https://doi.org/10.4081/jae.2024.1599
Identification of drought-salinity combined stress in tomato plants by vegetation indices
Published: 23 October 2024
Abstract Views: 37
PDF: 46
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
A major issue in several farming areas of the Mediterranean basin consists of drought and salinity stress. This stress is mainly due to a steady exposition of warm daily temperature and heatwaves, moreover with inevitable irrigation with saline water. Therefore, detecting the stress is essential to minimise significant yield loss and preserve agricultural sustainability. In this context, remote and proximal sensing can play a crucial role in allowing fast, not destructive, extensive, and reliable assessment of crop status. In this work, the effectiveness of several multispectral indices in detecting salinity and water stress in tomato plants, grown under controlled green-house conditions, was investigated. Three different classifiers (fine tree model, linear discriminant model, and linear support vector machines model) were used to verify whether, and the extent to which, the adopted multispectral indices can be adopted to identify a stress condition of the tomato plants. In the experimental campaign, the stress occurrence on tomato plants was assessed on the base of a set of ecophysiological measurements, such as transpiration, stomatal conductance, and photosynthesis rate. Obtained results showed that a classification model based on linear support vector machines, exploiting the combination of Photochemical Reflectance Index and the Chlorophyl Index, can detect drought and salinity stress in tomato plants with an accuracy higher than 94%.
Aragüés, R., Urdanoz, V., Çetin, M., Kirda, C., Daghari, H., Ltifi, W., et al. 2011. Soil salinity related to physical soil characteristics and irrigation management in four Mediterranean irrigation districts. Agr. Water Manage. 98:959-966.
DOI: https://doi.org/10.1016/j.agwat.2011.01.004
Arlot, A., Celisse, A. 2010. A survey of cross-validation procedures for model selection. Statist. Surv. 4:40-79.
DOI: https://doi.org/10.1214/09-SS054
Biglia, A., Zaman, S., Gay, P., Ricauda Aimonino, D., Comba, L. 2022. 3D point cloud density-based segmentation for vine rows detection and localization. Comput. Electron. Agr.199:107166.
DOI: https://doi.org/10.1016/j.compag.2022.107166
Carter, G.A. 1993. Responses of leaf spectral reflectance to plant stress. Am. J. Bot. 80:239-243.
DOI: https://doi.org/10.1002/j.1537-2197.1993.tb13796.x
Chaves, M.M., Maroco, J.P., Pereira J.S. 2003. Understanding plant responses to drought - from genes to the whole plan”. Funct. Plant Biol. 30:239-264.
DOI: https://doi.org/10.1071/FP02076
Chaves, M.M., Oliveira, M.M. 2004. Mechanisms underlying plant resilience to water deficits: Prospects for water-saving agriculture. J. Exp.l Bot. 55:2365-2384.
DOI: https://doi.org/10.1093/jxb/erh269
Chaves, M.M., Flexas, J., Pinheiro, C. 2009. Photosynthesis under drought and salt stress: Regulation mechanisms from whole plant to cell. Ann. Bot. 103:551-560.
DOI: https://doi.org/10.1093/aob/mcn125
Comba, L., Biglia, A., Ricauda Aimonino, D., Barge, P., Tortia, C., Gay, P. 2019. 2D and 3D data fusion for crop monitoring in precision agriculture. 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Portici, Italy. pp. 62-67.
DOI: https://doi.org/10.1109/MetroAgriFor.2019.8909219
Comba, L., Biglia, A., Ricauda Aimonino, D., Barge, P., Tortia, C., Gay, P. 2021. Thermal network clustering for crops thermal mapping. Acta Hortic. 1311:513-520.
DOI: https://doi.org/10.17660/ActaHortic.2021.1311.65
Comba, L., Zaman, S., Biglia, A., Ricauda Aimonino, D., Dabbene, F., Gay, P. 2020. Semantic interpretation and complexity reduction of 3D point clouds of vineyards. Biosyst. Eng. 197:216-230.
