Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Site, Conditions, and Design
2.2. Irrigation Water Requirements
2.3. Thermal Measurements
2.4. Digital RGB Imaging
2.5. Plant Trait Measurements
2.6. Modeling
2.6.1. Stepwise Multiple Linear Regression (SMLR)
2.6.2. Adaptive Neuro-Fuzzy Inference System (ANFIS) Model
2.6.3. Genetic Algorithm (GA)
2.6.4. Data Analysis
3. Results and Discussion
3.1. Combined Analysis of Variance for Measured Plant Traits, and Thermal and RGB Imaging Indices
3.2. Impact of the Irrigation Regime on the Growth, Water Status, and Production of Potato
3.3. Performance of Remote Sensing Indices for Assessing the Measured Plant Traits
3.3.1. Performance of Thermal Index
3.3.2. Performance of the RGB Indices
3.4. Integration of the Thermal and RGB Imagery Indices for the Assessment of the Measured Plant Traits Using Stepwise Multiple Linear Regression Models
3.5. Performance of ANFIS-GA Models Based on All Thermal and RGB Imagery Indices for Predicting the Measured Plant Traits
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Perry, C.; Steduto, P.; Allen, R.G.; Burt, C.M. Increasing productivity in irrigated agriculture: Agronomic constraints and hydrological realities. Agric. Water Manag. 2009, 96, 1517–1524. [Google Scholar] [CrossRef] [Green Version]
- FAOSTAT. FAO Statistics Division: Quebec City, QC, Canada, October 2017. Available online: http://www.fao.org/faostat/en/#data/QC (accessed on 15 March 2021).
- FAOSTAT. Food and Agriculture Organization of the United Nations Statistics Database. 2020. Available online: http://www.fao.org/faostat/en/#data/QC (accessed on 21 June 2020).
- Fereres, F.; Soriano, M.A. Deficit irrigation for reducing agricultural water use. J. Exp. Bot. 2007, 58, 147–159. [Google Scholar] [CrossRef] [Green Version]
- El-Hendawy, S.; Hassan, W.; Al-Suhaibani, N.; Schmidhalter, U. Spectral assessment of drought toleranceindices and grain yield in advanced spring wheat lines grown under full and limited water irrigation. Agric. Water Manag. 2017, 182, 1–12. [Google Scholar] [CrossRef]
- Vreugdenhil, D.; Bradshaw, J.; Gebhardt, C.; Govers, F.; Mackerron, D.K.L.; Taylor, M.A.; Ross, H.A. Potato Biology and Biotechnology: Advances and Perspectives; Elsevier: Oxford, UK, 2011. [Google Scholar]
- Ahmadi, S.H.; Plauborg, F.; Andersen, M.N.; Sepaskhah, A.R.; Jensen, C.R.; Hansen, S. Effects of irrigation strategies and soils on field grown potatoes: Root distribution. Agric. Water Manag. 2011, 98, 1280–1290. [Google Scholar] [CrossRef]
- Matovic, G.; Brocic, Z.; Djuricin, S.; Gregoric, E.; Bodroza, D. Profitability assessment of potato production applying different irrigation methods. Irrig. Drain. 2016, 65, 502–513. [Google Scholar] [CrossRef] [Green Version]
- Ierna, A.; Mauromicale, G. Potato growth, yield and water productivity response todifferent irrigation and fertilization regimes. Agric. Water Manag. 2018, 201, 21–26. [Google Scholar] [CrossRef]
- Soltys-Kalina, D.; Plich, J.; Strzelczyk-Żyta, D.; Sliwka, J.; Marczewski, W. The effect of drought stress on the leaf relative water content and tuber yield of a half-sib family of ‘Katahdin’-derived potato cultivars. Breed. Sci. 2016, 66, 328–331. [Google Scholar] [CrossRef] [Green Version]
- Romero, A.P.; Alarcon, A.; Galeano, C. Physiological assessment of water stress in potato using spectral information. Front. Plant Sci. 2017, 8, 1608. [Google Scholar] [CrossRef] [PubMed]
- Shock, C.C.; Pereira, A.B.; Eldredge, E.P. Irrigation best management practices for potato. Am. J. Potato Res. 2007, 84, 29–37. [Google Scholar] [CrossRef]
- Brocic, Z.; Jovanovic, Z.; Stikic, R.; Radovic, B.V.; Mojevic, M. Partial root drying: New approach for potato irrigation. Cereal Res. Commun. 2009, 37, 229–232. [Google Scholar]
