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Elmetwalli AH, Derbala A, Alsudays IM, Al-Shahari EA, Elhosary M, Elsayed S, Al-Shuraym LA, Moghanm FS, Elsherbiny O. Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies. PLoS One 2024; 19:e0308826. [PMID: 39186505 PMCID: PMC11346661 DOI: 10.1371/journal.pone.0308826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 07/31/2024] [Indexed: 08/28/2024] Open
Abstract
Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R2 values with respect to Chl a (R2 = 0.80) for mandarin fruits while the GI had the greatest R2 with Chl a (R2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R2 of 0.993 with an RMSE of 0.149 for the training set, and an R2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits.
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Affiliation(s)
- Adel H. Elmetwalli
- Agricultural Engineering Department, Faculty of Agriculture, Tanta University, Tanta, Egypt
| | - Asaad Derbala
- Agricultural Engineering Department, Faculty of Agriculture, Tanta University, Tanta, Egypt
| | | | - Eman A. Al-Shahari
- Department of Biology, Faculty of Science and Arts, King Khalid University, Abha, Saudi Arabia
| | - Mahmoud Elhosary
- Evaluation of Natural Resources Department, Agricultural Engineering, Environmental Studies and Research Institute, University of Sadat City, Minufia, Egypt
| | - Salah Elsayed
- Evaluation of Natural Resources Department, Agricultural Engineering, Environmental Studies and Research Institute, University of Sadat City, Minufia, Egypt
- New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah, Iraq
| | - Laila A. Al-Shuraym
- Biology Department, Faculty of Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Farahat S. Moghanm
- Soil and Water Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh, Egypt
| | - Osama Elsherbiny
- Department of Agricultural Engineering, Faculty of Agriculture, Mansoura University, Mansoura, Egypt
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Kim HH, Song IS, Cha RJ. Advancing DIEP Flap Monitoring with Optical Imaging Techniques: A Narrative Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:4457. [PMID: 39065854 PMCID: PMC11280549 DOI: 10.3390/s24144457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 07/05/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024]
Abstract
OBJECTIVES This review aims to explore recent advancements in optical imaging techniques for monitoring the viability of Deep Inferior Epigastric Perforator (DIEP) flap reconstruction. The objectives include highlighting the principles, applications, and clinical utility of optical imaging modalities such as near-infrared spectroscopy (NIRS), indocyanine green (ICG) fluorescence angiography, laser speckle contrast imaging (LSCI), hyperspectral imaging (HSI), dynamic infrared thermography (DIRT), and short-wave infrared thermography (SWIR) in assessing tissue perfusion and oxygenation. Additionally, this review aims to discuss the potential of these techniques in enhancing surgical outcomes by enabling timely intervention in cases of compromised flap perfusion. MATERIALS AND METHODS A comprehensive literature review was conducted to identify studies focusing on optical imaging techniques for monitoring DIEP flap viability. We searched PubMed, MEDLINE, and relevant databases, including Google Scholar, Web of Science, Scopus, PsycINFO, IEEE Xplore, and ProQuest Dissertations & Theses, among others, using specific keywords related to optical imaging, DIEP flap reconstruction, tissue perfusion, and surgical outcomes. This extensive search ensured we gathered comprehensive data for our analysis. Articles discussing the principles, applications, and clinical use of NIRS, ICG fluorescence angiography, LSCI, HSI, DIRT, and SWIR in DIEP flap monitoring were selected for inclusion. Data regarding the techniques' effectiveness, advantages, limitations, and potential impact on surgical decision-making were extracted and synthesized. RESULTS Optical imaging modalities, including NIRS, ICG fluorescence angiography, LSCI, HSI, DIRT, and SWIR offer a non- or minimal-invasive, real-time assessment of tissue perfusion and oxygenation in DIEP flap reconstruction. These techniques provide objective and quantitative data, enabling surgeons to monitor flap viability accurately. Studies have demonstrated the effectiveness of optical imaging in detecting compromised perfusion and facilitating timely intervention, thereby reducing the risk of flap complications such as partial or total loss. Furthermore, optical imaging modalities have shown promise in improving surgical outcomes by guiding intraoperative decision-making and optimizing patient care. CONCLUSIONS Recent advancements in optical imaging techniques present valuable tools for monitoring the viability of DIEP flap reconstruction. NIRS, ICG fluorescence angiography, LSCI, HSI, DIRT, and SWIR offer a non- or minimal-invasive, real-time assessment of tissue perfusion and oxygenation, enabling accurate evaluation of flap viability. These modalities have the potential to enhance surgical outcomes by facilitating timely intervention in cases of compromised perfusion, thereby reducing the risk of flap complications. Incorporating optical imaging into clinical practice can provide surgeons with objective and quantitative data, assisting in informed decision-making for optimal patient care in DIEP flap reconstruction surgeries.
