1
|
Nagy A, Szabó A, Elbeltagi A, Nxumalo GS, Bódi EB, Tamás J. Hyperspectral indices data fusion-based machine learning enhanced by MRMR algorithm for estimating maize chlorophyll content. FRONTIERS IN PLANT SCIENCE 2024; 15:1419316. [PMID: 39479550 PMCID: PMC11521818 DOI: 10.3389/fpls.2024.1419316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 09/12/2024] [Indexed: 11/02/2024]
Abstract
Accurate estimation of chlorophyll is essential for monitoring maize health and growth, for which hyperspectral imaging provides rich data. In this context, this paper presents an innovative method to estimate maize chlorophyll by combining hyperspectral indices and advanced machine learning models. The methodology of this study focuses on the development of machine learning models using proprietary hyperspectral indices to estimate corn chlorophyll content. Six advanced machine learning models were used, including robust linear stepwise regression, support vector machines (SVM), fine Gaussian SVM, Matern 5/2 Gaussian stepwise regression, and three-layer neural network. The MRMR algorithm was integrated into the process to improve feature selection by identifying the most informative spectral bands, thereby reducing data redundancy and improving model performance. The results showed significant differences in the performance of the six machine learning models applied to chlorophyll estimation. Among the models, the Matern 5/2 Gaussian process regression model showed the highest prediction accuracy. The model achieved R2 = 0.71 for the training set, RMSE = 338.46 µg/g and MAE = 264.30 µg/g. In the case of the validation set, the Matern 5/2 Gaussian process regression model further improved its performance, reaching R2 =0.79, RMSE=296.37 µg/g, MAE=237.12 µg/g. These metrics show that Matern's 5/2 Gaussian process regression model combined with the MRMR algorithm to select optimal traits is highly effective in predicting corn chlorophyll content. This research has important implications for precision agriculture, particularly for real-time monitoring and management of crop health. Accurate estimation of chlorophyll allows farmers to take timely and targeted action.
Collapse
Affiliation(s)
- Attila Nagy
- Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, Debrecen, Hungary
- National Laboratory for Water Science and Water Safety, Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, Debrecen, Hungary
| | - Andrea Szabó
- Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, Debrecen, Hungary
- National Laboratory for Water Science and Water Safety, Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, Debrecen, Hungary
| | - Ahmed Elbeltagi
- Agricultural Engineering Dept., Faculty of Agriculture, Mansoura University, Mansoura, Egypt
| | - Gift Siphiwe Nxumalo
- Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, Debrecen, Hungary
| | - Erika Budayné Bódi
- Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, Debrecen, Hungary
- National Laboratory for Water Science and Water Safety, Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, Debrecen, Hungary
| | - János Tamás
- Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, Debrecen, Hungary
- National Laboratory for Water Science and Water Safety, Institute of Water and Environmental Management, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, Debrecen, Hungary
| |
Collapse
|
2
|
Morton M, Fiene G, Ahmed HI, Rey E, Abrouk M, Angel Y, Johansen K, Saber NO, Malbeteau Y, Al-Mashharawi S, Ziliani MG, Aragon B, Oakey H, Berger B, Brien C, Krattinger SG, Mousa MAA, McCabe MF, Negrão S, Tester M, Julkowska MM. Deciphering salt stress responses in Solanum pimpinellifolium through high-throughput phenotyping. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 119:2514-2537. [PMID: 38970620 DOI: 10.1111/tpj.16894] [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: 11/21/2023] [Accepted: 06/03/2024] [Indexed: 07/08/2024]
Abstract
Soil salinity is a major environmental stressor affecting agricultural productivity worldwide. Understanding plant responses to salt stress is crucial for developing resilient crop varieties. Wild relatives of cultivated crops, such as wild tomato, Solanum pimpinellifolium, can serve as a useful resource to further expand the resilience potential of the cultivated germplasm, S. lycopersicum. In this study, we employed high-throughput phenotyping in the greenhouse and field conditions to explore salt stress responses of a S. pimpinellifolium diversity panel. Our study revealed extensive phenotypic variations in response to salt stress, with traits such as transpiration rate, shoot mass, and ion accumulation showing significant correlations with plant performance. We found that while transpiration was a key determinant of plant performance in the greenhouse, shoot mass strongly correlated with yield under field conditions. Conversely, ion accumulation was the least influential factor under greenhouse conditions. Through a Genome Wide Association Study, we identified candidate genes not previously associated with salt stress, highlighting the power of high-throughput phenotyping in uncovering novel aspects of plant stress responses. This study contributes to our understanding of salt stress tolerance in S. pimpinellifolium and lays the groundwork for further investigations into the genetic basis of these traits, ultimately informing breeding efforts for salinity tolerance in tomato and other crops.
