV-based articles. The special issue covers 16 RGB sensor papers, 11 papers on multi-spectral imagery, and further 4 papers on hyperspectral and 3D data acquisition systems. A total of 13 plants’ phenotyping traits, including morphological, structural, and biochemical traits are covered. Twenty different data processing and machine learning methods are presented. In this way, the special issue provides a good overview regarding potential applications of the platforms and sensors, to timely provide crop phenotyping traits in a cost-efficient and objective manner. With the fast development of sensors technology and image processing algorithms, we expect that the estimation of crop phenotyping traits supporting crop breeding scientists will gain even more attention in the future.
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Grzybowski, Marcin, Nuwan K. Wijewardane, Abbas Atefi, Yufeng Ge, and James C. Schnable. "Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges." Plant Communications 2, no. 4 (July 2021): 100209. http://dx.doi.org/10.1016/j.xplc.2021.100209.
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Reddy, Priyanka, Kathryn M. Guthridge, Joe Panozzo, Emma J. Ludlow, German C. Spangenberg, and Simone J. Rochfort. "Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview." Sensors 22, no. 5 (March 3, 2022): 1981. http://dx.doi.org/10.3390/s22051981.
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Near-infrared (800–2500 nm; NIR) spectroscopy coupled to hyperspectral imaging (NIR-HSI) has greatly enhanced its capability and thus widened its application and use across various industries. This non-destructive technique that is sensitive to both physical and chemical attributes of virtually any material can be used for both qualitative and quantitative analyses. This review describes the advancement of NIR to NIR-HSI in agricultural applications with a focus on seed quality features for agronomically important seeds. NIR-HSI seed phenotyping, describing sample sizes used for building high-accuracy calibration and prediction models for full or selected wavelengths of the NIR region, is explored. The molecular interpretation of absorbance bands in the NIR region is difficult; hence, this review offers important NIR absorbance band assignments that have been reported in literature. Opportunities for NIR-HSI seed phenotyping in forage grass seed are described and a step-by-step data-acquisition and analysis pipeline for the determination of seed quality in perennial ryegrass seeds is also presented.
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Leucker, Marlene, Mirwaes Wahabzada, Kristian Kersting, Madlaina Peter, Werner Beyer, Ulrike Steiner, Anne-Katrin Mahlein, and Erich-Christian Oerke. "Hyperspectral imaging reveals the effect of sugar beet quantitative trait loci on Cercospora leaf spot resistance." Functional Plant Biology 44, no. 1 (2017): 1. http://dx.doi.org/10.1071/fp16121.
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The quantitative resistance of sugar beet (Beta vulgaris L.) against Cercospora leaf spot (CLS) caused by Cercospora beticola (Sacc.) was characterised by hyperspectral imaging. Two closely related inbred lines, differing in two quantitative trait loci (QTL), which made a difference in disease severity of 1.1–1.7 on the standard scoring scale (1–9), were investigated under controlled conditions. The temporal and spatial development of CLS lesions on the two genotypes were monitored using a hyperspectral microscope. The lesion development on the QTL-carrying, resistant genotype was characterised by a fast and abrupt change in spectral reflectance, whereas it was slower and ultimately more severe on the genotype lacking the QTL. An efficient approach for clustering of hyperspectral signatures was adapted in order to reveal resistance characteristics automatically. The presented method allowed a fast and reliable differentiation of CLS dynamics and lesion composition providing a promising tool to improve resistance breeding by objective and precise plant phenotyping.
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Choudhury, Malini Roy, Jack Christopher, Armando Apan, Scott Chapman, Neal Menzies, and Yash Dang. "Integrated High-Throughput Phenotyping with High Resolution Multispectral, Hyperspectral and 3D Point Cloud Techniques for Screening Wheat Genotypes on Sodic Soils." Proceedings 36, no. 1 (April 8, 2020): 206. http://dx.doi.org/10.3390/proceedings2019036206.
