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Wang Y, Ou X, He HJ, Kamruzzaman M. Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review. Food Chem X 2024; 21:101235. [PMID: 38420503 PMCID: PMC10900407 DOI: 10.1016/j.fochx.2024.101235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/07/2024] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
The potential of hyperspectral imaging technology (HIT) for the determination of physicochemical and nutritional components, evaluation of fungal/mycotoxins contamination, wheat varieties classification, identification of non-mildew-damaged wheat kernels, as well as detection of flour adulteration is comprehensively illustrated and reviewed. The latest findings (2018-2023) of HIT in wheat quality evaluation through internal and external attributes are compared and summarized in detail. The limitations and challenges of HIT to improve assessment accuracy are clearly described. Additionally, various practical recommendations and strategies for the potential application of HIT are highlighted. The future trends and prospects of HIT in evaluating wheat quality are also mentioned. In conclusion, HIT stands as a cutting-edge technology with immense potential for revolutionizing wheat quality evaluation. As advancements in HIT continue, it will play a pivotal role in shaping the future of wheat quality assessment and contributing to a more sustainable and efficient food supply chain.
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Affiliation(s)
- Yuling Wang
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Xingqi Ou
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Hong-Ju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
| | - Mohammed Kamruzzaman
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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Camenzind MP, Yu K. Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering. FRONTIERS IN PLANT SCIENCE 2024; 14:1214931. [PMID: 38235203 PMCID: PMC10791776 DOI: 10.3389/fpls.2023.1214931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 11/29/2023] [Indexed: 01/19/2024]
Abstract
High throughput field phenotyping techniques employing multispectral cameras allow extracting a variety of variables and features to predict yield and yield related traits, but little is known about which types of multispectral features are optimal to forecast yield potential in the early growth phase. In this study, we aim to identify multispectral features that are able to accurately predict yield and aid in variety classification at different growth stages throughout the season. Furthermore, we hypothesize that texture features (TFs) are more suitable for variety classification than for yield prediction. Throughout 2021 and 2022, a trial involving 19 and 18 European wheat varieties, respectively, was conducted. Multispectral images, encompassing visible, Red-edge, and near-infrared (NIR) bands, were captured at 19 and 22 time points from tillering to harvest using an unmanned aerial vehicle (UAV) in the first and second year of trial. Subsequently, orthomosaic images were generated, and various features were extracted, including single-band reflectances, vegetation indices (VI), and TFs derived from a gray level correlation matrix (GLCM). The performance of these features in predicting yield and classifying varieties at different growth stages was assessed using random forest models. Measurements during the flowering stage demonstrated superior performance for most features. Specifically, Red reflectance achieved a root mean square error (RMSE) of 52.4 g m-2 in the first year and 64.4 g m-2 in the second year. The NDRE VI yielded the most accurate predictions with an RMSE of 49.1 g m-2 and 60.6 g m-2, respectively. Moreover, TFs such as CONTRAST and DISSIMILARITY displayed the best performance in predicting yield, with RMSE values of 55.5 g m-2 and 66.3 g m-2 across the two years of trial. Combining data from different dates enhanced yield prediction and stabilized predictions across dates. TFs exhibited high accuracy in classifying low and high-yielding varieties. The CORRELATION feature achieved an accuracy of 88% in the first year, while the HOMOGENEITY feature reached 92% accuracy in the second year. This study confirms the hypothesis that TFs are more suitable for variety classification than for yield prediction. The results underscore the potential of TFs derived from multispectral images in early yield prediction and varietal classification, offering insights for HTP and precision agriculture alike.
