Vegetation Indices for Early Grey Mould Detection in Lettuce Grown under Different Lighting Conditions
Abstract
:1. Introduction
2. Results
3. Materials and Methods
3.1. Lettuce Growth Conditions
3.2. Light Treatments
3.3. Botrytis cinerea Preparation
3.4. Botrytis cinerea Inoculation on Lettuce In Vivo
3.5. Non-Destructive Measurements
3.6. Determination of Total Phenolic Content
3.7. Evaluation of DPPH Free-Radical Scavenging Activity
3.8. Statistical Analysis
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
- Veloso, J.; Van Kan, J.A.L. Many Shades of Grey in Botrytis–Host Plant Interactions. Trends Plant Sci. 2018, 23, 613–622. [Google Scholar] [CrossRef] [PubMed]
- Schumacher, J. How Light Affects the Life of Botrytis. Fungal Genet. Biol. 2017, 106, 26–41. [Google Scholar] [CrossRef] [PubMed]
- Elad, Y.; Freeman, S. Biological Control of Fungal Plant Pathogens. In Agricultural Applications; Kempken, F., Ed.; Springer: Berlin/Heidelberg, Germany, 2002; ISBN 9783642076503. [Google Scholar]
- Elmer, R.A.G.; Hoyte, S.M.; Vanneste, J.L.; Reglinski, T.; Wood, R.N.; Parry, F.J. Biological Control of Fruit Pathogens. N. Z. Plant Prot. 2005, 58, 47–54. [Google Scholar] [CrossRef]
- Ray, M.; Ray, A.; Dash, S.; Mishra, A.; Achary, K.G.; Nayak, S.; Singh, S. Fungal Disease Detection in Plants: Traditional Assays, Novel Diagnostic Techniques and Biosensors. Biosens. Bioelectron. 2017, 87, 708–723. [Google Scholar] [CrossRef] [PubMed]
- Fedele, G.; Brischetto, C.; Rossi, V. Biocontrol of Botrytis cinerea on Grape Berries as Influenced by Temperature and Humidity. Front. Plant Sci. 2020, 11, 1232. [Google Scholar] [CrossRef] [PubMed]
- Rasiukevičiūtė, N.; Brazaitytė, A.; Vaštakaitė-Kairienė, V.; Kupčinskienė, A.; Duchovskis, P.; Samuolienė, G.; Valiuškaitė, A. The Effect of Monochromatic LED Light Wavelengths and Photoperiods on Botrytis cinerea. J. Fungi 2021, 7, 970. [Google Scholar] [CrossRef] [PubMed]
- Bi, K.; Liang, Y.; Mengiste, T.; Sharon, A. Killing Softly: A Roadmap of Botrytis cinerea Pathogenicity. Trends Plant Sci. 2023, 28, 211–222. [Google Scholar] [CrossRef]
- Brazaitytė, A.; Vaštakaitė-Kairienė, V.; Sutulienė, R.; Rasiukevičiūtė, N.; Viršilė, A.; Miliauskienė, J.; Laužikė, K.; Valiuškaitė, A.; Dėnė, L.; Chrapačienė, S.; et al. Phenolic Compounds Content Evaluation of Lettuce Grown under Short-Term Preharvest Daytime or Nighttime Supplemental LEDs. Plants 2022, 11, 1123. [Google Scholar] [CrossRef]
- Samuolinė, G.; Sirtautas, R.; Brazaitytė, A.; Viršilė, A.; Duchovskis, P. Supplementary Red-LED Lighting and the Changes in Phytochemical Content of Two Baby Leaf Lettuce Varieties During Three Seasons. J. Food Agric. Environ. 2012, 10, 701–706. [Google Scholar]
- Ashenafi, E.L.; Nyman, W.C.; Holey, J.K.; Mattson, N.S.; Rangarajan, A. Phenotypic Plasticity and Nutritional Quality of Three Kale Cultivars (Brassica oleracea L. Var. acephala) under Field, Greenhouse, and Growth Chamber Environments. Environ. Exp. Bot. 2022, 199, 104895. [Google Scholar]
- Leroux, P.; Elad, Y.; Williamson, B.; Tudzynski, P.; Delen, N. Chemical Control of Botrytis and Its Resistance to Chemical Fungicides. In Botrytis: Biology, Pathology and Control; Springer: Dordrecht, The Netherlands, 2007; pp. 195–222. ISBN 9781402026249. [Google Scholar]