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Wheat production in southern Queensland, Australia is adversely affected by soil sodicity. Crop phenotyping could be useful to improve productivity in such soils. This research focused on adapting high-throughput phenotyping of crop biophysical attributes to monitor crop health, nutrient deficiencies and plant moisture availability. We conducted an aerial and ground-based campaign during several wheat growing stages to capture crop information for 18 wheat genotypes at a moderately sodic site near Goondiwindi in southern Queensland. Three techniques were employed (multispectral, hyperspectral, and 3D point cloud) to monitor crop characteristics and predict biomass and yield. Spectral information and vegetation indices (VI) such as, normalized different vegetation index (NDVI), modified soil adjusted vegetation index (MSAVI), and leaf area index (LAI) were derived from multispectral imagery and compared with ground-based agronomic data for biomass, leaf area, and yield. Significant correlations were observed between NDVI and yield (R2 = 0.81), LAI (R2 = 0.74), and biomass (R2 = 0.65). Partial least square regression (PLS-R) modelling using hyperspectral spectroscopy data provided crop yield predictions that correlated significantly with observed yield (R2 = 0.65). The 3D point cloud technique was effective with comparison to in field manual measurements of crop architectural traits height and foliage cover (e.g., for height R2 = 0.73). For, this study multispectral techniques showed a greater potential to predict biomass and yield of wheat genotypes under moderately sodic soils than hyperspectral and 3D point cloud techniques. In future, the genotypes will be tested under more severely sodic soils to monitor crop performance and predicting yield.
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Liu, Huajian, Brooke Bruning, Trevor Garnett, and Bettina Berger. "The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat." Sensors 20, no. 16 (August 13, 2020): 4550. http://dx.doi.org/10.3390/s20164550.
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The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.
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Virlet, Nicolas, Kasra Sabermanesh, Pouria Sadeghi-Tehran, and Malcolm J. Hawkesford. "Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring." Functional Plant Biology 44, no. 1 (2017): 143. http://dx.doi.org/10.1071/fp16163.
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Current approaches to field phenotyping are laborious or permit the use of only a few sensors at a time. In an effort to overcome this, a fully automated robotic field phenotyping platform with a dedicated sensor array that may be accurately positioned in three dimensions and mounted on fixed rails has been established, to facilitate continual and high-throughput monitoring of crop performance. Employed sensors comprise of high-resolution visible, chlorophyll fluorescence and thermal infrared cameras, two hyperspectral imagers and dual 3D laser scanners. The sensor array facilitates specific growth measurements and identification of key growth stages with dense temporal and spectral resolution. Together, this platform produces a detailed description of canopy development across the crops entire lifecycle, with a high-degree of accuracy and reproducibility.
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Yang, Myongkyoon. "Physiological Disorder Diagnosis of Plant Leaves Based on Full-Spectrum Hyperspectral Images with Convolutional Neural Network." Horticulturae 8, no. 9 (September 19, 2022): 854. http://dx.doi.org/10.3390/horticulturae8090854.
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The prediction and early detection of physiological disorders based on the nutritional conditions and stress of plants are extremely vital for the growth and production of crops. High-throughput phenotyping is an effective nondestructive method to understand this, and numerous studies are being conducted with the development of convergence technology. This study analyzes physiological disorders in plant leaves using hyperspectral images and deep learning algorithms. Data on seven classes for various physiological disorders, including normal, prediction, and the appearance of symptom, were obtained for strawberries subjected to artificial treatment. The acquired hyperspectral images were used as input for a convolutional neural network algorithm without spectroscopic preprocessing. To determine the optimal model, several hyperparameter tuning and optimizer selection processes were performed. The Adam optimizer exhibited the best performance with an F1 score of ≥0.95. Moreover, the RMSProp optimizer exhibited slightly similar performance, confirming the potential for performance improvement. Thus, the novel possibility of utilizing hyperspectral images and deep learning algorithms for nondestructive and accurate analysis of the physiological disorders of plants was shown.
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Cheshkova, A. F. "A review of hyperspectral image analysis techniques for plant disease detection and identif ication." Vavilov Journal of Genetics and Breeding 26, no. 2 (April 5, 2022): 202–13. http://dx.doi.org/10.18699/vjgb-22-25.
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Plant diseases cause signif icant economic losses in agriculture around the world. Early detection, quantif ication and identif ication of plant diseases are crucial for targeted application of plant protection measures in crop production. Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. The analysis of the ref lection spectrum of plant tissue makes it possible to classify healthy and diseased plants, assess the severity of the disease, differentiate the types of pathogens, and identify the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. This review describes the basic principles of hyperspectral measurements and different types of available hyperspectral sensors. Possible applications of hyperspectral sensors and platforms on different scales for diseases diagnosis are discussed and evaluated. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which make it possible to simultaneously evaluate both physiological and morphological parameters. The review describes the main steps of the hyperspectral data analysis process: image acquisition and preprocessing; data extraction and processing; modeling and analysis of data. The algorithms and methods applied at each step are mainly summarized. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation and identif ication of diseases, estimation of disease severity, phenotyping of disease resistance of genotypes. A comprehensive review of scientif ic publications on the diagnosis of plant diseases highlights the benef its of hyperspectral technologies in investigating interactions between plants and pathogens at various measurement scales. Despite the encouraging progress made over the past few decades in monitoring plant diseases based on hyperspectral technologies, some technical problems that make these methods diff icult to apply in practice remain unresolved. The review is concluded with an overview of problems and prospects of using new technologies in agricultural production.