DOI: https://doi.org/10.1016/j.biosystemseng.2020.05.013
Cramer, W., Guiot, J., Fader, M., Garrabou, J., Gattuso, J.-P., Iglesias, A., et al. 2018. Climate change and interconnected risks to sustainable development in the Mediterranean. Nature Clim. Change 8:972-980.
DOI: https://doi.org/10.1038/s41558-018-0299-2
De Kreij, C., Voogt, W., Van den Bos, A.L., Baas, R. 1997. [Voedingsoplossingen voor de teelt van tomaat in gesloten teeltsystemen].[in Dutch]. Brochure VG Tomaat, The Netherlands.
Demmig-Adams, B., Adams, W. 1996. The role of xanthophyll cycle carotenoids in the protection of photosynthesis. Trends Plant Sci. 1:21-26.
DOI: https://doi.org/10.1016/S1360-1385(96)80019-7
De Pascale, S., Ruggiero, C., Barbieri, G., Maggio, A. 2003. Physiological responses of pepper to salinity and drought. J. Am. Soc. Hortic. Sci. 128:48-54.
DOI: https://doi.org/10.21273/JASHS.128.1.0048
Ekinci, M., Ors, S., Turan, M., Yildiz, S., Yildirim, E. 2018. Effects of individual and combined effects of salinity and drought on physiological, nutritional and biochemical properties of cabbage (Brassica oleracea var. capitata). Sci. Hortic.-Amsterdam 240:196-204.
DOI: https://doi.org/10.1016/j.scienta.2018.06.016
Elvanidi, A., Katsoulas, N., Ferentinos, K.P., Bartzanas, T., Kittas, C. 2018. Hyperspectral machine vision as a tool for water stress severity assessment in soilless tomato crop. Biosyst. Eng. 165:25-35.
DOI: https://doi.org/10.1016/j.biosystemseng.2017.11.002
Eurostat. 2019. European Commission. Available from: https://ec.europa.eu/eurostat
FAOSTAT. 2019. Food and agriculture data. Available from: htttp://fao.org/faostat
Farooq, M., Hussain, M., Wahid, A., Siddique, K.H.M. 2012. Drought stress in plants: An overview. In: Aroca, R. (ed.), Plant responses to drought stress. Berlin, Springer.
DOI: https://doi.org/10.1007/978-3-642-32653-0_1
Guidoni, S., Drory, E., Comba, L., Biglia, A., Ricauda Aimonino, D., Gay, P. 2021. A method for crop water status evaluation by thermal imagery for precision viticulture: preliminary results. Acta Hortic. 1314:83-90.
DOI: https://doi.org/10.17660/ActaHortic.2021.1314.12
Katsoulas, N., Elvanidi, A. Ferentinos, K.P., Kacira, M., Bartzanas, T., Kittas, C. 2016. Crop reflectance monitoring as a tool for water stress detection in greenhouses: A review. Biosyst. Eng.151:374-398.
DOI: https://doi.org/10.1016/j.biosystemseng.2016.10.003
Krause, G.H., Weis, E. 1991. Chlorophyll fluorescence and photosynthesis: the basics. Annu. Rev. Plant Physiol. Plant Mol. Biol. 42:313-349.
DOI: https://doi.org/10.1146/annurev.pp.42.060191.001525
Linares, C., Díaz, J., Negev, M., Martínez, G.S., Debono, R., Paz, S. 2020. Impacts of climate change on the public health of the Medi-terranean Basin population - Current situation, projections, preparedness and adaptation. Environ. Res. 182:109107.
DOI: https://doi.org/10.1016/j.envres.2019.109107
Lioy, S., Bianchi, E., Biglia, A., Bessone, M., Laurino, D., Porporato, M. 2021. Viability of thermal imaging in detecting nests of the invasive hornet Vespa velutina. Insect Sci. 28:271-277.