- Byrd, S.A.; Rowland, D.L.; Bennett, J.; Zotarelli, L.; Wright, D.; Alva, A.; Nordgaard, J. Reductions in a commercial potato irrigation schedule during tuberbulking in Florida: Physiological, yield, and quality effects. J. Crop Improv. 2014, 28, 660–679. [Google Scholar] [CrossRef]
- Karam, F.; Amacha, N.; Fahed, S.; El Asmar, T.; Domínguez, A. Response of potato to full and deficit irrigation under semiaridclimate: Agronomic and economic implications. Agric. Water Manag. 2014, 142, 144–151. [Google Scholar] [CrossRef]
- El-Abedin, T.K.Z.; Mattar, M.A.; Alazba, A.A.; Al-Ghobari, H.M. Comparative effects of two water-saving irrigation techniqueson soil water status, yield, and water use efficiency in potato. Sci. Hortic. 2017, 225, 525–532. [Google Scholar] [CrossRef]
- Wang, X.; Guo, T.; Wang, Y.; Xing, Y.; Wang, Y.; He, X. Exploring the optimization of water and fertilizer management practices forpotato production in the sandy loam soils of Northwest China based on PCA. Agric. Water Manag. 2020, 237, 106180. [Google Scholar] [CrossRef]
- Djaman, K.; Irmak, S.; Koudahe, K.; Allen, S. Irrigation management in potato (Solanum tuberosum L.) production: A review. Sustainability 2021, 13, 1504. [Google Scholar] [CrossRef]
- Gutierrez, M.; Reynolds, M.P.; Klatt, A.R. Association of Water spectral indices with plant and soil water relations in contrasting wheat genotypes. J. Exp. Bot. 2010, 61, 3291–3303. [Google Scholar] [CrossRef] [Green Version]
- Wang, B.; Liu, D.L.; Asseng, S.; Macadam, I.; Yu, Q. Impact of Climate Change on Wheat Flowering Time in Eastern Australia. Agric. For. Meteorol. 2015, 209, 11–21. [Google Scholar] [CrossRef]
- El-Hendawy, S.E.; Al-Suhaibani, N.; Dewir, Y.H.; El-Sayed, S.; Alotaibi, M.; Hassan, W.M.; Refay, Y.; Tahir, M.U. Ability of modified spectral reflectance indices for estimating growth and photosynthetic efficiency of wheat under saline field conditions. Agronomy 2019, 9, 35. [Google Scholar] [CrossRef] [Green Version]
- Zhang, F.; Zhou, G. Estimation of vegetation water content using hyperspectral vegetation indices: A comparison of crop water indicators in response to water stress treatments for summer maize. BMC Ecol. 2019, 19, 18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Elmetwalli, A.H.; El-Hendawy, S.E.; Al-Suhaibani, N.; Alotaibi, M.; Tahir, M.U.; Mubushar, M.; Hassan, W.M.; Elsayed, S. Potential of hyperspectral and thermal proximal sensing for estimating growth performance and yield of soybean exposed to different drip irrigation regimes under arid conditions. Sensors 2020, 20, 6569. [Google Scholar] [CrossRef]
- Garriga, M.; Romero-Bravo, S.; Estrada, F.; Méndez-Espinoza, A.M.; González-Martínez, L.; Matus, I.A.; Castillo, D.; Lobos, G.A.; Del Pozo, A. Estimating carbon isotope discrimination and grain yield of bread wheat grown under water-limited and full irrigation conditions by hyperspectral canopy reflectance and multilinear regression analysis. Int. J. Remote Sens. 2021, 42, 2848–2871. [Google Scholar] [CrossRef]
- Jackson, R.D.; Idso, S.B.; Reginato, R.J.; Pinter, P.J., Jr. Canopy temperature as acrop water stress indicator. Water Resour. Res. 1981, 17, 1133–1138. [Google Scholar] [CrossRef]
- Dejonge, K.C.; Taghvaeian, S.; Trout, T.J.; Comas, L.H. Comparison of canopy temperature-based water stress indices for maize. Agric. Water Manag. 2015, 156, 51–62. [Google Scholar] [CrossRef]
- Masseroni, D.; Addin, J.; Tyrrell, R.; Mareels, I.; Gandolfi, C.; Facchi, A. Towards a smart automated surface irrigation management in rice-growing areas in Italy. Agric. Eng. 2017, 48, 42–48. [Google Scholar] [CrossRef] [Green Version]
- Medrano, H.; Escalona, J.M.; Bota, J.; Gulías, J.; Flexas, J. Regulation of photosynthesis of C3 plants in response to progressive drought: Stomatal conductance as a reference parameter. Ann. Bot. 2002, 89, 895–905. [Google Scholar] [CrossRef]
