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][Green Version]
- 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]
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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