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Affiliation(s)
- Hailey Hwiram Kim
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA; (H.H.K.); (R.J.C.)
| | - In-Seok Song
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA; (H.H.K.); (R.J.C.)
- Department of Oral & Maxillofacial Surgery, Korea University Anam Hospital, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Richard Jaepyeong Cha
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA; (H.H.K.); (R.J.C.)
- Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC 20052, USA
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Taha MF, Mao H, Wang Y, ElManawy AI, Elmasry G, Wu L, Memon MS, Niu Z, Huang T, Qiu Z. High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images. PLANTS (BASEL, SWITZERLAND) 2024; 13:392. [PMID: 38337925 PMCID: PMC10857024 DOI: 10.3390/plants13030392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 02/12/2024]
Abstract
Chlorophyll content reflects plants' photosynthetic capacity, growth stage, and nitrogen status and is, therefore, of significant importance in precision agriculture. This study aims to develop a spectral and color vegetation indices-based model to estimate the chlorophyll content in aquaponically grown lettuce. A completely open-source automated machine learning (AutoML) framework (EvalML) was employed to develop the prediction models. The performance of AutoML along with four other standard machine learning models (back-propagation neural network (BPNN), partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM) was compared. The most sensitive spectral (SVIs) and color vegetation indices (CVIs) for chlorophyll content were extracted and evaluated as reliable estimators of chlorophyll content. Using an ASD FieldSpec 4 Hi-Res spectroradiometer and a portable red, green, and blue (RGB) camera, 3600 hyperspectral reflectance measurements and 800 RGB images were acquired from lettuce grown across a gradient of nutrient levels. Ground measurements of leaf chlorophyll were acquired using an SPAD-502 m calibrated via laboratory chemical analyses. The results revealed a strong relationship between chlorophyll content and SPAD-502 readings, with an R2 of 0.95 and a correlation coefficient (r) of 0.975. The developed AutoML models outperformed all traditional models, yielding the highest values of the coefficient of determination in prediction (Rp2) for all vegetation indices (VIs). The combination of SVIs and CVIs achieved the best prediction accuracy with the highest Rp2 values ranging from 0.89 to 0.98, respectively. This study demonstrated the feasibility of spectral and color vegetation indices as estimators of chlorophyll content. Furthermore, the developed AutoML models can be integrated into embedded devices to control nutrient cycles in aquaponics systems.
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Affiliation(s)
- Mohamed Farag Taha
- School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China; (M.F.T.); (Y.W.); (M.S.M.)
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (Z.N.); (T.H.); (Z.Q.)
- Department of Soil and Water Sciences, Faculty of Environmental Agricultural Sciences, Arish University, North Sinai 45516, Egypt
| | - Hanping Mao
- School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China; (M.F.T.); (Y.W.); (M.S.M.)
| | - Yafei Wang
- School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China; (M.F.T.); (Y.W.); (M.S.M.)
| | - Ahmed Islam ElManawy
- Agricultural Engineering Department, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt (G.E.)
| | - Gamal Elmasry
- Agricultural Engineering Department, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt (G.E.)
| | - Letian Wu
- Institute of Agricultural Mechanization, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China
| | - Muhammad Sohail Memon
- School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China; (M.F.T.); (Y.W.); (M.S.M.)
- Department of Farm Power and Machinery, Faculty of Agricultural Engineering, Sindh Agriculture University, Tandojam 70060, Pakistan
| | - Ziang Niu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (Z.N.); (T.H.); (Z.Q.)
| | - Ting Huang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (Z.N.); (T.H.); (Z.Q.)
| | - Zhengjun Qiu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (Z.N.); (T.H.); (Z.Q.)