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Affiliation(s)
- Moritz Paul Camenzind
- Precision Agriculture Lab, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Kang Yu
- Precision Agriculture Lab, School of Life Sciences, Technical University of Munich, Freising, Germany
- World Agricultural Systems Center (Hans Eisenmann-Forum for Agricultural Sciences – HEF), Technical University of Munich, Freising, Germany
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Pokhrel A, Virk S, Snider JL, Vellidis G, Hand LC, Sintim HY, Parkash V, Chalise DP, Lee JM, Byers C. Estimating yield-contributing physiological parameters of cotton using UAV-based imagery. FRONTIERS IN PLANT SCIENCE 2023; 14:1248152. [PMID: 37794937 PMCID: PMC10546020 DOI: 10.3389/fpls.2023.1248152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/29/2023] [Indexed: 10/06/2023]
Abstract
Lint yield in cotton is governed by light intercepted by the canopy (IPAR), radiation use efficiency (RUE), and harvest index (HI). However, the conventional methods of measuring these yield-governing physiological parameters are labor-intensive, time-consuming and requires destructive sampling. This study aimed to explore the use of low-cost and high-resolution UAV-based RGB and multispectral imagery 1) to estimate fraction of IPAR (IPARf), RUE, and biomass throughout the season, 2) to estimate lint yield using the cotton fiber index (CFI), and 3) to determine the potential use of biomass and lint yield models for estimating cotton HI. An experiment was conducted during the 2021 and 2022 growing seasons in Tifton, Georgia, USA in randomized complete block design with five different nitrogen treatments. Different nitrogen treatments were applied to generate substantial variability in canopy development and yield. UAV imagery was collected bi-weekly along with light interception and biomass measurements throughout the season, and 20 different vegetation indices (VIs) were computed from the imagery. Generalized linear regression was performed to develop models using VIs and growing degree days (GDDs). The IPARf models had R2 values ranging from 0.66 to 0.90, and models based on RVI and RECI explained the highest variation (93%) in IPARf during cross-validation. Similarly, cotton above-ground biomass was best estimated by models from MSAVI and OSAVI. Estimation of RUE using actual biomass measurement and RVI-based IPARf model was able to explain 84% of variation in RUE. CFI from UAV-based RGB imagery had strong relationship (R2 = 0.69) with machine harvested lint yield. The estimated HI from CFI-based lint yield and MSAVI-based biomass models was able to explain 40 to 49% of variation in measured HI for the 2022 growing season. The models developed to estimate the yield-contributing physiological parameters in cotton showed low to strong performance, with IPARf and above-ground biomass having greater prediction accuracy. Future studies on accurate estimation of lint yield is suggested for precise cotton HI prediction. This study is the first attempt of its kind and the results can be used to expand and improve research on predicting functional yield drivers of cotton.
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Affiliation(s)
- Amrit Pokhrel
- Department of Crop and Soil Sciences, University of Georgia, Tifton, GA, United States
| | - Simerjeet Virk
- Department of Crop and Soil Sciences, University of Georgia, Tifton, GA, United States
| | - John L. Snider
- Department of Crop and Soil Sciences, University of Georgia, Tifton, GA, United States
| | - George Vellidis
- Department of Crop and Soil Sciences, University of Georgia, Tifton, GA, United States
| | - Lavesta C. Hand
- Department of Crop and Soil Sciences, University of Georgia, Tifton, GA, United States
| | - Henry Y. Sintim
- Department of Crop and Soil Sciences, University of Georgia, Tifton, GA, United States
| | - Ved Parkash
- Department of Crop and Soil Sciences, University of Georgia, Tifton, GA, United States
| | - Devendra P. Chalise
- Department of Crop and Soil Sciences, University of Georgia, Tifton, GA, United States
| | - Joshua M. Lee
- Department of Crop and Soil Sciences, University of Georgia, Tifton, GA, United States
| | - Coleman Byers
- College of Engineering, University of Georgia, Athens, GA, United States
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Ganeva D, Roumenina E, Dimitrov P, Gikov A, Jelev G, Dyulgenova B, Valcheva D, Bozhanova V. Remotely Sensed Phenotypic Traits for Heritability Estimates and Grain Yield Prediction of Barley Using Multispectral Imaging from UAVs. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115008. [PMID: 37299735 DOI: 10.3390/s23115008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/12/2023] [Accepted: 05/20/2023] [Indexed: 06/12/2023]
Abstract
This study tested the potential of parametric and nonparametric regression modeling utilizing multispectral data from two different unoccupied aerial vehicles (UAVs) as a tool for the prediction of and indirect selection of grain yield (GY) in barley breeding experiments. The coefficient of determination (R2) of the nonparametric models for GY prediction ranged between 0.33 and 0.61 depending on the UAV and flight date, where the highest value was achieved with the DJI Phantom 4 Multispectral (P4M) image from 26 May (milk ripening). The parametric models performed worse than the nonparametric ones for GY prediction. Independent of the retrieval method and UAV, GY retrieval was more accurate in milk ripening than dough ripening. The leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fraction vegetation cover (fCover), and leaf chlorophyll content (LCC) were modeled at milk ripening using nonparametric models with the P4M images. A significant effect of the genotype was found for the estimated biophysical variables, which was referred to as remotely sensed phenotypic traits (RSPTs). Measured GY heritability was lower, with a few exceptions, compared to the RSPTs, indicating that GY was more environmentally influenced than the RSPTs. The moderate to strong genetic correlation of the RSPTs to GY in the present study indicated their potential utility as an indirect selection approach to identify high-yield genotypes of winter barley.