- Hura, T.; Hura, K.; Grzesiak, M.; Rzepka, A. Effect of long-term drought stress on leaf gas exchange and fluorescence parameters in C3 and C4 plants. Acta Physiol. Plant. 2007, 29, 103–113. [Google Scholar] [CrossRef]
- Jones, H.G. Application of thermal imaging and infrared sensing in plant physiology and eco-physiology. In Advances in Botanical Research; Academic Press: Cambridge, MA, USA, 2004; pp. 107–163. [Google Scholar]
- Lapidot, O.; Ignat, T.; Rud, R.; Rog, I.; Alchanati, V.; Klein, T. Use of thermal imaging to detect evaporative cooling in coniferous and broadleaved tree species of the Mediterranean maquis. Agric. For. Meteorol. 2019, 271, 285–294. [Google Scholar] [CrossRef]
- Pirasteh-Anosheh, H.; Saed-Moucheshi, A.; Pakniyat, H.; Pessarakli, M. Stomatal responses to drought stress. In Water Stress and Crop Plants: A Sustainable Approach, 1st ed.; Ahmad, P., Ed.; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2016; Volume 1. [Google Scholar]
- Elsayed, S.; Rischbeck, P.; Schmidhalter, U. Comparing the performance of active and passive reflectance sensors to assess the normalized relative canopy temperature and grain yield of drought-stressed barley cultivars. Field Crops Res. 2015, 177, 148–160. [Google Scholar] [CrossRef]
- Irmak, S.; Haman, D.Z.; Bastug, R. Determination of crop water stress index for irrigation timing and yield estimation of corn. Agron. J. 2002, 92, 1221–1227. [Google Scholar] [CrossRef]
- O’Shaughnessy, S.A.; Evett, S.R.; Colaizzi, P.D.; Howell, T.A. Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton. Agric. Water Manag. 2011, 98, 1523–1535. [Google Scholar] [CrossRef]
- Elsayed, S.; Elhoweity, M.; Ibrahim, H.H.; Dewir, Y.H.; Migdadic, H.M.; Schmidhalter, U. Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes. Agric. Water Manag. 2017, 189, 98–110. [Google Scholar] [CrossRef]
- Sakamoto, T.; Gitelson, A.A.; Nguyrobertson, A.L.; Arkebauer, T.J.; Wardlow, B.D. An alternative method using digital cameras for continuous monitoring of crop status. Agric. For. Meteorol. 2012, 154, 113–126. [Google Scholar] [CrossRef] [Green Version]
- Wang, W.; Yao, X.; Yao, X.F.; Tian, Y.C.; Liu, X.J.; Ni, J.; Cao, W.X.; Zhu, Y. Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat. Field Crop. Res. 2012, 129, 90–98. [Google Scholar] [CrossRef]
- Sulistyo, S.B.; Woo, W.L.; Dlay, S.S. Regularized neural networks fusion and genetic algorithm based on-field nitrogen status estimation of wheat plants. IEEE Trans. Ind. Inform. 2017, 13, 103–114. [Google Scholar] [CrossRef] [Green Version]
- Wenting, H.; Yu, S.; Tengfei, X.; Xiangwei, C.; Ooi, S.K. Detecting maize leaf water status by using digital RGB images. Int. J. Agr. Biol. Eng. 2014, 7, 45–53. [Google Scholar]
- Petrozza, A.; Santaniello, A.; Summerer, S.; Di Tommaso, G.; Di Tommaso, D.; Paparelli, E.; Piaggesic, A.; Peratab, P.; Cellini, F. Physiological responses to Megafol treatments in tomato plants under drought stress: A phenomic and molecular approach. Sci. Hortic. 2014, 174, 185–192. [Google Scholar] [CrossRef]
- Elsayed, S.; Barmeier, G.; Schmidhalter, U. Passive reflectance sensing and digital image analysis allows for assessing the biomass and nitrogen status of wheat in early and late tillering stages. Front. Plant Sci. 2018, 9, 1478. [Google Scholar] [CrossRef] [PubMed]
- Paruelo, J.M.; Lauenroth, W.K.; Roset, P.A. Estimating aboveground plant biomass using a photographic technique. J. Range Manag. 2000, 53, 190–193. [Google Scholar] [CrossRef]
- Lee, J.K.; Lee, W.B. Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis. Eur. J. Agron. 2013, 48, 57–65. [Google Scholar] [CrossRef]
- Kipp, S.; Mistele, B.; Baresel, P.; Schmidhalter, U. High-throughput phenotyping early plant vigour of winter wheat. Eur. J. Agron. 2014, 52, 271–278. [Google Scholar] [CrossRef]