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Islam ElManawy, Ahmed, Dawei Sun, Alwaseela Abdalla, Yueming Zhu, and Haiyan Cen. "HSI-PP: A flexible open-source software for hyperspectral imaging-based plant phenotyping." Computers and Electronics in Agriculture 200 (September 2022): 107248. http://dx.doi.org/10.1016/j.compag.2022.107248.
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Yendrek, Craig R., Tiago Tomaz, Christopher M. Montes, Youyuan Cao, Alison M. Morse, Patrick J. Brown, Lauren M. McIntyre, Andrew D. B. Leakey, and Elizabeth A. Ainsworth. "High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance." Plant Physiology 173, no. 1 (November 15, 2016): 614–26. http://dx.doi.org/10.1104/pp.16.01447.
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Moghimi, Ali, Ce Yang, and Peter M. Marchetto. "Ensemble Feature Selection for Plant Phenotyping: A Journey From Hyperspectral to Multispectral Imaging." IEEE Access 6 (2018): 56870–84. http://dx.doi.org/10.1109/access.2018.2872801.
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Rehman, Tanzeel U., and Jian Jin. "Deep adversarial domain adaptation for hyperspectral calibration model transfer among plant phenotyping systems." Biosystems Engineering 224 (December 2022): 246–58. http://dx.doi.org/10.1016/j.biosystemseng.2022.10.016.
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Ma, Dongdong, Tanzeel U. Rehman, Libo Zhang, Hideki Maki, Mitchell R. Tuinstra, and Jian Jin. "Modeling of Diurnal Changing Patterns in Airborne Crop Remote Sensing Images." Remote Sensing 13, no. 9 (April 29, 2021): 1719. http://dx.doi.org/10.3390/rs13091719.
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Airborne remote sensing technologies have been widely applied in field crop phenotyping. However, the quality of current remote sensing data suffers from significant diurnal variances. The severity of the diurnal issue has been reported in various plant phenotyping studies over the last four decades, but there are limited studies on the modeling of the diurnal changing patterns that allow people to precisely predict the level of diurnal impacts. In order to comprehensively investigate the diurnal variability, it is necessary to collect time series field images with very high sampling frequencies, which has been difficult. In 2019, Purdue agricultural (Ag) engineers deployed their first field visible to near infrared (VNIR) hyperspectral gantry platform, which is capable of repetitively imaging the same field plots every 2.5 min. A total of 8631 hyperspectral images of the same field were collected for two genotypes of corn plants from the vegetative stage V4 to the reproductive stage R1 in the 2019 growing season. The analysis of these images showed that although the diurnal variability is very significant for almost all the image-derived phenotyping features, the diurnal changes follow stable patterns. This makes it possible to predict the imaging drifts by modeling the changing patterns. This paper reports detailed diurnal changing patterns for several selected plant phenotyping features such as Normalized Difference Vegetation Index (NDVI), Relative Water Content (RWC), and single spectrum bands. For example, NDVI showed a repeatable V-shaped diurnal pattern, which linearly drops by 0.012 per hour before the highest sun angle and increases thereafter by 0.010 per hour. The different diurnal changing patterns in different nitrogen stress treatments, genotypes and leaf stages were also compared and discussed. With the modeling results of this work, Ag remote sensing users will be able to more precisely estimate the deviation/change of crop feature predictions caused by the specific imaging time of the day. This will help people to more confidently decide on the acceptable imaging time window during a day. It can also be used to calibrate/compensate the remote sensing result against the time effect.
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Park, Eunsoo, Yun-Soo Kim, Mohammad Kamran Omari, Hyun-Kwon Suh, Mohammad Akbar Faqeerzada, Moon S. Kim, Insuck Baek, and Byoung-Kwan Cho. "High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng (Panax ginseng Meyer) Using a Hyperspectral Reflectance Image." Sensors 21, no. 16 (August 21, 2021): 5634. http://dx.doi.org/10.3390/s21165634.