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Elsherbiny O, Elaraby A, Alahmadi M, Hamdan M, Gao J. Rapid Grapevine Health Diagnosis Based on Digital Imaging and Deep Learning. PLANTS (BASEL, SWITZERLAND) 2024; 13:135. [PMID: 38202443 PMCID: PMC10780826 DOI: 10.3390/plants13010135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 12/30/2023] [Accepted: 12/30/2023] [Indexed: 01/12/2024]
Abstract
Deep learning plays a vital role in precise grapevine disease detection, yet practical applications for farmer assistance are scarce despite promising results. The objective of this research is to develop an intelligent approach, supported by user-friendly, open-source software named AI GrapeCare (Version 1, created by Osama Elsherbiny). This approach utilizes RGB imagery and hybrid deep networks for the detection and prevention of grapevine diseases. Exploring the optimal deep learning architecture involved combining convolutional neural networks (CNNs), long short-term memory (LSTM), deep neural networks (DNNs), and transfer learning networks (including VGG16, VGG19, ResNet50, and ResNet101V2). A gray level co-occurrence matrix (GLCM) was employed to measure the textural characteristics. The plant disease detection platform (PDD) created a dataset of real-life grape leaf images from vineyards to improve plant disease identification. A data augmentation technique was applied to address the issue of limited images. Subsequently, the augmented dataset was used to train the models and enhance their capability to accurately identify and classify plant diseases in real-world scenarios. The analyzed outcomes indicated that the combined CNNRGB-LSTMGLCM deep network, based on the VGG16 pretrained network and data augmentation, outperformed the separate deep network and nonaugmented version features. Its validation accuracy, classification precision, recall, and F-measure are all 96.6%, with a 93.4% intersection over union and a loss of 0.123. Furthermore, the software developed through the proposed approach holds great promise as a rapid tool for diagnosing grapevine diseases in less than one minute. The framework of the study shows potential for future expansion to include various types of trees. This capability can assist farmers in early detection of tree diseases, enabling them to implement preventive measures.
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Affiliation(s)
- Osama Elsherbiny
- School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China;
- Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt
| | - Ahmed Elaraby
- Department of Cybersecurity, College of Engineering and Information Technology, Buraydah Private Colleges, Buraydah 51418, Saudi Arabia;
- Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena 83523, Egypt
| | - Mohammad Alahmadi
- Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia;
| | - Mosab Hamdan
- Interdisciplinary Research Center for Intelligent Secure Systems, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia;
| | - Jianmin Gao
- School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China;
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Al Dawasari HJ, Bilal M, Moinuddin M, Arshad K, Assaleh K. DeepVision: Enhanced Drone Detection and Recognition in Visible Imagery through Deep Learning Networks. SENSORS (BASEL, SWITZERLAND) 2023; 23:8711. [PMID: 37960411 PMCID: PMC10649875 DOI: 10.3390/s23218711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/05/2023] [Accepted: 10/17/2023] [Indexed: 11/15/2023]
Abstract
Drones are increasingly capturing the world's attention, transcending mere hobbies to revolutionize areas such as engineering, disaster aid, logistics, and airport protection, among myriad other fascinating applications. However, there is growing concern about the risks that they pose to physical infrastructure, particularly at airports, due to potential misuse. In recent times, numerous incidents involving unauthorized drones at airports disrupting flights have been reported. To solve this issue, this article introduces an innovative deep learning method proposed to effectively distinguish between drones and birds. Evaluating the suggested approach with a carefully assembled image dataset demonstrates exceptional performance, surpassing established detection systems previously proposed in the literature. Since drones can appear extremely small compared to other aerial objects, we developed a robust image-tiling technique with overlaps, which showed improved performance in the presence of very small drones. Moreover, drones are frequently mistaken for birds due to their resemblances in appearance and movement patterns. Among the various models tested, including SqueezeNet, MobileNetV2, ResNet18, and ResNet50, the SqueezeNet model exhibited superior performance for medium area ratios, achieving higher average precision (AP) of 0.770. In addition, SqueezeNet's superior AP scores, faster detection times, and more stable precision-recall dynamics make it more suitable for real-time, accurate drone detection than the other existing CNN methods. The proposed approach has the ability to not only detect the presence or absence of drones in a particular area but also to accurately identify and differentiate between drones and birds. The dataset utilized in this research was obtained from a real-world dataset made available by a group of universities and research institutions as part of the 2020 Drone vs. Bird Detection Challenge. We have also tested the performance of the proposed model on an unseen dataset, further validating its better performance.
