1
|
Lu Y, Zhang X, Cui Y, Chao Y, Song G, Nie C, Wang L. Response of different varieties of maize to nitrogen stress and diagnosis of leaf nitrogen using hyperspectral data. Sci Rep 2023; 13:5890. [PMID: 37041196 PMCID: PMC10090166 DOI: 10.1038/s41598-023-31887-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 03/20/2023] [Indexed: 04/13/2023] Open
Abstract
Spectral technology is theoretically effective in diagnosing N stress in maize (Zea mays L.), but its application is affected by varietal differences. In this study, the responses to N stress, leaf N spectral diagnostic models and the differences between two maize varieties were analysed. The variety "Jiyu 5817" exhibited a greater response to different N stresses at the 12-leaf stage (V12), while "Zhengdan 958" displayed a greater response in the silking stage (R1). Correlation analysis showed that the spectral bands more sensitive to leaf N content were 548-556 nm and 706-721 nm at the V12 stage in "Jiyu 5817" and 760-1142 nm at the R1 stage in "Zhengdan 958". An N spectral diagnostic model that considers the varietal effect improves the model fit and root mean square error (RMSE) with respect to the model without it by 10.6% and 29.2%, respectively. It was concluded that the V12 stage for "Jiyu 5817" and the R1 stage for "Zhengdan 958" were the best diagnostic stages and were more sensitive to N stress, which can further guide fertilization decision-making in precision fertilization.
Collapse
Affiliation(s)
- Yanli Lu
- State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China/Key Laboratory of Plant Nutrition and Fertilizer, Ministry of Agriculture and Rural Affairs/ Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xiaoyu Zhang
- Inner Mongolia Agricultural University, Hohhot, 010018, China
| | - Yuezhi Cui
- Inner Mongolia Agricultural University, Hohhot, 010018, China
| | - Yaru Chao
- State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China/Key Laboratory of Plant Nutrition and Fertilizer, Ministry of Agriculture and Rural Affairs/ Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Guipei Song
- State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China/Key Laboratory of Plant Nutrition and Fertilizer, Ministry of Agriculture and Rural Affairs/ Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Caie Nie
- State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China/Key Laboratory of Plant Nutrition and Fertilizer, Ministry of Agriculture and Rural Affairs/ Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Lei Wang
- State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China/Key Laboratory of Plant Nutrition and Fertilizer, Ministry of Agriculture and Rural Affairs/ Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| |
Collapse
|
2
|
Terentev A, Dolzhenko V, Fedotov A, Eremenko D. Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review. SENSORS 2022; 22:s22030757. [PMID: 35161504 PMCID: PMC8839015 DOI: 10.3390/s22030757] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 01/13/2022] [Accepted: 01/16/2022] [Indexed: 01/10/2023]
Abstract
The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants' disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.
Collapse
Affiliation(s)
- Anton Terentev
- All-Russian Institute of Plant Protection, 3 Podbelsokogo Str., Pushkin, 196608 Saint Petersburg, Russia;
- Correspondence: (A.T.); (A.F.); Tel.: +7-921-937-1550 (A.T.); +7-921-741-6303 (A.F.)
| | - Viktor Dolzhenko
- All-Russian Institute of Plant Protection, 3 Podbelsokogo Str., Pushkin, 196608 Saint Petersburg, Russia;
| | - Alexander Fedotov
- World-Class Research Center «Advanced Digital Technologies», Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya Str., 195251 Saint Petersburg, Russia;
- Correspondence: (A.T.); (A.F.); Tel.: +7-921-937-1550 (A.T.); +7-921-741-6303 (A.F.)