- Baresel, J.P.; Rischbeck, P.; Hu, Y.; Kipp, S.; Hu, Y.; Barmeier, G.; Mistele, B.; Schmidhalter, U. Use of a digital camera as alternative method for non-destructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat. Comput. Electron. Agric. 2017, 140, 25–33. [Google Scholar] [CrossRef]
- Li, B.; Xu, X.; Zhang, L.; Han, J.; Bian, C.; Li, G.; Liu, J.; Jin, L. Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging. ISPRS J. Photogramm. Remote Sens. 2020, 162, 161–172. [Google Scholar] [CrossRef]
- Arakawa, M.; Yamashita, Y.; Funatsu, K. Genetic algorithm-based wavelength selection method for spectral calibration. J. Chemom. 2011, 25, 10–19. [Google Scholar] [CrossRef]
- Yao, X.; Huang, Y.; Shang, G.; Zhou, C.; Cheng, T.; Tian, Y.; Cao, W.; Zhu, Y. Evaluation of six algorithms to monitor wheat leaf nitrogen concentration. Remote Sens. 2015, 7, 14939–14966. [Google Scholar] [CrossRef] [Green Version]
- Zheng, H.; Li, W.; Jiang, J.; Liu, Y.; Cheng, T.; Tian, Y.; Zhu, Y.; Cao, W.; Zhang, Y.; Yao, X. A Comparative assessment of different modeling algorithms for estimating leaf nitrogen content in winter wheat using multispectral images from an unmanned aerial vehicle. Remote Sens. 2018, 10, 2026. [Google Scholar] [CrossRef] [Green Version]
- Coast, O.; Shah, S.; Ivakov, A.; Gaju, O.; Wilson, P.B.; Posch, B.C.; Callum, J.B.; Negrini, A.C.A.; Evans, J.R.; Condon, A.G.; et al. Predicting dark respiration rates of wheat leaves from hyperspectral reflectance. Plant Cell Environ. 2019, 42, 2133–2150. [Google Scholar] [CrossRef] [Green Version]
- Khademi, F.; Akbari, M.; Jamal, S.M.; Nikoo, M. Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Front. Struct. Civ. Eng. 2017, 11, 90–99. [Google Scholar] [CrossRef]
- Bui, D.T.; Bui, Q.T.; Nguyen, Q.P.; Pradhan, B.; Nampak, H.; Trinh, P.T. A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest firesusceptibility modeling at a tropical area. Agric. For. Meteorol. 2017, 233, 32–44. [Google Scholar]
- Bui, Q.T.; Pham, M.V.; Nguyen, Q.H.; Nguyen, L.X.; Pham, H.M. Whale optimization algorithm and adaptive neuro-fuzzy inference system: A hybrid method for feature selection and land pattern classification. Int. J. Remote Sens. 2019, 40, 5078–5093. [Google Scholar] [CrossRef]
- Jang, J.S.R. ANFIS: Adaptive network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 1993, 23, 665–685. [Google Scholar] [CrossRef]
- Chau, K. A review on integration of artificial intelligence into water quality modelling. Mar. Pollut. Bull. 2006, 52, 726–733. [Google Scholar] [CrossRef] [Green Version]
- Kodogiannis, V.S.; Lolis, A. Forecasting financial time series using neural network and fuzzy System based techniques. Neural Comput. Appl. 2002, 11, 90–102. [Google Scholar] [CrossRef]
- Sheta, A.; Turabieh, H. A comparison between genetic algorithms and sequential quadratic programming in solving constrained optimization problems. ICGST Int. J. Artif. Intell. Mach. Learn. 2006, 6, 67–74. [Google Scholar]
- Smith, M. CROPWATA Computer Program for Irrigation Planning and Management; Irrigation and Drainage Paper No. 46; FAO: Rome, Italy, 1992; 125p. [Google Scholar]
- Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration Guidelines for Computing Crop Water Requirements; Irrigation and Drainage Paper 56; FAO: Rome, Italy, 1998. [Google Scholar]
- Gonzalez-Dugo, M.P.; Moran, M.S.; Mateos, L.; Bryant, R. Canopy temperature variability as an indicator of crop water stress severity. Irrig. Sci. 2006, 24, 233–240. [Google Scholar] [CrossRef]
- Fauzi, J.F.; Tolle, H.; Dewi, R.K. Implementasi metode RGB To HSV pada aplikasi pengenalan mata uang kertas berbasis Android untuk tuna netra. J. Pengemb. Teknol. Inf. Ilmu Komput. 2018, 2, 2319–2325. [Google Scholar]
- Yossya, E.H.; Pranata, J.; Wijaya, T.; Hermawan, H.; Budiharto, W. Mango Fruit Sortation System using Neural Network and Computer Vision. Procedia Comput. Sci. 2017, 116, 569–603. [Google Scholar] [CrossRef]