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Panax ginseng has been used as a traditional medicine to strengthen human health for centuries. Over the last decade, significant agronomical progress has been made in the development of elite ginseng cultivars, increasing their production and quality. However, as one of the significant environmental factors, heat stress remains a challenge and poses a significant threat to ginseng plants’ growth and sustainable production. This study was conducted to investigate the phenotype of ginseng leaves under heat stress using hyperspectral imaging (HSI). A visible/near-infrared (Vis/NIR) and short-wave infrared (SWIR) HSI system were used to acquire hyperspectral images for normal and heat stress-exposed plants, showing their susceptibility (Chunpoong) and resistibility (Sunmyoung and Sunil). The acquired hyperspectral images were analyzed using the partial least squares-discriminant analysis (PLS-DA) technique, combining the variable importance in projection and successive projection algorithm methods. The correlation of each group was verified using linear discriminant analysis. The developed models showed 12 bands over 79.2% accuracy in Vis/NIR and 18 bands with over 98.9% accuracy at SWIR in validation data. The constructed beta-coefficient allowed the observation of the key wavebands and peaks linked to the chlorophyll, nitrogen, fatty acid, sugar and protein content regions, which differentiated normal and stressed plants. This result shows that the HSI with the PLS-DA technique significantly differentiated between the heat-stressed susceptibility and resistibility of ginseng plants with high accuracy.
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Dilmurat, Kamila, Vasit Sagan, Maitiniyazi Maimaitijiang, Stephen Moose, and Felix B. Fritschi. "Estimating Crop Seed Composition Using Machine Learning from Multisensory UAV Data." Remote Sensing 14, no. 19 (September 25, 2022): 4786. http://dx.doi.org/10.3390/rs14194786.
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The pre-harvest estimation of seed composition from standing crops is imperative for field management practices and plant phenotyping. This paper presents for the first time the potential of Unmanned Aerial Vehicles (UAV)-based high-resolution hyperspectral and LiDAR data acquired from in-season stand crops for estimating seed protein and oil compositions of soybean and corn using multisensory data fusion and automated machine learning. UAV-based hyperspectral and LiDAR data was collected during the growing season (reproductive stage five (R5)) of 2020 over a soybean test site near Columbia, Missouri and a cornfield at Urbana, Illinois, USA. Canopy spectral and texture features were extracted from hyperspectral imagery, and canopy structure features were derived from LiDAR point clouds. The extracted features were then used as input variables for automated machine-learning methods available with the H2O Automated Machine-Learning framework (H2O-AutoML). The results presented that: (1) UAV hyperspectral imagery can successfully predict both the protein and oil of soybean and corn with moderate accuracies; (2) canopy structure features derived from LiDAR point clouds yielded slightly poorer estimates of crop-seed composition compared to the hyperspectral data; (3) regardless of machine-learning methods, the combination of hyperspectral and LiDAR data outperformed the predictions using a single sensor alone, with an R2 of 0.79 and 0.67 for corn protein and oil and R2 of 0.64 and 0.56 for soybean protein and oil; and (4) the H2O-AutoML framework was found to be an efficient strategy for machine-learning-based data-driven model building. Among the specific regression methods evaluated in this study, the Gradient Boosting Machine (GBM) and Deep Neural Network (NN) exhibited superior performance to other methods. This study reveals opportunities and limitations for multisensory UAV data fusion and automated machine learning in estimating crop-seed composition.
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Mahlein, A. K., M. T. Kuska, J. Behmann, G. Polder, and A. Walter. "Hyperspectral Sensors and Imaging Technologies in Phytopathology: State of the Art." Annual Review of Phytopathology 56, no. 1 (August 25, 2018): 535–58. http://dx.doi.org/10.1146/annurev-phyto-080417-050100.
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Plant disease detection represents a tremendous challenge for research and practical applications. Visual assessment by human raters is time-consuming, expensive, and error prone. Disease rating and plant protection need new and innovative techniques to address forthcoming challenges and trends in agricultural production that require more precision than ever before. Within this context, hyperspectral sensors and imaging techniques—intrinsically tied to efficient data analysis approaches—have shown an enormous potential to provide new insights into plant-pathogen interactions and for the detection of plant diseases. This article provides an overview of hyperspectral sensors and imaging technologies for assessing compatible and incompatible plant-pathogen interactions. Within the progress of digital technologies, the vision, which is increasingly discussed in the society and industry, includes smart and intuitive solutions for assessing plant features in plant phenotyping or for making decisions on plant protection measures in the context of precision agriculture.
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Maimaitijiang, M., V. Sagan, S. Bhadra, C. Nguyen, T. C. Mockler, and N. Shakoor. "A FULLY AUTOMATED AND FAST APPROACH FOR CANOPY COVER ESTIMATION USING SUPER HIGH-RESOLUTION REMOTE SENSING IMAGERY." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2021 (June 17, 2021): 219–26. http://dx.doi.org/10.5194/isprs-annals-v-3-2021-219-2021.
