Non-Invasive Monitoring of the Thermal and Morphometric Characteristics of Lettuce Grown in an Aeroponic System through Multispectral Image System
Source abstract
Abstract
Aeroponics is a soilless cultivation technology integrating plant nutrition, physiology, ecological environment, agricultural automation and horticulture. One of the soilless advantages is that a non-invasive observation of the root system growth development is possible. This paper presents a vegetative growth evaluation of lettuce plants in an aeroponic chamber, where root and leaf development parameters were measured in three lettuce crops through plant images captured in the visible (VIS), near infrared (NIR) and far infrared (IR) spectra. A total of ninety lettuce plants was transplanted for this research, thirty for each experimental crop. The three lettuce crops were grown for thirty days in an aeroponic growth plant chamber inside a greenhouse under favorable conditions. The morphometric and thermal parameters of the lettuce roots (perimeter, area, length and average temperature) and leaves (perimeter, area and average temperature) were evaluated for each crop along ten image-capturing sessions through an implemented multispectral vision system. The average values of the root and leaf morphometric parameters obtained with the implemented imaging system along the lettuce growing period were statistically analyzed with Tukey testing. The obtained analysis results show no significant difference for a value of p ≤ 0.05 in 86.67%. Hence, the morphometric parameters can be used to characterize the vegetative lettuce growth in aeroponic crops. On the other hand, a correlation analysis was conducted between the thermal parameters computed with the root and leaf thermal image processing and the measured ambient temperature. The results were: R = 0.945 for correlation between ambient and leaf temperature, R = 0.963 for correlation between ambient and root temperature and R = 0.977 for leaf and root temperature. According to these results, the plant temperature is highly correlated with the ambient temperature in an aeroponic crop. The obtained study results suggest that multispectral image processing is a useful non-invasive tool to estimate the vegetative root and leaf growth parameters of aeroponic lettuce plants in a greenhouse.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Greenhouse Technology and Climate Control · Leaf Properties and Growth Measurement · Smart Agriculture and AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Division of Postgraduate Studies and Research, Tecnológico Nacional de México en Celaya, Celaya 38010, Mexico
Department of Electronics Engineering, Tecnológico Nacional de México en Celaya, Celaya 38010, Mexico
Laboratory of Biosystems Engineering, Faculty of Engineering, Campus Amazcala, Autonomous University of Queretaro, Querétaro 76265, Mexico
Funding
CONACyT · Tecnológico Nacional de Mexico
References (58)
Show source-provided references
1. Thomas, J.A., Vasiliev, M., Nur-E-Alam, M., and Alameh, K. (2020). Increasing the Yield of Lactuca sativa, L. in Glass Greenhouses through Illumination Spectral Filtering and Development of an Optical Thin Film Filter. Sustainability, 12.10.20944/preprints202003.0417.v1 ↗
2. Macuphe, N., Oguntibeju, O., and Nchu, F. (2021). Evaluating the Endophytic Activities of Beauveria bassiana on the Physiology, Growth, and Antioxidant Activities of Extracts of Lettuce (Lactuca sativa L.). Plants, 10.10.3390/plants10061178 ↗
3. Sarkar, D., Uddain, J., Azad, O.K., Islam, J., Rahman, M., Choi, K.-Y., and Naznin, M. (2021). Estimation of Yield, Photosynthetic Rate, Biochemical, and Nutritional Content of Red Leaf Lettuce (Lactuca sativa L.) Grown in Organic Substrates. Plants, 10.10.3390/plants10061220 ↗
4. Dynamic Responses of Ascorbate Pool and Metabolism in Lettuce to Light Intensity at Night Time under Continuous Light Provided by Red and Blue LEDs Dynamic Responses of Ascorbate Pool and Metabolism in Lettuce to Institute of Environment and Sustainable Development in Agriculture10.3390/plants10020214 ↗
5. Modern plant cultivation technologies in agriculture under controlled environment: A review on aeroponics10.1080/17429145.2018.1472308 ↗
6. Li, Q., Li, X., Tang, B., and Gu, M. (2018). Growth Responses and Root Characteristics of Lettuce Grown in Aeroponics, Hydroponics, and Substrate Culture. Horticulturae, 4.10.20944/preprints201809.0108.v1 ↗