- Kumaseh, M.R.; Luther, L.; Nainggolan, N. Segmentasi Citra Digital Ikan Menggunakan Metode Thresholding. J. Ilm. Sains 2013, 74–79. [Google Scholar] [CrossRef] [Green Version]
- Verrelst, J.; Schaepman, M.E.; Koetz, B.; Kneubuhler, M. Angular sensitivity analysis of vegetation indices derived from 726 CHRIS/PROBA data. Remote Sens. Environ. 2008, 112, 2341–2353. [Google Scholar] [CrossRef]
- Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127–150. [Google Scholar] [CrossRef] [Green Version]
- Woebbecke, D.; Meyer, G.; von Bargen, K.; Mortensen, D. Plant species identification, size, and enumeration using machine vision techniques on near-binary images. Int. Soc. Opt. Photonics 1993, 1836, 208–219. [Google Scholar]
- Mao, W.; Wang, Y.; Wang, Y. Real-time detection of between-row weeds using machine vision. In Proceedings of the ASAE Annual Meeting, Las Vegas, NV, USA, 27–30 July 2003; American Society of Agricultural and Biological Engineers: St. Joseph, MI, USA, 2003; p. 1. [Google Scholar]
- Hague, T.; Tillett, N.; Wheeler, H. Automated crop and weed monitoring in widely spaced cereals. Precis. Agric. 2006, 7, 21–32. [Google Scholar] [CrossRef]
- Saberioon, M.M.; Amin, M.S.M.; Anuar, A.R.; Gholizadeh, A.; Wayayok, A.; Khairunniza- Bejo, S. Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale. Int. J. Appl. Earth Obs. Geoinf. 2014, 32, 35–45. [Google Scholar] [CrossRef]
- Guijarro, M.; Pajares, G.; Riomoros, I.; Herrera, P.J.; Burgos-Artizzu, X.P.; Ribeiro, A. Automatic segmentation of relevant textures in agricultural images. Comput. Electron. Agric. 2011, 75, 75–83. [Google Scholar] [CrossRef] [Green Version]
- Vasques, G.M.; Grunwald, S.; Sickman, J.O. Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma 2008, 146, 14–25. [Google Scholar] [CrossRef]
- Prasad, K.; Gorai, A.K.; Goyal, P. Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time. Atmos. Environ. 2016, 128, 246–262. [Google Scholar] [CrossRef]
- Sahin, M.; Erol, R. A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games. Math. Comput. Appl. 2017, 22, 43. [Google Scholar]
- Haznedar, B.; Kalinli, A. Training ANFIS using genetic algorithm for dynamic systems identification. Int. J. Intell. Syst. Appl. Eng. 2016, 4, 44–47. [Google Scholar] [CrossRef] [Green Version]
- Raj, R.; Mohan, B.M. Modeling and analysis of the simplest fuzzy PID controller of Takagi–Sugeno type with modified rule base. Soft Comput. 2018, 22, 5147–5161. [Google Scholar] [CrossRef]
- Khadr, M.; Gad, M.; El-Hendawy, S.; Al-Suhaibani, N.; Dewir, Y.H.; Tahir, M.U.; Mubushar, M.; Elsayed, S. The integration of multivariate statistical approaches, hyperspectral reflectance, and data-driven modeling for assessing the quality and suitability of groundwater for irrigation. Water 2021, 13, 35. [Google Scholar] [CrossRef]
- Eseye, A.T.; Lehtonen, M.; Tukia, T.; Uimonen, S.; Millar, R.J. Machine learning based integrated feature selection approach for improved electricity demand forecasting in decentralized energy systems. IEEE Access 2019, 7, 91463–91475. [Google Scholar] [CrossRef]
- Camargo, D.C.; Montoya, F.; Córcoles, J.I.; Ortega, J.F. Modeling the impacts of irrigation treatments on potato growth and development. Agric. Water Manag. 2015, 150, 119–128. [Google Scholar] [CrossRef]
- Foti, S.; Mauromicale, G.; Ierna, A. Influence of irrigation levels on growth and yield of potato cv. Spunta. Potato Res. 1995, 38, 307–318. [Google Scholar] [CrossRef]
- Kashyap, P.S.; Panda, R.K. Effect of irrigation scheduling on potato crop parameters under water stressed conditions. Agric. Water Manag. 2003, 59, 49–66. [Google Scholar] [CrossRef]
- Yuan, B.-Z.; Nishiyama, S.; Kang, Y. Effect different irrigation regimes on the growth and yield of drip-irrigated potato. Agric. Water Manag. 2003, 63, 153–167. [Google Scholar] [CrossRef]