DOI: https://doi.org/10.1111/1744-7917.12760
Liu, E.K., Mei, X.R., Yan, C.R., Gong, D.Z., Zhang, Y.Q. 2016. Effects of water stress on photosynthetic characteristics, dry matter translocation and WUE in two winter wheat genotypes. Agr. Water Manage. 167:75-85.
DOI: https://doi.org/10.1016/j.agwat.2015.12.026
Ors, S., Suarez, D.L. 2017. Spinach biomass yield and physiological response to interactive salinity and water stress. Agr. Water Manage. 190:31-41.
DOI: https://doi.org/10.1016/j.agwat.2017.05.003
Paranychianakis, N.V, Chartzoulakis, K.S. 2005. Irrigation of Mediterranean crops with saline water: from physiology to management practices. Agr. Ecosyst. Environ. 106:171-187.
DOI: https://doi.org/10.1016/j.agee.2004.10.006
Patono, D.L., Eloi Alcatrão, L., Dicembrini, E., Ivaldi, G., Ricauda Aimonino, D., Lovisolo, C. 2023. Technical advances for measurement of gas exchange at the whole plant level: Design solutions and prototype tests to carry out shoot and rootzone analyses in plants of different sizes. Plant Sci. 326:111505.
DOI: https://doi.org/10.1016/j.plantsci.2022.111505
Patono, D.L., Said-Pullicino, D., Eloi Alcatrāo, L., Firbus, A., Ivaldi, G., Chitarra, W., et al. 2022. Photosynthetic recovery in drought-rehydrated grapevines is associated with high demand from the sinks, maximizing the fruit-oriented performance. Plant J. 112:1098-1111.
DOI: https://doi.org/10.1111/tpj.16000
Psiroukis, V., Darra, N., Kasimati, A., Trojacek, P., Hasanli, G., Fountas, S. 2022. Development of a multi-scale tomato yield prediction model in Azerbaijan using spectral indices from Sentinel-2 imagery. Remote Sens. 14:4202.
DOI: https://doi.org/10.3390/rs14174202
Roberts, D., Roth, K., Perroy, R. 2012. Hyperspectral vegetation indices. In: Huete, A, Lyon, J.G., Thenkabail, P.S. (eds.), Hyperspectral Remote Sensing of Vegetation. CRC Press. pp. 309-327.
Schlemmer, M., Gitelson, A., Schepers, J., Ferguson, R., Peng, Y., Shanahan, J., et al. 2013. Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels. Int. J. Appl. Earth Obs. Geoinf. 25:47-54.
DOI: https://doi.org/10.1016/j.jag.2013.04.003
Secchi, F., Perrone, I., Chitarra, W., Zwieniecka, A.K., Lovisolo, C., Zwieniecki, M.A. 2013. The dynamics of embolism refilling in abscisic acid (ABA)-deficient tomato plants. Int. J. Mol. Sci. 14:359-377.
DOI: https://doi.org/10.3390/ijms14010359
Siebers, M.H., Gomez-Casanovas, N., Fu, P., Meacham-Hensold, K., Moore, C.E., Bernacchi, C.J. 2021. Emerging approaches to measure photosynthesis from the leaf to the ecosystem. Emerg. Top. Life Sci. 5:261-274.
DOI: https://doi.org/10.1042/ETLS20200292
Sinclair, T.R., Ludlow, M.M. 1986. Influence of soil water supply on the plant water balance of four tropical grain legumes. Aust. J. Plant Physiol. 13:329-341.
DOI: https://doi.org/10.1071/PP9860329
Tantinantrakun, A., Sukwanit, S., Thompson, A.K., Teerachaichayut, S. 2023. Nondestructive evaluation of SW-NIRS and NIR-HSI for predicting the maturity index of intact pineapples. Nondestructive evaluation of SW-NIRS and NIR-HSI for predicting the maturity index of intact pineapples. Postharvest Biol. Technol.195:112141.
DOI: https://doi.org/10.1016/j.postharvbio.2022.112141
Tramblay, Y., Koutroulis, A., Samaniego, L., Vicente-Serrano, S.M., Volaire,F., Boone, A., et al. 2020. Challenges for drought assessment in the Mediterranean region under future climate scenarios. Earth-Sci. Rev. 210:103348.