7. Koukounaras, A. (2021). Advanced Greenhouse Horticulture: New Technologies and Cultivation Practices. Horticulturae, 7.10.3390/horticulturae7010001 ↗
8. Formisano, L., Ciriello, M., Cirillo, V., Pannico, A., El-Nakhel, C., Cristofano, F., Duri, L., Giordano, M., Rouphael, Y., and De Pascale, S. (2021). Divergent Leaf Morpho-Physiological and Anatomical Adaptations of Four Lettuce Cultivars in Response to Different Greenhouse Irradiance Levels in Early Summer Season. Plants, 10.10.3390/plants10061179 ↗
9. Kerstens, M., Hesen, V., Yalamanchili, K., Bimbo, A., Grigg, S., Opdenacker, D., Beeckman, T., Heidstra, R., and Willemsen, V. (2021). Nature and Nurture: Genotype-Dependent Differential Responses of Root Architecture to Agar and Soil Environments. Genes, 12.10.3390/genes12071028 ↗
10. Root engineering in maize by increasing cytokinin degradation causes enhanced root growth and leaf mineral enrichment10.1007/s11103-021-01173-5 ↗
11. Regulation of plant root system architecture: Implications for crop advancement10.1016/j.copbio.2014.11.015 ↗
12. Untapping root system architecture for crop improvement10.1093/jxb/erw262 ↗
13. Detection of nutrition deficiencies in plants using proximal images and machine learning: A review10.1016/j.compag.2019.04.035 ↗
14. Hyperspectral machine vision as a tool for water stress severity assessment in soilless tomato crop10.1016/j.biosystemseng.2017.11.002 ↗
15. Detection of early plant stress responses in hyperspectral images10.1016/j.isprsjprs.2014.03.016 ↗
16. Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance10.1016/j.scitotenv.2016.08.014 ↗
17. Quantitative determination of rice starch based on hyperspectral imaging technology10.1080/10942912.2017.1326058 ↗
18. Yu, K.Q., Zhao, Y.R., Li, X.L., Shao, Y.N., Liu, F., and He, Y. (2014). Hyperspectral imaging for mapping of total nitrogen spatial distribution in pepper plant. PLoS ONE, 9.10.1371/journal.pone.0116205 ↗
19. A polarized hyperspectral imaging system for in vivo detection: Multiple applications in sunflower leaf analysis10.1016/j.compag.2019.02.008 ↗
20. Li, X., Li, R., Wang, M., Liu, Y., Zhang, B., and Zhou, B.Z.A.J. (2018). Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables. Hyperspectral Imaging in Agriculture, Food and Environment, IntechOpen.10.5772/intechopen.72250 ↗
21. Optimized angles of the swing hyperspectral imaging system for single corn plant10.1016/j.compag.2018.11.030 ↗
22. Improving in-row weed detection in multispectral stereoscopic images10.1016/j.compag.2009.07.001 ↗
23. Integrating multispectral reflectance and fluorescence imaging for defect detection on apples10.1016/j.compag.2005.10.002 ↗
24. Comparison of machine learning methods for citrus greening detection on UAV multispectral images10.1016/j.compag.2020.105234 ↗
25. Development of multispectral imaging algorithm for detection of frass on mature red tomatoes10.1016/j.postharvbio.2014.01.022 ↗
26. Detection of contaminated hazelnuts and ground red chili pepper flakes by multispectral imaging10.1016/j.compag.2011.03.005 ↗
27. Fuzzy logic control of a multispectral imaging sensor for in-field plant sensing10.1016/j.compag.2007.09.008 ↗
28. Leaf Scanner: A portable and low-cost multispectral corn leaf scanning device for precise phenotyping10.1016/j.compag.2019.105069 ↗
29. Deep learning for classification and severity estimation of coffee leaf biotic stress10.1016/j.compag.2019.105162 ↗
30. Application of infrared thermal imaging for the rapid diagnosis of crop disease10.1016/j.ifacol.2018.08.184 ↗
31. Sensitivity of thermal imaging and infrared thermometry to detect water status changes in Euonymus japonica plants irrigated with saline reclaimed water10.1016/j.biosystemseng.2015.02.014 ↗
32. Infrared thermography used to diagnose the effects of salinity in a soilless culture10.1080/17686733.2013.763471 ↗
33. Peach variety detection using VIS-NIR spectroscopy and deep learning10.1016/j.compag.2020.105553 ↗
34. Rapid Determination of Leaf Water Content Using VIS/NIR Spectroscopy Analysis with Wavelength Selection10.1155/2012/276795 ↗
35. Evaluation of plant seedling water stress using dynamic fluorescence index with blue LED-based fluorescence imaging10.1016/j.compag.2010.03.005 ↗
36. Water stress detection in potato plants using leaf temperature, emissivity, and reflectance