- Lahlou, O.; Ledent, J.-F. Root mass and depth, stolons and roots formed on stolons in four cultivars of potato under water stress. Eur. J. Agron. 2005, 22, 159–173. [Google Scholar] [CrossRef]
- Fleisher, D.H.; Timlin, D.J.; Reddy, V.R. Elevated carbon dioxide and waterstress effects on potato canopy gas exchange, wateruse, and productivity. Agric. For. Meteorol. 2008, 148, 1109–1122. [Google Scholar] [CrossRef]
- Cantore, V.; Yamaç, S.S.; Albrizio, R.; Stellacci, A.M.; Todorovic, M. Yield and water use efficiency of early potato grown under different irrigation regimes. Int. J. Plant Prod. 2014, 8, 409–428. [Google Scholar]
- Deblonde, P.M.K.; Ledent, J.F. Effects of moderate drought conditions on green leaf number, stem height, leaf length and tuber yield of potato cultivars. Eur. J. Agron. 2001, 14, 31–41. [Google Scholar] [CrossRef]
- Fandika, I.R.; Kemp, P.D.; Millner, J.P.; Horne, D.; Roskruge, N. Irrigation and nitrogen effects on tuber yield and water use efficiency of heritage and modern potato cultivars. Agric. Water Manag. 2016, 170, 148–157. [Google Scholar] [CrossRef]
- Ferreira, T.C.; Carr, M.K.V. Responses of potatoes (Solanum tuberosum L.) to irrigation and nitrogen in a hot dry climate: I. Water use. Field Crops Res. 2002, 78, 51–64. [Google Scholar] [CrossRef]
- Board, J.; Kahlon, C.S. Soybean yield formation: What controls it and How it can be improved. In Soybean Physiology and Biochemistry; Louisiana State University Agricultural Center: Baton Rouge, LA, USA, 2011. [Google Scholar] [CrossRef] [Green Version]
- Rauf, S.; Al-Khayri, J.M.; Zaharieva, M.; Monneveux, P.; Khalil, F. Breeding strategies to enhance drought tolerance in crops. In Advances in Plant Breeding Strategies; Agronomic, Abiotic and Biotic Stress Traits; Al-Khayri, J.M., Jain, S.M., Johnson, D.V., Eds.; Springer: Berlin/Heidelberg, Germany, 2016; pp. 1–70. [Google Scholar]
- Anjum, S.A.; Xie, X.Y.; Wang, L.C.; Saleem, M.F.; Man, C.; Lei, W. Morphological, physiological and biochemical responses of plants to drought stress. Afr. J. Agric. Res. 2011, 6, 2026–2032. [Google Scholar]
- Osakabe, Y.; Osakabe, K.; Shinozaki, K.; Tran, L.S.P. Response of plants to water stress. Front. Plant Sci. 2014, 5, 1–8. [Google Scholar] [CrossRef] [Green Version]
- Clauw, P.; Coppens, F.; de Beuf, K.; Dhondt, S.; van Daele, T.; Maleux, K.; Inze, D. Leaf responses to mild drought stress in natural variants of Arabidopsis thaliana. Plant Physiol. 2015, 167, 800–816. [Google Scholar] [CrossRef] [Green Version]
- Olsen, R.L.; Pratt, B.P.; Gump, P.; Kemper, A.; Tallman, G. Red light activates a chloroplast-dependent ion uptake mechanism for stomatal opening under reduced CO2 concentrations in Vicia spp. New Phytol. 2002, 153, 497–508. [Google Scholar] [CrossRef] [Green Version]
- Zeiger, E.; Talbott, L.D.; Frechilla, S.; Srivastava, A.; Zhu, J. The guard cell chloroplast: A perspective for the twenty-first century. New Phytol. 2002, 153, 415–424. [Google Scholar] [CrossRef]
- Schlemmer, M.R.; Francis, D.D.; Shanahan, J.F.; Schepers, J.S. Remotely measuring chlorophyll content in corn leaves with differing nitrogen levels and relative water content. Agron. J. 2005, 97, 106–112. [Google Scholar] [CrossRef] [Green Version]
- Carter, G.A. Primary and secondary effects of the water content on the spectral reflectance of leaves. Am. J. Bot. 1991, 78, 916–924. [Google Scholar] [CrossRef]
- Fleisher, D.H.; Barnaby, J.; Sicher, R.; Resop, J.P.; Timlin, D.J.; Reddy, V.R. Effects of elevated CO2 and cyclic drought on potato under varying radiation regimes. Agric. For. Meteorol. 2013, 171, 270–280. [Google Scholar] [CrossRef]
- Rolando, J.L.; Ramírez, D.A.; Yactayo, W.; Monneveux, P.; Quiroz, R. Leaf greenness as a drought tolerance related trait in potato (Solanum tuberosum L.). Environ. Exp. Bot. 2015, 110, 27–35. [Google Scholar] [CrossRef]