| | - Danila Eremenko
- World-Class Research Center «Advanced Digital Technologies», Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya Str., 195251 Saint Petersburg, Russia;
| |
Collapse
|
3
|
Assessment of Soil Quality under Different Soil Management Strategies: Combined Use of Statistical Approaches to Select the Most Informative Soil Physico-Chemical Indicators. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11115099] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Assessment of soil quality under different management practices is crucial for sustainable agricultural production and natural resource use. In this study, different statistical methods (principal component analysis, PCA; stepwise discriminant analysis, SDA; partial least squares regression with VIP statistics, PLSR) were applied to identify the variables that most discriminated soil status under minimum tillage and no-tillage. Data collected in 2015 from a long-term field experiment on durum wheat (Triticum durum Desf.) were used and twenty soil indicators (chemical, physical and biological) were quantified for the upper soil layer (0–0.20 m). The long-term iteration of different management strategies affected soil quality, showing greater bulk density, relative field capacity (RFC), organic and extractable carbon contents (TOC and TEC) and exchangeable potassium under no-tillage. PCA and SDA confirmed these results and underlined also the role of available phosphorous and organic carbon fractions as variables that most discriminated the treatments investigated. PLSR, including information on plant response (grain yield and protein content), selected, as the most important variables, plant nutrients, soil physical quality indicators, pH and exchangeable cations. The research showed the effectiveness of combining variable selection methods to summarize information deriving from multivariate datasets and improving the understanding of the system investigated. The statistical approaches compared provided different results in terms of variables selected and the ranking of the selected variables. The combined use of the three methods allowed the selection of a smaller number of variables (TOC, TEC, Olsen P, water extractable nitrogen, RFC, macroporosity, air capacity), which were able to provide a clear discrimination between the treatments compared, as shown by the PCA carried out on the reduced dataset. The presence of a response variable in PLSR considerably drove the feature selection process.
Collapse
|
4
|
Riefolo C, Antelmi I, Castrignanò A, Ruggieri S, Galeone C, Belmonte A, Muolo MR, Ranieri NA, Labarile R, Gadaleta G, Nigro F. Assessment of the Hyperspectral Data Analysis as a Tool to Diagnose Xylella fastidiosa in the Asymptomatic Leaves of Olive Plants. PLANTS 2021; 10:plants10040683. [PMID: 33916301 PMCID: PMC8065538 DOI: 10.3390/plants10040683] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 03/30/2021] [Accepted: 03/30/2021] [Indexed: 11/16/2022]
Abstract
Xylella fastidiosa is a bacterial pathogen affecting many plant species worldwide. Recently, the subspecies pauca (Xfp) has been reported as the causal agent of a devastating disease on olive trees in the Salento area (Apulia region, southeastern Italy), where centenarian and millenarian plants constitute a great agronomic, economic, and landscape trait, as well as an important cultural heritage. It is, therefore, important to develop diagnostic tools able to detect the disease early, even when infected plants are still asymptomatic, to reduce the infection risk for the surrounding plants. The reference analysis is the quantitative real time-Polymerase-Chain-Reaction (qPCR) of the bacterial DNA. The aim of this work was to assess whether the analysis of hyperspectral data, using different statistical methods, was able to select with sufficient accuracy, which plants to analyze with PCR, to save time and economic resources. The study area was selected in the Municipality of Oria (Brindisi). Partial Least Square Regression (PLSR) and Canonical Discriminant Analysis (CDA) indicated that the most important bands were those related to the chlorophyll function, water, lignin content, as can also be seen from the wilting symptoms in Xfp-infected plants. The confusion matrix of CDA showed an overall accuracy of 0.67, but with a better capability to discriminate the infected plants. Finally, an unsupervised classification, using only spectral data, was able to discriminate the infected plants at a very early stage of infection. Then, in phase of testing qPCR should be performed only on the plants predicted as infected from hyperspectral data, thus, saving time and financial resources.