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Affiliation(s)
- Hassan J. Al Dawasari
- Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (H.J.A.D.); (M.B.)
- Centre of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Muhammad Bilal
- Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (H.J.A.D.); (M.B.)
- Centre of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Muhammad Moinuddin
- Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (H.J.A.D.); (M.B.)
- Centre of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Kamran Arshad
- Department of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates;
- Artificial Intelligence Research Centre, Ajman University, Ajman 346, United Arab Emirates
| | - Khaled Assaleh
- Department of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates;
- Artificial Intelligence Research Centre, Ajman University, Ajman 346, United Arab Emirates
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Indirect Quantitative Analysis of Biochemical Parameters in Banana Using Spectral Reflectance Indices Combined with Machine Learning Modeling. HORTICULTURAE 2022. [DOI: 10.3390/horticulturae8050438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The primary issues in collecting biochemical information in a large area using chemical laboratory procedures are low throughput, hard work, time-consuming, and requiring several samples. Thus, real-time and precise estimation of biochemical variables of various fruits using a proximal remote sensing based on spectral reflectance is critical for harvest time, artificial ripening, and food processing, which might be beneficial economically and ecologically. The main goal of this study was to assess the biochemical parameters of banana fruits such as chlorophyll a (Chl a), chlorophyll b (Chl b), respiration rate, total soluble solids (TSS), and firmness using published and newly developed spectral reflectance indices (SRIs), integrated with machine learning modeling (Artificial Neural Networks; ANN and support vector machine regression; SVMR) at different ripening degrees. The results demonstrated that there were evident and significant differences in values of SRIs at different ripening degrees, which may be attributed to the large variations in values of biochemical parameters. The newly developed two-band SRIs are more effective at measuring different biochemical parameters. The SRIs that were extracted from the visible (VIS), near-infrared (NIR), and their combination showed better R2 with biochemical parameters. SRIs combined with ANN and SVMR would be an effective method for estimating five biochemical parameters in the calibration (Cal.) and validation (Val.) datasets with acceptable accuracy. The ANN-TSS-SRI-13 model was built to determine TSS with greater performance expectations (R2 = 1.00 and 0.97 for Cal. and Val., respectively). Furthermore, the model ANN-Firmness-SRI-15 was developed for determining firmness, and it performed better (R2 = 1.00 and 0.98 for Cal. and Val., respectively). In conclusion, this study revealed that SRIs and a combination approach of ANN and SVMR models would be a useful and excellent tool for estimating the biochemical characteristics of banana fruits.
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Combining Hyperspectral Reflectance and Multivariate Regression Models to Estimate Plant Biomass of Advanced Spring Wheat Lines in Diverse Phenological Stages under Salinity Conditions. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12041983] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
An area of growing interest in wheat-breeding programs for abiotic stresses is the accurate and expeditious phenotyping of large genotype collections using nondestructive hyperspectral sensing tools. The main goal of this study was to use data from canopy spectral signatures (CSS) in the full-spectrum range (400–2500 nm) to estimate and predict the plant biomass dry weight at booting (BDW-BT) and anthesis (BDW-AN) growth stages, and biological yield (BY) of 64 spring wheat germplasms exposed to 150 mM NaCl using 13 spectral reflectance indices (SRIs, consisting of seven vegetation-related SRIs and six water-related SRIs) and partial least squares regression (PLSR). SRI and PLSR performance in estimating plant traits was evaluated during two years at BT, AN, and early milk grain (EMG) growth stages. Results showed significant genotypic differences between the three traits and SRIs, with highly significant two-way and three-way interactions between genotypes, years, and growth stages for all SRIs. Genotypic differences in CSS and the relationships between the three traits and a single wavelength over the full-spectrum range depended on the growth stage. Water-related SRIs were more strongly correlated with the three traits compared with vegetation-related SRIs at the BT stage; the opposite was found at the EMG stage. Both types of SRIs exhibited comparable associations with the three traits at the AN stage. Principal component analysis indicated that it is possible to assess plant biomass variations at an early stage (BT) through published and modified SRIs. SRIs coupled with PLSR models at the BT stage exhibited good prediction capacity of BDW-BT (57%), BDW-AN (82%), and BY (55%). Overall, results demonstrated that the integration of SRIs and multivariate models may present a feasible tool for plant breeders to increase the efficiency of the evaluation process and to improve the genetics for salt tolerance in wheat-breeding programs.