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Affiliation(s)
- Dessislava Ganeva
- Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Eugenia Roumenina
- Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Petar Dimitrov
- Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Alexander Gikov
- Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Georgi Jelev
- Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | | | - Darina Valcheva
- Institute of Agriculture, Agriculture Academy, 8400 Karnobat, Bulgaria
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Martínez-Peña R, Schlereth A, Höhne M, Encke B, Morcuende R, Nieto-Taladriz MT, Araus JL, Aparicio N, Vicente R. Source-Sink Dynamics in Field-Grown Durum Wheat Under Contrasting Nitrogen Supplies: Key Role of Non-Foliar Organs During Grain Filling. FRONTIERS IN PLANT SCIENCE 2022; 13:869680. [PMID: 35574116 PMCID: PMC9100808 DOI: 10.3389/fpls.2022.869680] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 03/31/2022] [Indexed: 05/08/2023]
Abstract
The integration of high-throughput phenotyping and metabolic approaches is a suitable strategy to study the genotype-by-environment interaction and identify novel traits for crop improvement from canopy to an organ level. Our aims were to study the phenotypic and metabolic traits that are related to grain yield and quality at canopy and organ levels, with a special focus on source-sink coordination under contrasting N supplies. Four modern durum wheat varieties with contrasting grain yield were grown in field conditions under two N fertilization levels in north-eastern Spain. We evaluated canopy vegetation indices taken throughout the growing season, physiological and metabolic traits in different photosynthetic organs (flag leaf blade, sheath, peduncle, awn, glume, and lemma) at anthesis and mid-grain filling stages, and agronomic and grain quality traits at harvest. Low N supply triggered an imbalance of C and N coordination at the whole plant level, leading to a reduction of grain yield and nutrient composition. The activities of key enzymes in C and N metabolism as well as the levels of photoassimilates showed that each organ plays an important role during grain filling, some with a higher photosynthetic capacity, others for nutrient storage for later stages of grain filling, or N assimilation and recycling. Interestingly, the enzyme activities and sucrose content of the ear organs were positively associated with grain yield and quality, suggesting, together with the regression models using isotope signatures, the potential contribution of these organs during grain filling. This study highlights the use of holistic approaches to the identification of novel targets to improve grain yield and quality in C3 cereals and the key role of non-foliar organs at late-growth stages.
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Affiliation(s)
- Raquel Martínez-Peña
- Group of Cereals, Section of Herbaceous, Instituto Tecnológico Agrario de Castilla y León (ITACyL), Junta de Castilla y León, Valladolid, Spain
| | - Armin Schlereth
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Melanie Höhne
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Beatrice Encke
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Rosa Morcuende
- Institute of Natural Resources and Agrobiology of Salamanca (IRNASA), Consejo Superior de Investigaciones Científicas (CSIC), Salamanca, Spain
| | | | - José Luis Araus
- Integrative Crop Ecophysiology Group, Section of Plant Physiology, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Nieves Aparicio
- Group of Cereals, Section of Herbaceous, Instituto Tecnológico Agrario de Castilla y León (ITACyL), Junta de Castilla y León, Valladolid, Spain
| | - Rubén Vicente
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA), Plant Ecophysiology and Metabolism Group, Oeiras, Portugal
- *Correspondence: Rubén Vicente
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