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Abstract. Canopy cover is a key agronomic variable for understanding plant growth and crop development status. Estimation of canopy cover rapidly and accurately through a fully automated manner is significant with respect to high throughput plant phenotyping. In this work, we propose a simple, robust and fully automated approach, namely a rule-based method, that leverages the unique spectral pattern of green vegetation at visible (VIS) and near-infrared red (NIR) spectra regions to distinguish the green vegetation from background (i.e., soil, plant residue, non-photosynthetic vegetation leaves etc.), and then derive canopy cover. The proposed method was applied to high-resolution hyperspectral and multispectral imagery collected from gantry-based scanner and Unmanned Aerial Vehicle (UAV) platforms to estimate canopy cover. Additionally, machine learning methods, i.e., Support Vector Machine (SVM) and Random Forest (RF) were also employed as bench mark methods. The results show that: the rule-based method demonstrated promising classification accuracies that are comparable to SVM and RF for both hyperspectral and multispectral datasets. Although the rule-based method is more sensitive to mixed pixels and shaded canopy region, which potentially resulted in classification errors and underestimation of canopy cover in some cases; it showed better performance to detect smaller leaves than SVM and RF. Most importantly, the rule-based method substantially outperformed machine learning methods with respect to processing speed, indicating its greater potential for high-throughput plant phenotyping applications.
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Moghimi, Ali, Ce Yang, and James A. Anderson. "Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat." Computers and Electronics in Agriculture 172 (May 2020): 105299. http://dx.doi.org/10.1016/j.compag.2020.105299.
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Liu, Huajian, Brooke Bruning, Trevor Garnett, and Bettina Berger. "Hyperspectral imaging and 3D technologies for plant phenotyping: From satellite to close-range sensing." Computers and Electronics in Agriculture 175 (August 2020): 105621. http://dx.doi.org/10.1016/j.compag.2020.105621.
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Rehman, Tanzeel U., Libo Zhang, Dongdong Ma, Liangju Wang, and Jian Jin. "Calibration transfer across multiple hyperspectral imaging-based plant phenotyping systems: I – Spectral space adjustment." Computers and Electronics in Agriculture 176 (September 2020): 105685. http://dx.doi.org/10.1016/j.compag.2020.105685.
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Lin, Meng-Yang, Valerie Lynch, Dongdong Ma, Hideki Maki, Jian Jin, and Mitchell Tuinstra. "Multi-Species Prediction of Physiological Traits with Hyperspectral Modeling." Plants 11, no. 5 (March 1, 2022): 676. http://dx.doi.org/10.3390/plants11050676.
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Lack of high-throughput phenotyping is a bottleneck to breeding for abiotic stress tolerance in crop plants. Efficient and non-destructive hyperspectral imaging can quantify plant physiological traits under abiotic stresses; however, prediction models generally are developed for few genotypes of one species, limiting the broader applications of this technology. Therefore, the objective of this research was to explore the possibility of developing cross-species models to predict physiological traits (relative water content and nitrogen content) based on hyperspectral reflectance through partial least square regression for three genotypes of sorghum (Sorghum bicolor (L.) Moench) and six genotypes of corn (Zea mays L.) under varying water and nitrogen treatments. Multi-species models were predictive for the relative water content of sorghum and corn (R2 = 0.809), as well as for the nitrogen content of sorghum and corn (R2 = 0.637). Reflectances at 506, 535, 583, 627, 652, 694, 722, and 964 nm were responsive to changes in the relative water content, while the reflectances at 486, 521, 625, 680, 699, and 754 nm were responsive to changes in the nitrogen content. High-throughput hyperspectral imaging can be used to predict physiological status of plants across genotypes and some similar species with acceptable accuracy.
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Dilmurat, K., V. Sagan, and S. Moose. "AI-DRIVEN MAIZE YIELD FORECASTING USING UNMANNED AERIAL VEHICLE-BASED HYPERSPECTRAL AND LIDAR DATA FUSION." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 193–99. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-193-2022.