- Fernández-Ortuño, D.; Torés, J.A.; Chamorro, M.; Pérez-García, A.; De Vicente, A. Characterization of Resistance to Six Chemical Classes of Site-Specific Fungicides Registered for Grey Mould Control on Strawberry in Spain. Plant Dis. 2016, 100, 2234–2239. [Google Scholar] [CrossRef]
- Harman, G.E. Myths and Dogmas of Biocontrol Changes in Perceptions Derived from Research on Trichoderma harzinum T-22. Plant Dis. 2000, 84, 377–393. [Google Scholar] [CrossRef] [PubMed]
- Mitchell, C.A.; Dzakovich, M.P.; Gomez, C.; Lopez, R.; Burr, J.R.; Hernández, R.; Kubota, C.; Currey, C.J.; Meng, Q.; Runkle, E.S. Light-Emitting Diodes in Horticulture. In Horticultural Reviews; Janick, J., Ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2015; Volume 43, pp. 1–88. ISBN 9781119107781. [Google Scholar]
- Rahman, M.M.; Field, D.L.; Ahmed, S.M.; Hasan, M.T.; Basher, M.K.; Alameh, K. LED Illumination for High-Quality High-Yield Crop Growth in Protected Cropping Environments. Plants 2021, 10, 2470. [Google Scholar] [CrossRef] [PubMed]
- Appolloni, E.; Pennisi, G.; Zauli, I.; Carotti, L.; Paucek, I.; Quaini, S.; Orsini, F.; Gianquinto, G. Beyond Vegetables: Effects of Indoor LED Light on Specialized Metabolite Biosynthesis in Medicinal and Aromatic Plants, Edible Flowers, and Microgreens. J. Sci. Food Agric. 2022, 102, 472–487. [Google Scholar] [CrossRef] [PubMed]
- He, J.; Qin, L.; Soon Chow, W. Impacts of LED Spectral Quality on Leafy Vegetables: Productivity Closely Linked to Photosynthetic Performance or Associated with Leaf Traits? Int. J. Agric. Biol. Eng. 2019, 12, 16–25. [Google Scholar] [CrossRef]
- Tan, K.K. Red-Far-Red Reversible Photoreaction in the Recovery from Blue-Light Inhibition of Sporulation in Botrytis cinerea. J. Gen. Microbiol. 1974, 82, 201–202. [Google Scholar] [CrossRef]
- Mahlein, A.-K. Plant Disease Detection by Imaging Sensors—Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. Plant Dis. 2016, 100, 241–251. [Google Scholar] [CrossRef] [PubMed]
- Steddom, K.; Bredehoeft, M.W.; Khan, M.; Rush, C.M. Comparison of Visual and Multispectral Radiometric Disease Evaluations of Cercospora Leaf Spot of Sugar Beet. Plant Dis. 2005, 89, 153–158. [Google Scholar] [CrossRef]
- Steiner, U.; Bürling, K.; Oerke, E.-C. Sensorik für einen präzisierten Pflanzenschutz. Gesunde Pflanz. 2008, 60, 131–141. [Google Scholar] [CrossRef]
- Rumpf, T.; Mahlein, A.-K.; Steiner, U.; Oerke, E.-C.; Dehne, H.-W.; Plümer, L. Early Detection and Classification of Plant Diseases with Support Vector Machines Based on Hyperspectral Reflectance. Comput. Electron. Agric. 2010, 74, 91–99. [Google Scholar] [CrossRef]
- Basso, B.; Cammarano, D.D.; De Vita, P. Remotely Sensed Vegetation Indices: Theory and Applications for Crop Management. Riv. Ital. Agrometeorol. 2004, 1, 36–53. [Google Scholar]
- Mahlein, A.-K.; Rumpf, T.; Welke, P.; Dehne, H.-W.; Plümer, L.; Steiner, U.; Oerke, E.-C. Development of Spectral Indices for Detecting and Identifying Plant Diseases. Remote Sens. Environ. 2013, 128, 21–30. [Google Scholar] [CrossRef]
- Neupane, K.; Baysal-Gurel, F. Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review. Remote Sens. 2021, 13, 3841. [Google Scholar] [CrossRef]