DOI: https://doi.org/10.1016/j.earscirev.2020.103348
Usha, K., Singh, B. 2013. Potential applications of remote sensing in horticulture - A review. Sci. Hortic.-Amsterdam 153:71-83.
DOI: https://doi.org/10.1016/j.scienta.2013.01.008
von Caemmerer, S., Farquhar , G.D. 1981. Some relationships between the biochemistry of photosynthesis and the gas exchange of leaves. Planta 153:376-387.
DOI: https://doi.org/10.1007/BF00384257
Winterhalter, L., Mistele, B., Schmidhalter, U. 2013. Evaluation of active and passive sensor systems in the field to phenotype maize hybrids with high-throughput. Field Crops Res. 154:236-245.
DOI: https://doi.org/10.1016/j.fcr.2013.09.006
Supporting Agencies
European Union - NextGenerationEUHow to Cite
Biglia, A. (2024) “Identification of drought-salinity combined stress in tomato plants by vegetation indices”, Journal of Agricultural Engineering. doi: 10.4081/jae.2024.1599.
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Vito Ferro, Paolo Porto, Assessing theoretical flow velocity profile and resistance in gravel bed rivers by field measurements , Journal of Agricultural Engineering: Vol. 49 No. 4 (2018)
- Enrica Santolini, Alberto Barbaresi, Daniele Torreggiani, Patrizia Tassinari, Numerical simulations for the optimisation of ventilation system designed for wine cellars , Journal of Agricultural Engineering: Vol. 50 No. 4 (2019)
- Enrica Santolini, Marco Bovo, Alberto Barbaresi, Daniele Torreggiani, Patrizia Tassinari, Evaluation of microclimate in dairy farms using different model typologies in computational fluid dynamics analyses , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
- Raffaele Cavalli, Stefano Grigolato, Marco Pellegrini, DETERMINATION OF THE FOREST ROAD NETWORK INFLUENCE ON THE SUPPLY CHAIN FOR FIREWOOD PRODUCTION BY DISCRETE EVENT SIMULATION , Journal of Agricultural Engineering: Vol. 42 No. 4 (2011)
- Alessandro D'Emilio, Rosari Mazzarella, Simona M.C. Porto, NEURAL NETWORKS FOR THE SIMULATION OF MICROCLIMATIC PARAMETERS IN DAIRY HOUSES , Journal of Agricultural Engineering: Vol. 40 No. 2 (2009)
- Francesco Giuseppe Carollo, Vito Ferro, Donatella Termini, ANALYSING LONGITUDINAL TURBULENCE INTENSITY IN VEGETATED CHANNELS , Journal of Agricultural Engineering: Vol. 38 No. 4 (2007)
- Luigi Cavazza, Adriano Guarnieri, Angelo Fabbri, Chiara Cevoli, Giovanni Molari, Theoretical and experimental study on mechanical characterisation of a water drop impact on a solid surface , Journal of Agricultural Engineering: Vol. 47 No. 1 (2016)
- Nnaemeka R. Nwakuba, Optimisation of energy consumption of a solar-electric dryer during hot air drying of tomato slices , Journal of Agricultural Engineering: Vol. 50 No. 3 (2019)
- Marcello Niedda, Mauro Greppi, WATERSHED RUNOFFAND RIVER FLOOD MODELING IN LAND USE PLANNING , Journal of Agricultural Engineering: Vol. 41 No. 2 (2010)
- Claudio Caprara, Giovanni Stoppiello, HEAT STORAGE SYSTEM WITH PHASE CHANGE MATERIALS IN COGENERATION UNITS: STUDY OF PRELIMINARY MODEL , Journal of Agricultural Engineering: Vol. 39 No. 4 (2008)
<< < 42 43 44 45 46 47 48 49 > >>
You may also start an advanced similarity search for this article.