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Abstract. The increased availability of remote sensing data combined with the wide-ranging applicability of artificial intelligence has enabled agriculture stakeholders to monitor changes in crops and their environment frequently and accurately. Applying cutting-edge technology in precision agriculture also enabled the prediction of pre-harvest yield from standing crop signals. Forecasting grain yield from standing crops benefits high-throughput plant phenotyping and agriculture policymaking with information on where crop production is likely to decline. Advanced developments in the Unmanned Aerial Vehicle (UAV) platform and sensor technologies aided high-resolution spatial, spectral, and structural data collection processes at a relatively lower cost and shorter time. In this study, UAV-based LiDAR and hyperspectral images were collected during the growing season of 2020 over a cornfield near Urbana Champaign, Illinois, USA. Hyperspectral imagery-based canopy spectral & texture features and LiDAR point cloud-based canopy structure features were extracted and, along with their combination, were used as inputs for maize yield prediction under the H2O Automated Machine Learning framework (H2O-AutoML). The research results are (1) UAV Hyperspectral imagery can successfully predict maize yield with relatively decent accuracies; additionally, LiDAR point cloud-based canopy structure features are found to be significant indicators for maize yield prediction, which produced slightly poorer, yet comparable results to hyperspectral data; (2) regardless of machine learning methods, integration of hyperspectral imagery-based canopy spectral and texture information with LiDAR-based canopy structure features outperformed the predictions when using a single sensor alone; (3)the H2O-AutoML framework presented to be an efficient strategy for machine learning-based data-driven model building.
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Zhang, Libo, Jian Jin, Liangju Wang, Tanzeel U. Rehman, and Mark T. Gee. "Elimination of Leaf Angle Impacts on Plant Reflectance Spectra Using Fusion of Hyperspectral Images and 3D Point Clouds." Sensors 23, no. 1 (December 21, 2022): 44. http://dx.doi.org/10.3390/s23010044.
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During recent years, hyperspectral imaging technologies have been widely applied in agriculture to evaluate complex plant physiological traits such as leaf moisture content, nutrient level, and disease stress. A critical component of this technique is white referencing used to remove the effect of non-uniform lighting intensity in different wavelengths on raw hyperspectral images. However, a flat white tile cannot accurately reflect the lighting intensity variance on plant leaves, since the leaf geometry (e.g., tilt angles) and its interaction with the illumination severely impact plant reflectance spectra and vegetation indices such as the normalized difference vegetation index (NDVI). In this research, the impacts of leaf angles on plant reflectance spectra were summarized, and an improved image calibration model using the fusion of leaf hyperspectral images and 3D point clouds was built. Corn and soybean leaf samples were imaged at different tilt angles and orientations using an indoor desktop hyperspectral imaging system and analyzed for differences in the NDVI values. The results showed that the leaf’s NDVI largely changed with angles. The changing trends with angles differed between the two species. Using measurements of leaf tilt angle and orientation obtained from the 3D point cloud data taken simultaneously with the hyperspectral images, a support vector regression (SVR) model was successfully developed to calibrate the NDVI values of pixels at different angles on a leaf to a same standard as if the leaf was laid flat on a horizontal surface. The R-squared values between the measured and predicted leaf angle impacts were 0.76 and 0.94 for corn and soybean, respectively. This method has a potential to be used in any general plant imaging systems to improve the phenotyping quality.
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Bhadra, S., V. Sagan, C. Nguyen, M. Braud, A. L. Eveland, and T. C. Mockler. "AUTOMATIC EXTRACTION OF SOLAR AND SENSOR IMAGING GEOMETRY FROM UAV-BORNE PUSH-BROOM HYPERSPECTRAL CAMERA." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 131–37. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-131-2022.
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Abstract. Calculating solar-sensor zenith and azimuth angles for hyperspectral images collected by UAVs are important in terms of conducting bi-directional reflectance function (BRDF) correction or radiative transfer modeling-based applications in remote sensing. These applications are even more necessary to perform high-throughput phenotyping and precision agriculture tasks. This study demonstrates an automated Python framework that can calculate the solar-sensor zenith and azimuth angles for a push-broom hyperspectral camera equipped in a UAV. First, the hyperspectral images were radiometrically and geometrically corrected. Second, the high-precision Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data for the flight path was extracted and corresponding UAV points for each pixel were identified. Finally, the angles were calculated using spherical trigonometry and linear algebra. The results show that the solar zenith angle (SZA) and solar azimuth angle (SAA) calculated by our method provided higher precision angular values compared to other available tools. The viewing zenith angle (VZA) was lower near the flight path and higher near the edge of the images. The viewing azimuth angle (VAA) pattern showed higher values to the left and lower values to the right side of the flight line. The methods described in this study is easily reproducible to other study areas and applications.
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Proshkin, Yuriy A. "Computer Vision and Spectral Analysis Technologies for Non-Invasive Plant Studying." Elektrotekhnologii i elektrooborudovanie v APK 67, no. 2 (June 24, 2020): 107–14. http://dx.doi.org/10.22314/2658-4859-2020-67-2-107-114.