Collapse
Affiliation(s)
- Mitchell Morton
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Gabriele Fiene
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Hanin Ibrahim Ahmed
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Elodie Rey
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Michael Abrouk
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Yoseline Angel
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
| | - Kasper Johansen
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Noha O Saber
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Yoann Malbeteau
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Samir Al-Mashharawi
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Matteo G Ziliani
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Hydrosat S.à r.l., 9 Rue du Laboratoire, Luxembourg City, 1911, Luxembourg
| | - Bruno Aragon
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Helena Oakey
- Robinson Institute, University of Adelaide, Adelaide, Australia
| | - Bettina Berger
- Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, Australia
| | - Chris Brien
- Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, Australia
| | - Simon G Krattinger
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Magdi A A Mousa
- Department of Agriculture, Faculty of Environmental Sciences, King Abdulaziz University, Jeddah, 80208, Saudi Arabia
- Department of Vegetable Crops, Faculty of Agriculture, Assiut University, Assiut, 71526, Egypt
| | - Matthew F McCabe
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Sónia Negrão
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- University College, Dublin, Republic of Ireland
| | - Mark Tester
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Magdalena M Julkowska
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Boyce Thompson Institute, Ithaca, New York, USA
| |
Collapse
|
3
|
Zununjan Z, Turghan MA, Sattar M, Kasim N, Emin B, Abliz A. Combining the fractional order derivative and machine learning for leaf water content estimation of spring wheat using hyper-spectral indices. PLANT METHODS 2024; 20:97. [PMID: 38909230 PMCID: PMC11193302 DOI: 10.1186/s13007-024-01224-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 06/10/2024] [Indexed: 06/24/2024]
Abstract
Leaf water content (LWC) is a vital indicator of crop growth and development. While visible and near-infrared (VIS-NIR) spectroscopy makes it possible to estimate crop leaf moisture, spectral preprocessing and multiband spectral indices have important significance in the quantitative analysis of LWC. In this work, the fractional order derivative (FOD) was used for leaf spectral processing, and multiband spectral indices were constructed based on the band-optimization algorithm. Eventually, an integrated index, namely, the multiband spectral index (MBSI) and moisture index (MI), is proposed to estimate the LWC in spring wheat around Fu-Kang City, Xinjiang, China. The MBSIs for LWC were calculated from two types of spectral data: raw reflectance (RR) and the spectrum based on FOD. The LWC was estimated by combining machine learning (K-nearest neighbor, KNN; support vector machine, SVM; and artificial neural network, ANN). The results showed that the fractional derivative pretreatment of spectral data enhances the implied information of the spectrum (the maximum correlation coefficient appeared using a 0.8-order differential) and increases the number of sensitive bands, especially in the near-infrared bands (700-1100 nm). The correlations between LWC and the two-band index (RVI1156, 1628 nm), three-band indices (3BI-3(766, 478, 1042 nm), 3BI-4(1129, 1175, 471 nm), 3BI-5(814, 929, 525 nm), 3BI-6(1156, 1214, 802 nm), 3BI-7(929, 851, 446 nm)) based on FOD were higher than that of moisture indices and single-band spectrum, with r of - 0.71**, 0.74**, 0.73**, - 0.72**, 0.75** and - 0.76** for the correlation. The prediction accuracy of the two-band spectral indices (DVI(698, 1274 nm) DVI(698, 1274 nm) DVI(698, 1274 nm)) was higher than that of the moisture spectral index, with R2 of 0.81 and R2 of 0.79 for the calibration and validation, respectively. Due to a large amount of spectral indices, the correlation coefficient method was used to select the characteristic spectral index from full three-band indices. Among twenty seven models, the FWBI-3BI- 0.8 order model performed the best predictive ability (with an R2 of 0.86, RMSE of 2.11%, and RPD of 2.65). These findings confirm that combining spectral index optimization with machine learning is a highly effective method for inverting the leaf water content in spring wheat.