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El-Hendawy S, Dewir YH, Elsayed S, Schmidhalter U, Al-Gaadi K, Tola E, Refay Y, Tahir MU, Hassan WM. Combining Hyperspectral Reflectance Indices and Multivariate Analysis to Estimate Different Units of Chlorophyll Content of Spring Wheat under Salinity Conditions. PLANTS (BASEL, SWITZERLAND) 2022; 11:plants11030456. [PMID: 35161437 PMCID: PMC8839343 DOI: 10.3390/plants11030456] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 02/03/2022] [Accepted: 02/03/2022] [Indexed: 05/30/2023]
Abstract
Although plant chlorophyll (Chl) is one of the important elements in monitoring plant stress and reflects the photosynthetic capacity of plants, their measurement in the lab is generally time- and cost-inefficient and based on a small part of the leaf. This study examines the ability of canopy spectral reflectance data for the accurate estimation of the Chl content of two wheat genotypes grown under three salinity levels. The Chl content was quantified as content per area (Chl area, μg cm-2), concentration per plant (Chl plant, mg plant-1), and SPAD value (Chl SPAD). The performance of spectral reflectance indices (SRIs) with different algorithm forms, partial least square regression (PLSR), and stepwise multiple linear regression (SMLR) in estimating the three units of Chl content was compared. Results show that most indices within each SRI form performed better with Chl area and Chl plant and performed poorly with Chl SPAD. The PLSR models, based on the four forms of SRIs individually or combined, still performed poorly in estimating Chl SPAD, while they exhibited a strong relationship with Chl plant followed by Chl area in both the calibration (Cal.) and validation (Val.) datasets. The SMLR models extracted three to four indices from each SRI form as the most effective indices and explained 73-79%, 80-84%, and 39-43% of the total variability in Chl area, Chl plant, and Chl SPAD, respectively. The performance of the various predictive models of SMLR for predicting Chl content depended on salinity level, genotype, season, and the units of Chl content. In summary, this study indicates that the Chl content measured in the lab and expressed on content (μg cm-2) or concentration (mg plant-1) can be accurately estimated at canopy level using spectral reflectance data.
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Affiliation(s)
- Salah El-Hendawy
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, KSA, P.O. Box 2460, Riyadh 11451, Saudi Arabia; (Y.H.D.); (Y.R.); (M.U.T.)
| | - Yaser Hassan Dewir
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, KSA, P.O. Box 2460, Riyadh 11451, Saudi Arabia; (Y.H.D.); (Y.R.); (M.U.T.)
| | - Salah Elsayed
- Agricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Sadat City 32897, Egypt;
| | - Urs Schmidhalter
- Chair of Plant Nutrition, Department of Plant Sciences, Technical University of Munich, Emil-Ramann-Str. 2, D-85350 Munich, Germany;
| | - Khalid Al-Gaadi
- Department of Agricultural Engineering, Precision Agriculture Research Chair (PARC), College of Food and Agriculture Sciences, King Saud University, KSA, P.O. Box 2460, Riyadh 11451, Saudi Arabia; (K.A.-G.); (E.T.)
| | - ElKamil Tola
- Department of Agricultural Engineering, Precision Agriculture Research Chair (PARC), College of Food and Agriculture Sciences, King Saud University, KSA, P.O. Box 2460, Riyadh 11451, Saudi Arabia; (K.A.-G.); (E.T.)
| | - Yahya Refay
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, KSA, P.O. Box 2460, Riyadh 11451, Saudi Arabia; (Y.H.D.); (Y.R.); (M.U.T.)
| | - Muhammad Usman Tahir
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, KSA, P.O. Box 2460, Riyadh 11451, Saudi Arabia; (Y.H.D.); (Y.R.); (M.U.T.)
| | - Wael M. Hassan
- Department of Agricultural Botany, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt;
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