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Computer vision and spectral analysis of digital images are technologies that allow the use of automated and robotic systems for non-invasive plant studying, production and harvesting of agricultural products, phenotyping and selection of new plant species. (Research purpose) The research purpose is in analyzing the application of modern digital non-invasive methods of plant research using computer (technical) vision and prospects for their implementation. (Materials and methods) Authors have reviewed the works on the use of non-invasive methods for obtaining information about the state of plants. The article presents classification and analyze of the collected materials according to the criteria for collecting and analyzing digital data, the scope of application and prospects for implementation. Authors used the methods of a systematic approach to the research problem. (Results and discussion) The article presents the main directions of using computer vision systems and digital image analysis. The use of computer vision technologies in plant phenotyping and selection reduces the labor cost of research, allowing the formation of digital databases with a clear structure and classification by morphological features. It was found that the introduction of neural networks in the process of digital image processing increases the accuracy of plant recognition up to 99.9 percent, and infectious diseases up to 80 percent on average. (Conclusions) The article shows that in studies using hyperspectral optical cameras and sensors are used cameras with an optical range from 400 to 1000 nanometers, and in rare cases, hyperspectral camera systems with a total coverage of the optical range from 350 to 2000 nanometers. These optical systems are mainly installed on unmanned aerial vehicles to determine vegetation indices, foci of infection and the fertility of agricultural fields. It was found that computer vision systems with hyperspectral cameras could be used in conjunction with fluorescent plant markers, which makes it possible to solve complex problems of crop recognition without involving computational resources.
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Zhang, Yi, Yizhe Yang, Qinwei Zhang, Runqing Duan, Junqi Liu, Yuchu Qin, and Xianzhi Wang. "Toward Multi-Stage Phenotyping of Soybean with Multimodal UAV Sensor Data: A Comparison of Machine Learning Approaches for Leaf Area Index Estimation." Remote Sensing 15, no. 1 (December 20, 2022): 7. http://dx.doi.org/10.3390/rs15010007.
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Leaf Area Index (LAI) is an important parameter which can be used for crop growth monitoring and yield estimation. Many studies have been carried out to estimate LAI with remote sensing data obtained by sensors mounted on Unmanned Aerial Vehicles (UAVs) in major crops; however, most of the studies used only a single type of sensor, and the comparative study of different sensors and sensor combinations in the model construction of LAI was rarely reported, especially in soybean. In this study, three types of sensors, i.e., hyperspectral, multispectral, and LiDAR, were used to collect remote sensing data at three growth stages in soybean. Six typical machine learning algorithms, including Unary Linear Regression (ULR), Multiple Linear Regression (MLR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Back Propagation (BP), were used to construct prediction models of LAI. The results indicated that the hyperspectral and LiDAR data did not significantly improve the prediction accuracy of LAI. Comparison of different sensors and sensor combinations showed that the fusion of the hyperspectral and multispectral data could significantly improve the predictive ability of the models, and among all the prediction models constructed by different algorithms, the prediction model built by XGBoost based on multimodal data showed the best performance. Comparison of the models for different growth stages showed that the XGBoost-LAI model for the flowering stage and the universal models of the XGBoost-LAI and RF-LAI for three growth stages showed the best performances. The results of this study might provide some ideas for the accurate estimation of LAI, and also provide novel insights toward high-throughput phenotyping of soybean with multi-modal remote sensing data.
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Qiu, Wei, and Jian Jin. "MISIRoot: A Robotic, Minimally Invasive, in Situ Imaging System for Plant Root Phenotyping." Transactions of the ASABE 64, no. 5 (2021): 1647–58. http://dx.doi.org/10.13031/trans.14306.
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HighlightsA non-destructive, in situ, and low-cost root phenotyping system was developed.The system can collect color images and 3D cloud points of corn roots in soil.When tested in a greenhouse, the scanning process did not cause significant disturbance of corn plants.The results showed significant differences in root growth for different watering treatments and growth stages.Abstract. Plant root phenotyping technologies play an important role in breeding, plant protection, and other plant science research projects. Root phenotyping researchers urgently need technologies that are low-cost, in situ, non-destructive to roots, and suitable for the natural soil environment. Many recently developed root phenotyping methods, such as minirhizotron, X-CT, and MRI scanners, have unique advantages in observing plant roots, but they also have disadvantages and cannot meet all the critical requirements simultaneously. This study focused on the development of a new plant root phenotyping robot, called MISIRoot, that is minimally invasive and works in situ in natural soil. The MISIRoot system mainly consists of an industrial-level robotic arm, a miniature camera with lighting, a plant pot holding platform, and image processing software for root recognition and feature extraction. MISIRoot can acquire high-resolution color images of roots in soil with minimal disturbance to the roots and measure the roots’ three-dimensional (3D) structure with an accuracy of 0.1 mm. In tests, well-watered and drought-stressed groups of corn plants were measured with MISIRoot at the V3, V4, and V5 growth stages. The system successfully acquired RGB color images of the roots and 3D point cloud data containing the locations of the detected roots. The plants measured with MISIRoot and the plants not measured (control) were carefully compared with the results from a hyperspectral imaging facility (reference). No significant differences were found between the two groups of plants at different growth stages. Keywords: 3D point cloud, Low-cost phenotyping, Minimally invasive root measurement, Plant root phenotyping, Robotic arm application, Root imaging.