Collapse
Affiliation(s)
- Zinhar Zununjan
- School of Resources and Environment, Yili Normal University, Yining, 835000, China
| | - Mardan Aghabey Turghan
- State Key Laboratory of Oasis and Desert Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Mutallip Sattar
- College of Information Management, Xinjiang University of Finance and Economics, Urumqi, 830012, China
| | - Nijat Kasim
- School of Resources and Environment, Yili Normal University, Yining, 835000, China.
| | - Bilal Emin
- School of Resources and Environment, Yili Normal University, Yining, 835000, China
| | - Abdugheni Abliz
- College of Resources and Environmental Sciences, Xinjiang University, Urumqi, 830046, China
| |
Collapse
|
4
|
Shi Y, Choudhury K, Sopko X, Adham S, Chikwana E. In-silico prediction of dislodgeable foliar residues and regulatory implications for plant protection products. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2024:10.1038/s41370-024-00675-w. [PMID: 38678132 DOI: 10.1038/s41370-024-00675-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 04/29/2024]
Abstract
BACKGROUND When experimentally determined dislodgeable foliar residue (DFR) values are not available, regulatory agencies use conservative default DFR values as a first-tier approach to assess post-application dermal exposures to plant protection products (PPPs). These default values are based on a limited set of field studies, are very conservative, and potentially overestimate exposures from DFRs. OBJECTIVE Use Random Forest to develop classification and regression-type ensemble models to predict DFR values after last application (DFR0) by considering experimentally-based variability due to differences in physical and chemical properties of PPPs, agronomic practices, crop type, and climatic conditions. METHODS Random Forest algorithm was used to develop in-silico ensemble DFR0 prediction models using more than 100 DFR studies from Corteva AgriscienceTM. Several variables related to the active ingredient (a.i.) that was applied, crop, and climate conditions at the time of last application were considered as model parameters. RESULTS The proposed ensemble models demonstrated 98% prediction accuracy that if a DFR0 is predicted to be less than the European Food Safety Authority (EFSA) default DFR0 value of 3 µg/cm2/kg a.i./ha, it is highly indicative that the measured DFR value will be less than the default if the study is conducted. If a value is predicted to be larger than or equal to the EFSA default, the model has an 83% prediction accuracy. IMPACT STATEMENT This manuscript is expected to have significant impact globally as it provides: A framework for incorporating in silico DFR data into worker exposure assessment, A roadmap for a tiered approach for conducting re-entry exposure assessment, and A proof of concept for using existing DFR data to provide a read-across framework that can easily be harmonized across all regulatory agencies to provide more robust assessments for PPP exposures.
Collapse
Affiliation(s)
- Yi Shi
- Department of Biostatistics & Health Data Science, Indiana University Richard M. Fairbanks School of Public Health, 1050 Wishard Blvd, Indianapolis, IN, 46202, USA
| | - Kanak Choudhury
- Corteva Agriscience LLC, 9330 Zionsville Road, Indianapolis, IN, 46268, USA
| | - Xiaoyi Sopko
- Corteva Agriscience LLC, 9330 Zionsville Road, Indianapolis, IN, 46268, USA
| | - Sarah Adham
- Corteva Agriscience LLC, Abingdon, OX14 4RY, United Kingdom
| | - Edward Chikwana
- Corteva Agriscience LLC, 9330 Zionsville Road, Indianapolis, IN, 46268, USA.