37. Contribution of thermal imaging to fertigation in soilless culture10.1007/s10973-013-3529-x ↗
38. Blaya-Ros, P.J., Blanco, V., Domingo, R., Soto-Valles, F., and Torres-Sánchez, R. (2020). Feasibility of Low-Cost Thermal Imaging for Monitoring Water Stress in Young and Mature Sweet Cherry Trees. Appl. Sci., 10.10.3390/app10165461 ↗
39. Cavaco, A.M., Utkin, A.B., da Silva, J.M., and Guerra, R. (2022). Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture. Appl. Sci., 12.10.3390/app12030997 ↗
40. Advanced Application of Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy in Plant Disease Diagnostics: A Review10.1021/acs.jafc.0c07205 ↗
41. Poštić, D., Štrbanović, R., Tabaković, M., Popović, T., Ćirić, A., Banjac, N., Trkulja, N., and Stanisavljević, R. (2021). Germination and the Initial Seedling Growth of Lettuce, Celeriac and Wheat Cultivars after Micronutrient and a Biological Application Pre-Sowing Seed Treatment. Plants, 10.10.3390/plants10091913 ↗
42. Leitão, I., Martins, L., Carvalho, L., Oliveira, M., Marques, M., and Mourato, M. (2021). Acetaminophen Induces an Antioxidative Response in Lettuce Plants. Plants, 10.10.3390/plants10061152 ↗
43. Wu, B.-S., MacPherson, S., and Lefsrud, M. (2021). Filtering Light-Emitting Diodes to Investigate Amber and Red Spectral Effects on Lettuce Growth. Plants, 10.10.3390/plants10061075 ↗
44. Zhang, L., Martinelli, E., Senizza, B., Miras-Moreno, B., Yildiztugay, E., Arikan, B., Elbasan, F., Ak, G., Balci, M., and Zengin, G. (2021). The Combination of Mild Salinity Conditions and Exogenously Applied Phenolics Modulates Functional Traits in Lettuce. Plants, 10.10.3390/plants10071457 ↗
45. The effects of different night-time temperatures and cultivation durations on the polyphenolic contents of lettuce: Application of principal component analysis10.1016/j.jare.2015.01.004 ↗
46. Corrado, G., Lucini, L., Miras-Moreno, B., Zhang, L., El-Nakhel, C., Colla, G., and Rouphael, Y. (2021). Intraspecific Variability Largely Affects the Leaf Metabolomics Response to Isosmotic Macrocation Variations in Two Divergent Lettuce (Lactuca sativa L.) Varieties. Plants, 10.10.3390/plants10010091 ↗
47. Vaštakaitė-Kairienė, V., Rasiukevičiūtė, N., Dėnė, L., Chrapačienė, S., and Valiuškaitė, A. (2022). Determination of Specific Parameters for Early Detection of Botrytis cinerea in Lettuce. Horticulturae, 8.10.3390/horticulturae8010023 ↗
48. Artificial intelligence approaches to predict growth, harvest day, and quality of lettuce (Lactuca sativa L.) in a IoT-enabled greenhouse system10.1016/j.biosystemseng.2021.09.015 ↗
49. Comparison of growth characteristics, functional qualities, and texture of hydroponically grown and soil-grown lettuce10.1016/j.lwt.2021.111931 ↗
50. Jayalath, T.C., and van Iersel, M.W. (2021). Canopy Size and Light Use Efficiency Explain Growth Differences between Lettuce and Mizuna in Vertical Farms. Plants, 10.10.3390/plants10040704 ↗
51. Semenova, N., Smirnov, A., Grishin, A., Pishchalnikov, R., Chesalin, D., Gudkov, S., Chilingaryan, N., Skorokhodova, A., Dorokhov, A., and Izmailov, A. (2021). The Effect of Plant Growth Compensation by Adding Silicon-Containing Fertilizer under Light Stress Conditions. Plants, 10.10.3390/plants10071287 ↗
52. Blue Light-emitting Diode Light Irradiation of Seedlings Improves Seedling Quality and Growth after Transplanting in Red Leaf Lettuce10.21273/HORTSCI.45.12.1809 ↗
53. Spatiotemporal dynamics of leaf transpiration quantified with time-series thermal imaging10.1016/j.agrformet.2018.02.023 ↗
54. Alkahtani, M.D.F., Hafez, Y.M., Attia, K., Al-Ateeq, T., Ali, M.A.M., Hasanuzzaman, M., and Abdelaal, K.A.A. (2021). Bacillus thuringiensis and Silicon Modulate Antioxidant Metabolism and Improve the Physiological Traits to Confer Salt Tolerance in Lettuce. Plants, 10.10.3390/plants10051025 ↗
55. Physical and mechanical properties of hydroponic lettuce for automatic harvesting
56. Effects of ozone-treated domestic sludge on hydroponic lettuce growth and nutrition10.1016/S2095-3119(17)61868-9 ↗
57. Won, J.-H., Cho, B.-H., Kim, Y.-H., and Lee, J.-H. (2022). Growth Characteristics of Lettuce Relative to Generation Position of Air Anions in a Closed-Type Plant Factory. Horticulturae, 8.10.3390/horticulturae8040346 ↗
58. IoT Based Root Stress Detection for Lettuce Culture Using Infrared Leaf Temperature Sensor and Light Intensity Sensor10.1007/s11277-020-07219-z ↗