- Pechlivani, E.M.; Papadimitriou, A.; Pemas, S.; Giakoumoglou, N.; Tzovaras, D. Low-Cost Hyperspectral Imaging Device for Portable Remote Sensing. Instruments 2023, 7, 32. [Google Scholar] [CrossRef]
- Chojak-Koźniewska, J.; Kuźniak, E.; Zimny, J. The Effects of Combined Abiotic and Pathogen Stress in Plants: Insights from Salinity and Pseudomonas Syringae Pv Lachrymans Interaction in Cucumber. Front. Plant Sci. 2018, 9, 1691. [Google Scholar] [CrossRef] [PubMed]
- Sankaran, S.; Mishra, A.; Ehsani, R.; Davis, C. A Review of Advanced Techniques for Detecting Plant Diseases. Comput. Electron. Agric. 2010, 72, 1–13. [Google Scholar] [CrossRef]
- Giakoumoglou, N.; Pechlivani, E.M.; Sakelliou, A.; Klaridopoulos, C.; Frangakis, N.; Tzovaras, D. Deep Learning-Based Multi-Spectral Identification of Grey Mould. Smart Agric. Technol. 2023, 4, 100174. [Google Scholar] [CrossRef]
- Gröll, K.; Graeff, S.; Claupein, W. Use of Vegetation indices to detect plant diseases. In Agrarinformatik im Spannungsfeld Zwischen Regionalisierung und Globalen Wertschöpfungsketten–Referate der 27. GIL Jahrestagung; Regular Research Papers; Gesellschaft für Informatik e. V.: Bonn, Germany, 2007; pp. 91–94. ISBN 978-3-88579-195-9. [Google Scholar]
- Peter, A.; Tegla, D.; Giurgiulescu, L.; Cozmuta, A.M.; Nicula, C.; Cozmuta, L.M.; Vagelas, I. Development of Ag/TIO2-SiO2-Coated Food Packaging Film and Its Role in Preservation of Green Lettuce During Storage. Carpathian J. Food Sci. Technol. 2015, 7, 88–96. [Google Scholar]
- Chiang, K.S.; Liu, H.I.; Bock, C.H. A Discussion on Disease Severity Index Values. Part I: Warning on Inherent Errors and Suggestions to Maximise Accuracy. Ann. Appl. Biol. 2017, 171, 139–154. [Google Scholar] [CrossRef]
- Weber, H. Managing the White Zone: An Interview with Yale’s Assistant Professor Sparkle on the Impact of Water Management in the Everglades. CID-BioScience Tools that Work Where You Work. 2023. Available online: http://cid-inc.com (accessed on 21 February 2023).
- Gitelson, A.A.; Merzlyak, M.N.; Chivkunova, O.B. Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves. Photochem. Photobiol 2001, 74, 38. [Google Scholar] [CrossRef]
- Pen Uelas, J.; Filella, I.; Lloret, P.; Mun Oz, F.; Vilajeliu, M. Reflectance Assessment of Mite Effects on Apple Trees. Int. J. Remote Sens. 1995, 16, 2727–2733. [Google Scholar] [CrossRef]
- Merzlyak, M.N.; Solovchenko, A.E.; Smagin, A.I.; Gitelson, A.A. Apple Flavonols during Fruit Adaptation to Solar Radiation: Spectral Features and Technique for Non-Destructive Assessment. J. Plant Physiol. 2005, 162, 151–160. [Google Scholar] [CrossRef] [PubMed]
- Gitelson, A.A.; Merzlyak, M.N. Remote Estimation of Chlorophyll Content in Higher Plant Leaves. Int. J. Remote Sens. 1997, 18, 2691–2697. [Google Scholar] [CrossRef]
- Zarcotejada, P.; Berjon, A.; Lopezlozano, R.; Miller, J.; Martin, P.; Cachorro, V.; Gonzalez, M.; Defrutos, A. Assessing Vineyard Condition with Hyperspectral Indices: Leaf and Canopy Reflectance Simulation in a Row-Structured Discontinuous Canopy. Remote Sens. Environ. 2005, 99, 271–287. [Google Scholar] [CrossRef]