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Behmann, J., P. Schmitter, J. Steinrücken, and L. Plümer. "Ordinal classification for efficient plant stress prediction in hyperspectral data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 29–36. http://dx.doi.org/10.5194/isprsarchives-xl-7-29-2014.
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Detection of crop stress from hyperspectral images is of high importance for breeding and precision crop protection. However, the continuous monitoring of stress in phenotyping facilities by hyperspectral imagers produces huge amounts of uninterpreted data. In order to derive a stress description from the images, interpreting algorithms with high prediction performance are required. Based on a static model, the local stress state of each pixel has to be predicted. Due to the low computational complexity, linear models are preferable. <br><br> In this paper, we focus on drought-induced stress which is represented by discrete stages of ordinal order. We present and compare five methods which are able to derive stress levels from hyperspectral images: One-vs.-one Support Vector Machine (SVM), one-vs.-all SVM, Support Vector Regression (SVR), Support Vector Ordinal Regression (SVORIM) and Linear Ordinal SVM classification. The methods are applied on two data sets - a real world set of drought stress in single barley plants and a simulated data set. It is shown, that Linear Ordinal SVM is a powerful tool for applications which require high prediction performance under limited resources. It is significantly more efficient than the one-vs.-one SVM and even more efficient than the less accurate one-vs.-all SVM. Compared to the very compact SVORIM model, it represents the senescence process much more accurate.
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Koh, Joshua C. O., Bikram P. Banerjee, German Spangenberg, and Surya Kant. "Automated hyperspectral vegetation index derivation using a hyperparameter optimisation framework for high‐throughput plant phenotyping." New Phytologist 233, no. 6 (January 20, 2022): 2659–70. http://dx.doi.org/10.1111/nph.17947.
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Tao, Mingzhu, Xiulin Bai, Jinnuo Zhang, Yuzhen Wei, and Yong He. "Time-Series Monitoring of Transgenic Maize Seedlings Phenotyping Exhibiting Glyphosate Tolerance." Processes 10, no. 11 (October 26, 2022): 2206. http://dx.doi.org/10.3390/pr10112206.
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Glyphosate is a widely used nonselective herbicide. Probing the glyphosate tolerance mechanism is necessary for the screening and development of resistant cultivars. In this study, a hyperspectral image was used to develop a more robust leaf chlorophyll content (LCC) prediction model based on different datasets to finally analyze the response of LCC to glyphosate-stress. Chlorophyll a fluorescence (ChlF) was used to dynamically monitor the photosynthetic physiological response of transgenic glyphosate-resistant and wild glyphosate-sensitive maize seedlings and applying chemometrics methods to extract time-series features to screen resistant cultivars. Six days after glyphosate treatment, glyphosate-sensitive seedlings exhibited significant changes in leaf reflection and photosynthetic activity. By updating source domain and transfer component analysis, LCC prediction model performance was improved effectively (the coefficient of determination value increased from 0.65 to 0.84). Based on the predicted LCC and ChlF data, glyphosate-sensitive plants are too fragile to protect themselves from glyphosate stress, while glyphosate-resistant plants were able to maintain normal photosynthetic physiological activity. JIP-test parameters, φE0, VJ, ψE0, and M0, were used to indicate the degree of plant damage caused by glyphosate. This study constructed a transferable model for LCC monitoring to finally evaluate glyphosate tolerance in a time-series manner and verified the feasibility of ChlF in screening glyphosate-resistant cultivars.
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Banerjee, Bikram P., Sameer Joshi, Emily Thoday-Kennedy, Raj K. Pasam, Josquin Tibbits, Matthew Hayden, German Spangenberg, and Surya Kant. "Corrigendum to: High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response." Journal of Experimental Botany 72, no. 13 (May 24, 2021): 5093. http://dx.doi.org/10.1093/jxb/erab126.
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Mangalraj, P., and Byoung-Kwan Cho. "Recent trends and advances in hyperspectral imaging techniques to estimate solar induced fluorescence for plant phenotyping." Ecological Indicators 137 (April 2022): 108721. http://dx.doi.org/10.1016/j.ecolind.2022.108721.
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