Collapse
Affiliation(s)
- Carmela Riefolo
- Research Centre for Agriculture and Environment, Council for Agricultural Research and Economics (CREA-AA), 70125 Bari, Italy;
- Correspondence: (C.R.); (F.N.)
| | - Ilaria Antelmi
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy; (I.A.); (R.L.)
| | - Annamaria Castrignanò
- Department of Engineering and Geology (InGeo), Università degli Studi Gabriele D’Annunzio, Chieti-Pescara, 66013 Chieti, Italy;
| | - Sergio Ruggieri
- Research Centre for Agriculture and Environment, Council for Agricultural Research and Economics (CREA-AA), 70125 Bari, Italy;
| | - Ciro Galeone
- Water Research Institute, National Research Council (CNR-IRSA), 70125 Bari, Italy;
| | - Antonella Belmonte
- Institute for Electromagnetic Sensing of the Environment, National Research Council (CNR-IREA), 70126 Bari, Italy;
| | - Maria Rita Muolo
- Servizi di Informazione Territoriale S.r.l., 70015 Noci, Italy; (M.R.M.); (N.A.R.)
| | - Nicola A. Ranieri
- Servizi di Informazione Territoriale S.r.l., 70015 Noci, Italy; (M.R.M.); (N.A.R.)
| | - Rossella Labarile
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy; (I.A.); (R.L.)
| | | | - Franco Nigro
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy; (I.A.); (R.L.)
- Correspondence: (C.R.); (F.N.)
| |
Collapse
|
5
|
von Gersdorff GJ, Kulig B, Hensel O, Sturm B. Method comparison between real-time spectral and laboratory based measurements of moisture content and CIELAB color pattern during dehydration of beef slices. J FOOD ENG 2021. [DOI: 10.1016/j.jfoodeng.2020.110419] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
|
6
|
Galieni A, D'Ascenzo N, Stagnari F, Pagnani G, Xie Q, Pisante M. Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography. FRONTIERS IN PLANT SCIENCE 2021; 11:609155. [PMID: 33584752 PMCID: PMC7873487 DOI: 10.3389/fpls.2020.609155] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 11/18/2020] [Indexed: 05/24/2023]
Abstract
Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters), and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non-disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture.
Collapse
Affiliation(s)
- Angelica Galieni
- Research Centre for Vegetable and Ornamental Crops, Council for Agricultural Research and Economics, Monsampolo del Tronto, Italy
| | - Nicola D'Ascenzo
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
- Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo, I.R.C.C.S, Pozzilli, Italy
| | - Fabio Stagnari
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy
| | - Giancarlo Pagnani
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy
| | - Qingguo Xie
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
- Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo, I.R.C.C.S, Pozzilli, Italy
| | - Michele Pisante
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy
| |
Collapse
|
7
|
Castrignanò A, Belmonte A, Antelmi I, Quarto R, Quarto F, Shaddad S, Sion V, Muolo MR, Ranieri NA, Gadaleta G, Bartoccetti E, Riefolo C, Ruggieri S, Nigro F. A geostatistical fusion approach using UAV data for probabilistic estimation of Xylella fastidiosa subsp. pauca infection in olive trees. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 752:141814. [PMID: 32890831 DOI: 10.1016/j.scitotenv.2020.141814] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 08/17/2020] [Accepted: 08/18/2020] [Indexed: 06/11/2023]
Abstract
Xylella fastidiosa is one of the most destructive plant pathogenic bacteria worldwide, affecting more than 500 plant species. In Apulia region (southeastern Italy), X. fastidiosa subsp. pauca (Xfp) is responsible for a severe disease, the olive quick decline syndrome (OQDS), spreading epidemically and with dramatic impact on the agriculture, the landscape, the tourism, and the cultural heritage of this region. An early detection of the infected plants would hinder the rapid spread of the disease. The main objective of this paper was to define a geostatistical approach of data fusion, which combines remote (radiometric), and proximal (geophysical) sensor data and visual inspections with plant diagnostic tests, to provide probabilistic maps of Xfp infection risk. The study site was an olive grove located at Oria (province of Brindisi, Italy), where at the time of monitoring (September 2017) only few plants showed initial symptoms of the disease. The measurements included: 1) acquisitions of reflected electromagnetic radiation with UAV (Unmanned Aerial Vehicle) equipped with a multi-spectral camera; 2) geophysical surveys on the trunks of 49 plants with Ground Penetrating Radar (GPR); 3) disease severity rating, by visual inspection of the proportion of canopy with symptoms; 4) qPCR (real time-quantitative Polymerase Chain Reaction) data from tests on 61 plants. The data were submitted to a set of processing techniques to define a "data fusion" procedure, based on non-parametric multivariate geostatistics. The approach allowed marking those areas where the risk of infection was higher, and identifying the possible infection entry routes into the field. The probability map of infection risk could be used as an effective tool for a preventive action and for a better organization of the monitoring plans.