- Ramírez, D.A.; Kreuze, J.; Amoros, W.; Valdivia-Silva, J.E.; Ranck, J.; Garcia, S.; Salas, E.; Yactayo, W. Extreme salinity as a challenge to grow potatoes under mars-like soil conditions: Targeting promising genotypes. Int. J. Astrobiol. 2019, 18, 18–24. [Google Scholar] [CrossRef] [Green Version]
- Casadesus, J.; Kaya, Y.; Bort, J.; Nachit, M.M.; Araus, J.L.; Amor, S.; Ferrazzano, G.; Maalouf, F.; Maccaferri, M.; Martos, V.; et al. Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments. Ann. Appl. Bot. 2007, 150, 227–236. [Google Scholar] [CrossRef]
- Casadesús, J.; Villegas, D. Conventional digital cameras as a tool for assessingleaf area index and biomass for cereal breeding. J. Integr. Plant. Biol. 2014, 56, 7–14. [Google Scholar] [CrossRef]
- Marín, J.; Yousfi, S.; Mauri, P.V.; Parra, L.; Lloret, J.; Masaguer, A. RGB Vegetation Indices, NDVI, and Biomass as Indicators to Evaluate C3 and C4 Turfgrass under Different Water Conditions. Sustainability 2020, 12, 2160. [Google Scholar] [CrossRef] [Green Version]
- Zakaluk, R.; Ranjan, R.S. Artificial neural network modelling of leaf water potential for potatoes using RGB digital images: A greenhouse study. Potato Res. 2007, 49, 255–272. [Google Scholar] [CrossRef]
- Taghvaeian, S.; Chávez, J.L.; Mary, J.; Hattendorf, M.J.; Crookston, M.A. Optical and thermal remote sensing of turfgrass quality, water stress, and water use under different soil and irrigation treatments. Remote Sens. 2013, 5, 2327–2347. [Google Scholar] [CrossRef] [Green Version]
- Yue, J.; Feng, H.; Jin, X.; Yuan, H.; Li, Z.; Zhou, C.; Yang, G.; Tian, Q. A comparison of crop parameters estimation using images from UAV-mounted snapshot hyperspectral sensor and high-definition digital camera. Remote Sens. 2018, 10, 1138. [Google Scholar] [CrossRef] [Green Version]
- Pimstein, A.; Karnieli, A.; Bansal, S.K.; Bonfil, D.J. Exploring remotely sensed technologies for monitoring wheat potassium and phosphorus using field spectroscopy. Field Crops Res. 2011, 121, 125–135. [Google Scholar] [CrossRef]
- Silva-Perez, V.; Molero, G.; Serbin, S.P.; Condon, A.G.; Reynolds, M.P.; Furbank, R.T.; Evans, J.R. Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat. J. Exp. Bot. 2018, 69, 483–496. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lobos, G.A.; Escobar-Opazo, A.; Estrada, F.; Romero-Bravo, S.; Garriga, M.; del Pozo, A.; Poblete-Ecghevarría, C.; González-Talice, J.; González-Martinez, L.; Caligari, P. Spectral reflectance modeling by wavelength selection: Studying the scope for blueberry physiological breeding under contrasting water supply and heat conditions. Remote Sens. 2019, 11, 329. [Google Scholar] [CrossRef] [Green Version]
- Sayed, T.; Tavakolie, A.; Razavi, A. Comparison of adaptive network based fuzzy inference systems and Bspline neuro-fuzzy mode choice models. J. Comput. Civ. Eng. 2003, 17, 123–130. [Google Scholar] [CrossRef]
- Kisi, O. Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrol. Sci. J. 2005, 50, 683–696. [Google Scholar] [CrossRef]
- El-Hendawy, S.E.; Al-Suhaibani, N.; Elsayed, S.; Refay, Y.; Alotaibi, M.; Dewir, Y.H.; Hassan, W.M.; Schmidhalter, U. Combining biophysical parameters, spectral indices and multivariate hyperspectral models for estimating yield and water productivity of spring wheat across different agronomic practices. PLoS ONE 2019, 14, e0212294. [Google Scholar]
RGB Indices | Formula | References |
---|---|---|
Red pixel precentage (R %) | R/(R + G + B) | [64] |
Green pixel precentage (G %) | G/(R + G + B) | [64] |
Blue pixel precentage (B %) | B/(R + G + B) | [64] |
Green red ratio index (GRRI) | G/R | [65] |
Green-red vegetation index (GRVI) | (G − R)/(G + R) | [66] |
Normalized difference index (NDI) | (R − G)/(R + G + 0.01) | [67] |
Excess red vegetation index (ExR) | 1.4 × R − G | [68] |