| |
Collapse
|
5
|
Uddin MG, Nash S, Rahman A, Dabrowski T, Olbert AI. Data-driven modelling for assessing trophic status in marine ecosystems using machine learning approaches. ENVIRONMENTAL RESEARCH 2024; 242:117755. [PMID: 38008200 DOI: 10.1016/j.envres.2023.117755] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 10/05/2023] [Accepted: 11/20/2023] [Indexed: 11/28/2023]
Abstract
Assessing eutrophication in coastal and transitional waters is of utmost importance, yet existing Trophic Status Index (TSI) models face challenges like multicollinearity, data redundancy, inappropriate aggregation methods, and complex classification schemes. To tackle these issues, we developed a novel tool that harnesses machine learning (ML) and artificial intelligence (AI), enhancing the reliability and accuracy of trophic status assessments. Our research introduces an improved data-driven methodology specifically tailored for transitional and coastal (TrC) waters, with a focus on Cork Harbour, Ireland, as a case study. Our innovative approach, named the Assessment Trophic Status Index (ATSI) model, comprises three main components: the selection of pertinent water quality indicators, the computation of ATSI scores, and the implementation of a new classification scheme. To optimize input data and minimize redundancy, we employed ML techniques, including advanced deep learning methods. Specifically, we developed a CHL prediction model utilizing ten algorithms, among which XGBoost demonstrated exceptional performance, showcasing minimal errors during both training (RMSE = 0.0, MSE = 0.0, MAE = 0.01) and testing (RMSE = 0.0, MSE = 0.0, MAE = 0.01) phases. Utilizing a novel linear rescaling interpolation function, we calculated ATSI scores and evaluated the model's sensitivity and efficiency across diverse application domains, employing metrics such as R2, the Nash-Sutcliffe efficiency (NSE), and the model efficiency factor (MEF). The results consistently revealed heightened sensitivity and efficiency across all application domains. Additionally, we introduced a brand new classification scheme for ranking the trophic status of transitional and coastal waters. To assess spatial sensitivity, we applied the ATSI model to four distinct waterbodies in Ireland, comparing trophic assessment outcomes with the Assessment of Trophic Status of Estuaries and Bays in Ireland (ATSEBI) System. Remarkably, significant disparities between the ATSI and ATSEBI System were evident in all domains, except for Mulroy Bay. Overall, our research significantly enhances the accuracy of trophic status assessments in marine ecosystems. The ATSI model, combined with cutting-edge ML techniques and our new classification scheme, represents a promising avenue for evaluating and monitoring trophic conditions in TrC waters. The study also demonstrated the effectiveness of ATSI in assessing trophic status across various waterbodies, including lakes, rivers, and more. These findings make substantial contributions to the field of marine ecosystem management and conservation.
Collapse
Affiliation(s)
- Md Galal Uddin
- School of Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland.
| | - Stephen Nash
- School of Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland
| | - Azizur Rahman
- School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, Australia; The Gulbali Institute of Agriculture, Water and Environment, Charles Sturt University, Wagga Wagga, Australia
| | | | - Agnieszka I Olbert
- School of Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland
| |
Collapse
|
6
|
Pengphorm P, Thongrom S, Daengngam C, Duangpan S, Hussain T, Boonrat P. Optimal-Band Analysis for Chlorophyll Quantification in Rice Leaves Using a Custom Hyperspectral Imaging System. PLANTS (BASEL, SWITZERLAND) 2024; 13:259. [PMID: 38256812 PMCID: PMC10819252 DOI: 10.3390/plants13020259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 01/03/2024] [Accepted: 01/12/2024] [Indexed: 01/24/2024]
Abstract
Hyperspectral imaging (HSI) is a promising tool in chlorophyll quantification, providing a non-invasive method to collect important information for effective crop management. HSI contributes to food security solutions by optimising crop yields. In this study, we presented a custom HSI system specifically designed to provide a quantitative analysis of leaf chlorophyll content (LCC). To ensure precise estimation, significant wavelengths were identified using optimal-band analysis. Our research was centred on two sets of 120 leaf samples sourced from Thailand's unique Chaew Khing rice variant. The samples were subjected to (i) an analytical LCC assessment and (ii) HSI imaging for spectral reflectance data capture. A linear regression comparison of these datasets revealed that the green (575 ± 2 nm) and near-infrared (788 ± 2 nm) bands were the most outstanding performers. Notably, the green normalised difference vegetation index (GNDVI) was the most reliable during cross-validation (R2=0.78 and RMSE = 2.4 µg∙cm-2), outperforming other examined vegetable indices (VIs), such as the simple ratio (RED/GREEN) and the chlorophyll index. The potential development of a streamlined sensor dependent only on these two wavelengths is a significant outcome of identifying these two optimal bands. This innovation can be seamlessly integrated into farming landscapes or attached to UAVs, allowing real-time monitoring and rapid, targeted N management interventions.