Collapse
Affiliation(s)
- Annamaria Castrignanò
- CREA-AA - Council for Agricultural Research and Economics (Bari, Italy), Via Celso Ulpiani, 5, 70125 Bari (BA), Italy
| | - Antonella Belmonte
- CNR-IREA National Research Council - Institute for Electromagnetic Sensing of the Environment (Bari, Italy), Via Amendola, 122/D, 70126 Bari, Italy.
| | - Ilaria Antelmi
- Department of Soil, Plant and Food Sciences, University of Bari - Aldo Moro, Via G. Amendola 165/A, 70126 Bari, Italy
| | - Ruggiero Quarto
- Department of Earth and Geo-Environmental Sciences, University of Bari, Via Edoardo Orabona, 4, 70125 Bari (BA), Italy
| | - Francesco Quarto
- PRO-GEO s.a.s, Via M. R. Imbriani 13, 76121 Barletta (BT), Italy
| | - Sameh Shaddad
- Soil science Department, Faculty of Agriculture, Zagazig University, 44511 Zagazig, Egypt
| | - Valentina Sion
- Department of Soil, Plant and Food Sciences, University of Bari - Aldo Moro, Via G. Amendola 165/A, 70126 Bari, Italy
| | - Maria Rita Muolo
- Servizi di Informazione Territoriale S.r.l., Piazza Giovanni Paolo II, 8, 70015 Noci (BA), Italy
| | - Nicola A Ranieri
- Servizi di Informazione Territoriale S.r.l., Piazza Giovanni Paolo II, 8, 70015 Noci (BA), Italy
| | - Giovanni Gadaleta
- Professional Agronomist, Via Carr. Lamaveta, 63/F, 76011 Bisceglie (BT), Italy
| | | | - Carmela Riefolo
- CREA-AA - Council for Agricultural Research and Economics (Bari, Italy), Via Celso Ulpiani, 5, 70125 Bari (BA), Italy
| | - Sergio Ruggieri
- CREA-AA - Council for Agricultural Research and Economics (Bari, Italy), Via Celso Ulpiani, 5, 70125 Bari (BA), Italy
| | - Franco Nigro
- Department of Soil, Plant and Food Sciences, University of Bari - Aldo Moro, Via G. Amendola 165/A, 70126 Bari, Italy
| |
Collapse
|
8
|
El-Hendawy SE, Alotaibi M, Al-Suhaibani N, Al-Gaadi K, Hassan W, Dewir YH, Emam MAEG, Elsayed S, Schmidhalter U. Comparative Performance of Spectral Reflectance Indices and Multivariate Modeling for Assessing Agronomic Parameters in Advanced Spring Wheat Lines Under Two Contrasting Irrigation Regimes. FRONTIERS IN PLANT SCIENCE 2019; 10:1537. [PMID: 31850029 PMCID: PMC6892836 DOI: 10.3389/fpls.2019.01537] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 11/04/2019] [Indexed: 05/06/2023]
Abstract
The incorporation of nondestructive and cost-effective tools in genetic drought studies in combination with reliable indirect screening criteria that exhibit high heritability and genetic correlations will be critical for addressing the water deficit challenges of the agricultural sector under arid conditions and ensuring the success of genotype development. In this study, the proximal spectral reflectance data were exploited to assess three destructive agronomic parameters [dry weight (DW) and water content (WC) of the aboveground biomass and grain yield (GY)] in 30 recombinant F7 and F8 inbred lines (RILs) growing under full (FL) and limited (LM) irrigation regimes. The utility of different groups of spectral reflectance indices (SRIs) as an indirect assessment tool was tested based on heritability and genetic correlations. The performance of the SRIs and different models of partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) in estimating the destructive parameters was considered. Generally, all groups of SRIs, as well as different models of PLSR and SMLR, generated better estimations for destructive parameters under LM and combined FL+LM than under FL. Even though most of the SRIs exhibited a low association with destructive parameters under FL, they exhibited moderate to high genetic correlations and also had high heritability. The SRIs based on near-infrared (NIR)/visible (VIS) and NIR/NIR, especially those developed in this study, spectral band intervals extracted within VIS, red edge, and NIR spectral range, or individual effective wavelengths relevant to green, red, red edge, and middle NIR spectral region, were found to be more effective in estimating the destructive parameters under all conditions. Five models of SMLR and PLSR for each condition explained most of the variation in the three destructive parameters among genotypes. These models explained 42% to 46%, 19% to 30%, and 39% to 46% of the variation in DW, WC, and GY among genotypes under FL, 69% to 72%, 59% to 61%, and 77% to 81% under LM, and 71% to 75%, 61% to 71%, and 74% to 78% under FL+LM, respectively. Overall, these results confirmed that application of hyperspectral reflectance sensing in breeding programs is not only important for evaluating a sufficient number of genotypes in an expeditious and cost-effective manner but also could be exploited to develop indirect breeding traits that aid in accelerating the development of genotypes for application under adverse environmental conditions.