- Calderón, R.; Navas-Cortés, J.A.; Lucena, C.; Zarco-Tejada, P.J. High-Resolution Airborne Hyperspectral and Thermal Imagery for Early Detection of Verticillium Wilt of Olive Using Fluorescence, Temperature and Narrow-Band Spectral Indices. Remote Sens. Environ. 2013, 139, 231–245. [Google Scholar] [CrossRef]
- Lichtenthaler, H.K. Vegetation Stress: An Introduction to the Stress Concept in Plants. J. Plant Physiol. 1996, 148, 4–14. [Google Scholar] [CrossRef]
- Blackburn, G.A. Spectral Indices for Estimating Photosynthetic Pigment Concentrations: A Test Using Senescent Tree Leaves. Int. J. Remote Sens. 1998, 19, 657–675. [Google Scholar] [CrossRef]
- Zarco-Tejada, P.J.; Miller, J.R.; Noland, T.L.; Mohammed, G.H.; Sampson, P.H. Scaling-up and Model Inversion Methods with Narrowband Optical Indices for Chlorophyll Content Estimation in Closed Forest Canopies with Hyperspectral Data. IEEE Trans. Geosci. Remote Sens. 2001, 39, 1491–1507. [Google Scholar] [CrossRef]
- Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W.; Harlan, J.C. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. In Earth Resources and Remote Sensing; The Univeristy of Tennessee: Knoxville, TN, USA, 1974; p. 371. [Google Scholar]
- Jordan, C.F. Derivation of Leaf-Area Index from Quality of Light on the Forest Floor. Ecology 1969, 50, 663–666. [Google Scholar] [CrossRef]
- Naidu, R.A.; Perry, E.M.; Pierce, F.J.; Mekuria, T. The Potential of Spectral Reflectance Technique for the Detection of Grapevine Leafroll-Associated Virus-3 in Two Red-Berried Wine Grape Cultivars. Comput. Electron. Agric. 2009, 66, 38–45. [Google Scholar] [CrossRef]
- Gamon, J.A.; Peñuelas, J.; Field, C.B. A Narrow-Waveband Spectral Index That Tracks Diurnal Changes in Photosynthetic Efficiency. Remote Sens. Environ. 1992, 41, 35–44. [Google Scholar] [CrossRef]
- Peñuelas, J.; Filella, I.; Biel, C.; Serrano, L.; Savé, R. The Reflectance at the 950–970 Nm Region as an Indicator of Plant Water Status. Int. J. Remote Sens. 1993, 14, 1887–1905. [Google Scholar] [CrossRef]
- Hernández-Clemente, R.; Navarro-Cerrillo, R.M.; Suárez, L.; Morales, F.; Zarco-Tejada, P.J. Assessing Structural Effects on PRI for Stress Detection in Conifer Forests. Remote Sens. Environ. 2011, 115, 2360–2375. [Google Scholar] [CrossRef]
- Moshou, D.; Bravo, C.; Oberti, R.; West, J.; Bodria, L.; McCartney, A.; Ramon, H. Plant Disease Detection Based on Data Fusion of Hyper-Spectral and Multi-Spectral Fluorescence Imaging Using Kohonen Maps. Real-Time Imaging 2005, 11, 75–83. [Google Scholar] [CrossRef]
- Merzlyak, M.N.; Gitelson, A.A.; Chivkunova, O.B.; Rakitin, V.Y. Non-Destructive Optical Detection of Pigment Changes during Leaf Senescence and Fruit Ripening. Physiol. Plant. 1999, 106, 135–141. [Google Scholar] [CrossRef]
- Vogelmann, J.E.; Rock, B.N.; Moss, D.M. Red Edge Spectral Measurements from Sugar Maple Leaves. Int. J. Remote Sens. 1993, 14, 1563–1575. [Google Scholar] [CrossRef]
- Gitelson, A.; Merzlyak, M.N. Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation. J. Plant Physiol. 1994, 143, 286–292. [Google Scholar] [CrossRef]