Excess green vegetation index (ExG) | 2 × G − R − B | [68] |
Excess green minus Excess red index (ExGR) | ExG − ExR | [68] |
Vegetative index (VEG) | G/(Ra × B(1 − a)), a = 0.667 | [69] |
Principal component analysis index (IPCA) | 0.994 × (R − B) + 0.961 × (G − B) + 0.914 × (G − R) | [70] |
Combination (COM) | 0.25 × ExG + 0.3 × ExGR + 0.33 × CIVE + 0.12 × VEG | [71] |
Varieties | Irrigation Regimes | NRCT | R % | G % | B % | GRRI | GRVI | NDI | ExR | ExG | ExGR | VEG | IPCA | COM |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Arizona | 100% ETc | 0.259c | 0.187c | 0.449a | 0.364a | 2.418a | 0.413a | −0.406b | −0.187b | 0.347a | 0.534a | 1.930a | 49.718a | 6.678a |
75% ETc | 0.486b | 0.194ab | 0.436b | 0.370a | 2.260a | 0.385b | −0.379b | −0.165b | 0.309b | 0.474b | 1.819b | 46.043b | 6.637b | |
50% ETc | 0.682a | 0.207a | 0.413c | 0.380a | 1.970b | 0.325c | −0.320a | −0.118a | 0.240c | 0.359c | 1.621c | 38.525c | 6.562c | |
Bellini | 100% ETc | 0.329c | 0.185b | 0.448a | 0.367a | 2.431a | 0.415a | −0.409c | −0.189c | 0.343a | 0.531a | 1.931a | 49.855a | 6.676a |
75% ETc | 0.540b | 0.203a | 0.432b | 0.364a | 2.132b | 0.360b | −0.354b | −0.147b | 0.296b | 0.443b | 1.753b | 43.414b | 6.617b | |
50% ETc | 0.710a | 0.214a | 0.414c | 0.373b | 1.947c | 0.319c | −0.314a | −0.114a | 0.241c | 0.356c | 1.614c | 37.980c | 6.560c | |
ANOVA (F-test) | ||||||||||||||
Season (S) | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | |
Irrigation (I) | *** | *** | *** | *** | *** | *** | *** | *** | *** | *** | *** | *** | *** | |
Variety (V) | ** | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | |
I × S | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | |
V × S | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | |
V × I | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | |
V× I × S | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS | NS |
Measured Traits | Influential Indices | Best Fitted Equation | Model R2 |
---|---|---|---|
BFW | NRCT, COM | BFW = −95.77 − 17.14(NRCT) + 17.98 (COM) | 0.89 *** |
BDW | VEG | BDW = − 0.038 + 1.518 (VEG) | 0.47 * |
BWC | NRCT, G%, B%, R% | CWC = 20.53 − 0.151 (NRCT) − 19.13 (G%) − 20.23 (B%) − 19.68 (R%) | 0.92 *** |
TTY | NRCT | TTY = 54.67 − 37.89 (NRCT) | 0.84 *** |
Measured Traits Tttratraits Variables | Equation | R2 | RMSE |
---|---|---|---|
BFW | y = 1.05x − 0.040 | 0.84 *** | 1.594 |
BDW | y = 0.179x + 2.197 | 0.04 ns | 0.288 |
BWC | y = 0.881x + 0.094 | 0.89 *** | 0.013 |
TTY | y = 0.760x + 8.736 | 0.73 *** | 3.673 |
Parameters | Performance Criteria | ||||
---|---|---|---|---|---|
R2 | RMSE | MAD | E | ||
Training Series | BFW | 0.99 *** | 0.31 | 0.10 | 0.99 |
BDW | 0.99 *** | 0.07 | 0.03 | 0.99 | |
BWC | 1.00 *** | 0.00 | 0.00 | 1.00 | |
TTY | 1.00 *** | 0.66 | 0.25 | 1.00 | |
Testing Series | BFW | 0.88 *** | 2.14 | 1.97 | 0.63 |
BDW | 0.71 *** | 1.2 | 0.886 | −14.95 | |
BWC | 1.00 *** | 0.01 | 0.01 | 0.99 | |
TTY | 0.80 *** | 8.54 | 7.02 | 0.19 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 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
Elsayed, S.; El-Hendawy, S.; Khadr, M.; Elsherbiny, O.; Al-Suhaibani, N.; Alotaibi, M.; Tahir, M.U.; Darwish, W. Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes. Remote Sens. 2021, 13, 1679. https://doi.org/10.3390/rs13091679
Elsayed S, El-Hendawy S, Khadr M, Elsherbiny O, Al-Suhaibani N, Alotaibi M, Tahir MU, Darwish W. Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes. Remote Sensing. 2021; 13(9):1679. https://doi.org/10.3390/rs13091679
Chicago/Turabian StyleElsayed, Salah, Salah El-Hendawy, Mosaad Khadr, Osama Elsherbiny, Nasser Al-Suhaibani, Majed Alotaibi, Muhammad Usman Tahir, and Waleed Darwish. 2021. "Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes" Remote Sensing 13, no. 9: 1679. https://doi.org/10.3390/rs13091679