Collapse
Affiliation(s)
- Panuwat Pengphorm
- Division of Physical Science, Faculty of Science, Prince of Songkla University, Hat Yai 90110, Songkhla, Thailand; (P.P.); (S.T.); (C.D.)
- National Astronomical Research Institute of Thailand (Public Organization), Mae Rim 50180, Chiang Mai, Thailand
| | - Sukrit Thongrom
- Division of Physical Science, Faculty of Science, Prince of Songkla University, Hat Yai 90110, Songkhla, Thailand; (P.P.); (S.T.); (C.D.)
- National Astronomical Research Institute of Thailand (Public Organization), Mae Rim 50180, Chiang Mai, Thailand
| | - Chalongrat Daengngam
- Division of Physical Science, Faculty of Science, Prince of Songkla University, Hat Yai 90110, Songkhla, Thailand; (P.P.); (S.T.); (C.D.)
- National Astronomical Research Institute of Thailand (Public Organization), Mae Rim 50180, Chiang Mai, Thailand
| | - Saowapa Duangpan
- Agricultural Innovation and Management Division, Faculty of Natural Resources, Prince of Songkla University, Hat Yai 90110, Songkhla, Thailand;
- Oil Palm Agronomical Research Center, Faculty of Natural Resources, Prince of Songkla University, Hat Yai 90110, Songkhla, Thailand
| | - Tajamul Hussain
- Hermiston Agricultural Research and Extension Center, Oregon State University, Hermiston, OR 97838, USA;
| | - Pawita Boonrat
- Faculty of Technology and Environment, Prince of Songkla University, Phuket Campus, Kathu 83120, Phuket, Thailand
| |
Collapse
|
7
|
Okyere FG, Cudjoe D, Sadeghi-Tehran P, Virlet N, Riche AB, Castle M, Greche L, Mohareb F, Simms D, Mhada M, Hawkesford MJ. Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping. PLANTS (BASEL, SWITZERLAND) 2023; 12:2035. [PMID: 37653952 PMCID: PMC10224253 DOI: 10.3390/plants12102035] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/03/2023] [Accepted: 05/10/2023] [Indexed: 07/15/2023]
Abstract
Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green-red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.
Collapse
Affiliation(s)
- Frank Gyan Okyere
- Sustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UK
- School of Water, Energy and Environment, Soil, Agrifood and Biosciences, Cranfield University, Bedford MK43 0AL, UK
| | - Daniel Cudjoe
- Sustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UK
- School of Water, Energy and Environment, Soil, Agrifood and Biosciences, Cranfield University, Bedford MK43 0AL, UK
| | | | - Nicolas Virlet
- Sustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UK
| | - Andrew B. Riche
- Sustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UK
| | - March Castle
- Sustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UK
| | - Latifa Greche
- Sustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UK
| | - Fady Mohareb
- School of Water, Energy and Environment, Soil, Agrifood and Biosciences, Cranfield University, Bedford MK43 0AL, UK
| | - Daniel Simms
- School of Water, Energy and Environment, Soil, Agrifood and Biosciences, Cranfield University, Bedford MK43 0AL, UK
| | - Manal Mhada
- African Integrated Plant and Soil Science, Agro-Biosciences, University of Mohammed VI Polytechnic, Lot 660, Ben Guerir 43150, Morocco
| | | |
Collapse
|
8
|
Zhang M, Chen T, Gu X, Chen D, Wang C, Wu W, Zhu Q, Zhao C. Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods. FRONTIERS IN PLANT SCIENCE 2023; 14:1073346. [PMID: 36968402 PMCID: PMC10030857 DOI: 10.3389/fpls.2023.1073346] [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: 10/18/2022] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works.
Collapse
Affiliation(s)
- Mingzheng Zhang
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
| | - Tian’en Chen
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Xiaohe Gu
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Dong Chen
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Cong Wang
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Wenbiao Wu
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Qingzhen Zhu
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Chunjiang Zhao
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| |
Collapse
|