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Assessment of optimal flying height and timing using high-resolution unmanned aerial vehicle images in precision agriculture

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Abstract

This paper shows some practical experiences of using unmanned aerial vehicles-based platform for remote sensing in supporting precision agriculture mapping. There have been studies on unmanned aerial vehicles used to calculate plant water stress; however, the scientific reports of drone images that are used to predict best time and height are rare. The trial was conducted during 2020, in a five-year-old Anji tea plant experimental field, where drone captures images in a different time series of 27 flights during experimental days. This work aims to (1) investigate the appropriate thermography timing and altitude based on unmanned aerial vehicles remote sensing, (2) conduct a quantitative and qualitative study of various thermal orthomosaics and photographs, (3) establish workflow for high-resolution remote sensing application. All flights were operated at 3 m/s flying speed. Flights were performed during the testing day at about 09:00 h, 11:00 h, and 13:00 h. The drone images were taken at relative flying heights of 25 m, 40 m, and 60 m each day. The relationship between canopy temperature and plant-based variables was also established. The results reported that flights at 11:00 h and 60-m altitude orthomosaic could provide the best relation and accurate canopy temperature. On the other hand, the high relationship between stomatal conductance and canopy temperature was R2 0.98 at 11:00 h. The selection of optimal timing and altitude can provide rapid and reliable canopy temperature information. Overall, high resolution with low-altitude unmanned aerial vehicles images proved good relationship in order to assess the canopy temperature.

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References

  • Aasen H, Honkavaara E, Lucieer A, Zarco-Tejada PJ (2018) Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: a review of sensor technology, measurement procedures, and data correction workflows. Remote Sens 10(7):1091

    Google Scholar 

  • Agam N et al (2013) An insight to the performance of crop water stress index for olive trees. Agric Water Manag 118:79–86

    Google Scholar 

  • Ai M, Hu Q, Li J, Wang M, Yuan H, Wang S (2015) A robust photogrammetric processing method of low-altitude UAV images. Remote Sens 7:2302–2333

    Google Scholar 

  • Awais M, Li W, Arshad A, Haydar Z, Yaqoob N, Hussain S (2018) Evaluating removal of tar contents in syngas produced from downdraft biomass gasification system. Int J Green Energy 15:724–731

    CAS  Google Scholar 

  • Awais M, Li W, Munir A et al (2020) Experimental investigation of downdraft biomass gasifier fed by sugarcane bagasse and coconut shells. Biomass Conv Bioref. https://doi.org/10.1007/s13399-020-00690-5

    Article  Google Scholar 

  • Baluja J, Diago MP, Balda P, Zorer R, Meggio F, Morales F, Tardaguila J (2012) Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrig Sci 30:511–522

    Google Scholar 

  • Bansod B, Singh R, Thakur R, Singhal G (2017) A comparision between satellite based and drone based remote sensing technology to achieve sustainable development: a review. J Agric Environ Int Dev (JAEID) 111:383–407

    Google Scholar 

  • Bellvert J, Marsal J, Girona J, Gonzalez-Dugo V, Fereres E, Ustin SL, Zarco-Tejada PJ (2016) Airborne thermal imagery to detect the seasonal evolution of crop water status in peach, nectarine and saturn peach orchards. Remote Sens 8:39

    Google Scholar 

  • Bellvert J, Zarco-Tejada PJ, Marsal J, Girona J, González-Dugo V, Fereres E (2016) Vineyard irrigation scheduling based on airborne thermal imagery and water potential thresholds. Aust J Grape Wine Res 22:307–315

    Google Scholar 

  • Berger B, Parent B, Tester M (2010) High-throughput shoot imaging to study drought responses. J Exp Bot 61:3519–3528

    CAS  Google Scholar 

  • Berni J, Zarco-Tejada P, Sepulcre-Cantó G, Fereres E, Villalobos F (2009) Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sens Environ 113:2380–2388

    Google Scholar 

  • Berni JA, Zarco-Tejada PJ, Suárez L, Fereres E (2009) Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans geosci Remote Sens 47:722–738

    Google Scholar 

  • Blonquist J Jr, Norman JM, Bugbee B (2009) Automated measurement of canopy stomatal conductance based on infrared temperature. Agric for Meteorol 149:1931–1945