- Sims, D.A.; Gamon, J.A. Relationships between Leaf Pigment Content and Spectral Reflectance across a Wide Range of Species, Leaf Structures and Developmental Stages. Remote Sens. Environ. 2002, 81, 337–354. [Google Scholar] [CrossRef]
- Datt, A. A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Tests Using Eucalyptus Leaves. J. Plant Physiol. 1999, 154, 30–36. [Google Scholar] [CrossRef]
- Imada, K.; Tanaka, S.; Ibaraki, Y.; Yoshimura, K.; Ito, S. Antifungal Effect of 405-Nm Light on Botrytis cinerea. Lett. Appl. Microbiol. 2014, 59, 670–676. [Google Scholar] [CrossRef]
- Canessa, P.; Schumacher, J.; Tudzynski, P.P.; Hevia, M.A.; Larrondo, L.F. Assessing the Effects of Light on Differentiation and Virulence of the Plant Pathogen Botrytis Cinerea: Characterization of the White Collar Complex. PLoS ONE 2013, 8, e84223. [Google Scholar] [CrossRef]
- Courbier, S.; Grevink, S.; Sluijs, E.; Bonhomme, P.; Kajala, K.; Van Wees, S.C.M.; Pierik, R. Far-red Light Promotes Botrytis cinerea Disease Development in Tomato Leaves via Jasmonate-dependent Modulation of Soluble Sugars. Plant Cell Environ. 2020, 43, 2769–2781. [Google Scholar] [CrossRef] [PubMed]
- Hamedalla, A.M.; Ali, M.M.; Ali, W.M.; Ahmed, M.A.A.; Kaseb, M.O.; Kalaji, H.M.; Gajc-Wolska, J.; Yousef, A.F. Increasing the Performance of Cucumber (Cucumis sativus L.) Seedlings by LED Illumination. Sci. Rep. 2022, 12, 852. [Google Scholar] [CrossRef]
- Rusakov, D.V.; Kanash, E.V. Spectral Characteristics of Leaves Diffuse Reflection in Conditions of Soil Drought: A Study of Soft Spring Wheat Cultivars of Different Drought Resistance. Plant Soil Environ. 2022, 68, 137–145. [Google Scholar] [CrossRef]
- Chen, Y.; Zhou, B.; Li, J.; Tang, H.; Tang, J.; Yang, Z. Formation and Change of Chloroplast-Located Plant Metabolites in Response to Light Conditions. Int. J. Mol. Sci. 2018, 19, 654. [Google Scholar] [CrossRef]
- Vaštakaitė-Kairienė, V.; Rasiukevičiūtė, N.; Dėnė, L.; Chrapačienė, S.; Valiuškaitė, A. Determination of Specific Parameters for Early Detection of Botrytis cinerea in Lettuce. Horticulturae 2021, 8, 23. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Kupčinskienė, A.; Brazaitytė, A.; Rasiukevičiūtė, N.; Valiuškaitė, A.; Morkeliūnė, A.; Vaštakaitė-Kairienė, V. Vegetation Indices for Early Grey Mould Detection in Lettuce Grown under Different Lighting Conditions. Plants 2023, 12, 4042. https://doi.org/10.3390/plants12234042
Kupčinskienė A, Brazaitytė A, Rasiukevičiūtė N, Valiuškaitė A, Morkeliūnė A, Vaštakaitė-Kairienė V. Vegetation Indices for Early Grey Mould Detection in Lettuce Grown under Different Lighting Conditions. Plants. 2023; 12(23):4042. https://doi.org/10.3390/plants12234042
Chicago/Turabian StyleKupčinskienė, Asta, Aušra Brazaitytė, Neringa Rasiukevičiūtė, Alma Valiuškaitė, Armina Morkeliūnė, and Viktorija Vaštakaitė-Kairienė. 2023. "Vegetation Indices for Early Grey Mould Detection in Lettuce Grown under Different Lighting Conditions" Plants 12, no. 23: 4042. https://doi.org/10.3390/plants12234042
APA StyleKupčinskienė, A., Brazaitytė, A., Rasiukevičiūtė, N., Valiuškaitė, A., Morkeliūnė, A., & Vaštakaitė-Kairienė, V. (2023). Vegetation Indices for Early Grey Mould Detection in Lettuce Grown under Different Lighting Conditions. Plants, 12(23), 4042. https://doi.org/10.3390/plants12234042