Collapse
Affiliation(s)
- Salah E. El-Hendawy
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
- Department of Agronomy, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt
| | - Majed Alotaibi
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Nasser Al-Suhaibani
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Khalid Al-Gaadi
- Department of Agricultural Engineering, Precision Agriculture Research Chair, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Wael Hassan
- Department of Agricultural Botany, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt
- Department of Biology, College of Science and Humanities at Quwayiah, Shaqra University, Riyadh, Saudi Arabia
| | - Yaser Hassan Dewir
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
- Department of Horticulture, Faculty of Agriculture, Kafrelsheikh University, Kafr El Sheikh, Egypt
| | | | - Salah Elsayed
- Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Menoufia, Egypt
| | - Urs Schmidhalter
- Department of Plant Sciences, Technische Universität München, Freising, Germany
| |
Collapse
|
9
|
El-Hendawy S, Al-Suhaibani N, Alotaibi M, Hassan W, Elsayed S, Tahir MU, Mohamed AI, Schmidhalter U. Estimating growth and photosynthetic properties of wheat grown in simulated saline field conditions using hyperspectral reflectance sensing and multivariate analysis. Sci Rep 2019; 9:16473. [PMID: 31712701 PMCID: PMC6848100 DOI: 10.1038/s41598-019-52802-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 10/15/2019] [Indexed: 01/01/2023] Open
Abstract
The timely estimation of growth and photosynthetic-related traits in an easy and nondestructive manner using hyperspectral data will become imperative for addressing the challenges of environmental stresses inherent to the agricultural sector in arid conditions. However, the handling and analysis of these data by exploiting the full spectrum remains the determining factor for refining the estimation of crop variables. The main objective of this study was to estimate growth and traits underpinning photosynthetic efficiency of two wheat cultivars grown under simulated saline field conditions and exposed to three salinity levels using hyperspectral reflectance information from 350–2500 nm obtained at two years. Partial least squares regression (PLSR) based on the full spectrum was applied to develop predictive models for estimating the measured parameters in different conditions (salinity levels, cultivars, and years). Variable importance in projection (VIP) of PLSR in combination with multiple linear regression (MLR) was implemented to identify important waveband regions and influential wavelengths related to the measured parameters. The results showed that the PLSR models exhibited moderate to high coefficients of determination (R2) in both the calibration and validation datasets (0.30–0.95), but that this range of R2 values depended on parameters and conditions. The PLSR models based on the full spectrum accurately and robustly predicted three of four parameters across all conditions. Based on the combination of PLSR-VIP and MLR analysis, the wavelengths selected within the visible (VIS), red-edge, and middle near-infrared (NIR) wavebands were the most sensitive to all parameters in all conditions, whereas those selected within the shortwave infrared (SWIR) waveband were effective for some parameters in particular conditions. Overall, these results indicated that the PLSR analysis and band selection techniques can offer a rapid and nondestructive alternative approach to accurately estimate growth- and photosynthetic-related trait responses to salinity stress.
Collapse
Affiliation(s)
- Salah El-Hendawy
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, 11451, Riyadh, Saudi Arabia. .,Department of Agronomy, Faculty of Agriculture, Suez Canal University, Ismailia, 41522, Egypt.
| | - Nasser Al-Suhaibani
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, 11451, Riyadh, Saudi Arabia
| | - Majed Alotaibi
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, 11451, Riyadh, Saudi Arabia
| | - Wael Hassan
- Department of Agricultural Botany, Faculty of Agriculture, Suez Canal University, Ismailia, 41522, Egypt.,Department of Biology, College of Science and Humanities at Quwayiah, Shaqra University, Riyadh, 11961, Saudi Arabia
| | - Salah Elsayed
- Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Menoufia, 32897, Egypt
| | - Muhammad Usman Tahir
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, 11451, Riyadh, Saudi Arabia
| | - Ahmed Ibrahim Mohamed
- Department of Soil and Water, Faculty of Agriculture, Suez Canal University, Ismailia, 41522, Egypt
| | - Urs Schmidhalter
- Department of Plant Science, Chair of Plant Nutrition, Technical University of Munich, Freising, Germany
| |
Collapse
|
10
|
Assessing the Feasibility of a Miniaturized Near-Infrared Spectrometer in Determining Quality Attributes of San Marzano Tomato. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01475-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
11
|
Fracchiolla M, Stellacci AM, Cazzato E, Tedone L, Alhajj Ali S, De Mastro G. Effects of Conservative Tillage and Nitrogen Management on Weed Seed Bank after a Seven-Year Durum Wheat-Faba Bean Rotation. PLANTS (BASEL, SWITZERLAND) 2018; 7:plants7040082. [PMID: 30274336 PMCID: PMC6313847 DOI: 10.3390/plants7040082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 09/28/2018] [Indexed: 06/08/2023]
Abstract
Conservative agriculture includes a range of management strategies with low energy inputs such as no-tillage, minimum tillage, and low application of fertilizers. Weed flora in arable fields is strictly affected by agronomic practices such as tillage and fertilization management. This study was conducted seven years after the beginning of a long-term-durum wheat⁻faba bean-rotation. It analyzes the combined effects on the soil seed bank of three different tillage systems (conservative, reduced, and conventional tillage) and two levels of nitrogen fertilization. The effects were investigated both using stepwise discriminant analysis and analysis of variance in order to find statistical differences among main factors and their interactions. The seed bank of Conyza canadensis, Papaver rhoeas, Solanum nigrum, Fallopia convolvulus, and Fumaria officinalis was higher in conservative or reduced tillage plots. The magnitude of the response to nitrogen supply varied among weed species. Conyza canadensis seemed to be favored by low nitrogen supply, whereas Sinapis arvensis by higher doses of nitrogen. Anagallis arvensis showed the lowest seed bank in conventionally tilled plots, without distinction of nitrogen supply. The results suggest that different tillage systems and, to a lesser extent, different nitrogen supply, produce changes in the seed bank size and composition, along the soil profile.
Collapse
Affiliation(s)
- Mariano Fracchiolla
- Department of Agricultural and Environmental Science, University of Bari, 70125 Bari, Italy.
| | - Anna Maria Stellacci
- Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, 70125 Bari, Italy.
| | - Eugenio Cazzato
- Department of Agricultural and Environmental Science, University of Bari, 70125 Bari, Italy.
| | - Luigi Tedone
- Department of Agricultural and Environmental Science, University of Bari, 70125 Bari, Italy.
| | - Salem Alhajj Ali
- Department of Agricultural and Environmental Science, University of Bari, 70125 Bari, Italy.
| | - Giuseppe De Mastro
- Department of Agricultural and Environmental Science, University of Bari, 70125 Bari, Italy.
| |
Collapse
|