    Google Scholar 

  • Calderón R, Navas-Cortés J, Lucena C, Zarco-Tejada P (2013) High-resolution hyperspectral and thermal imagery acquired from UAV platforms for early detection of Verticillium wilt using fluorescence, temperature and narrow-band indices. In: Proceedings of the workshop on UAV-based remote sensing methods for monitoring Vegetation, Cologne, Germany, pp 9–10

  • Chen Q, Wachenheim C, Zheng S (2020) Land scale, cooperative membership and benefits information: unmanned aerial vehicle adoption in China. Sustain Futures 2:100025

    Google Scholar 

  • Dandois JP, Ellis EC (2010) Remote sensing of vegetation structure using computer vision. Remote sens 2:1157–1176

    Google Scholar 

  • Díaz-Varela RA, De la Rosa R, León L, Zarco-Tejada PJ (2015) High-resolution airborne UAV imagery to assess olive tree crown parameters using 3D photo reconstruction: application in breeding trials. Remote Sens 7:4213–4232

    Google Scholar 

  • García-Tejero I, Costa J, Egipto R, Durán-Zuazo V, Lima R, Lopes C, Chaves M (2016) Thermal data to monitor crop-water status in irrigated mediterranean viticulture. Agric Water Manag 176:80–90

    Google Scholar 

  • García-Tejero I, Rubio A, Viñuela I, Hernández A, Gutiérrez-Gordillo S, Rodríguez-Pleguezuelo C, Durán-Zuazo V (2018) Thermal imaging at plant level to assess the crop-water status in almond trees (cv. Guara) under deficit irrigation strategies. Agric Water Manag 208:176–186

    Google Scholar 

  • García-Tejero IF, Ortega-Arévalo CJ, Iglesias-Contreras M, Moreno JM, Souza L, Tavira SC, Durán-Zuazo VH (2018) Assessing the crop-water status in almond (Prunus dulcis mill.) trees via thermal imaging camera connected to smartphone. Sensors 18:1050

    Google Scholar 

  • Gates DM (1964) Leaf temperature and transpiration 1. Agron J 56:273–277

    Google Scholar 

  • Gebbers R, Adamchuk VI (2010) Precision agriculture and food security. Science 327:828–831

    CAS  Google Scholar 

  • Gevaert CM, Suomalainen J, Tang J, Kooistra L (2015) Generation of spectral–temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications. IEEE J Sel Top Appl Earth Obs Remote Sens 8:3140–3146

    Google Scholar 

  • Gomes-Laranjo J, Coutinho J, Galhano V, Cordeiro V (2006) Responses of five almond cultivars to irrigation: Photosynthesis and leaf water potential. Agric Water Manag 83:261–265

    Google Scholar 

  • Gómez-Candón D, Virlet N, Labbé S, Jolivot A, Regnard J-L (2016) Field phenotyping of water stress at tree scale by UAV-sensed imagery: new insights for thermal acquisition and calibration. Precis Agric 17:786–800

    Google Scholar 

  • Gonzalez-Dugo V, Zarco-Tejada PJ, Fereres E (2014) Applicability and limitations of using the crop water stress index as an indicator of water deficits in citrus orchards. Agric for meteorol 198:94–104

    Google Scholar 

  • Hunter MC, Smith RG, Schipanski ME, Atwood LW, Mortensen DA (2017) Agriculture in 2050: recalibrating targets for sustainable intensification. Bioscience 67:386–391

    Google Scholar 

  • Hussain S et al (2020) Robust TiN nanoparticles polysulfide anchor for Li–S storage and diffusion pathways using first principle calculations. Chem Eng J 391:123595

    CAS  Google Scholar 

  • Idso S, Jackson R, Pinter P Jr, Reginato R, Hatfield J (1981) Normalizing the stress-degree-day parameter for environmental variability. Agric meteorol 24:45–55

    Google Scholar 

  • Iglesias A, Garrote L (2018) Local and collective actions for adaptation to use less water for agriculture in the mediterranean region. Water scarcity and sustainable agriculture in semiarid environment. Elsevier, Amsterdam, pp 73–84

    Google Scholar 

  • Jackson RD (1982) Canopy temperature and crop water stress. Advances in irrigation, vol 1. Elsevier, Amsterdam, pp 43–85

    Google Scholar 

  • Jackson RD, Idso S, Reginato R, Pinter P Jr (1981) Canopy temperature as a crop water stress indicator. Water Resour Res 17:1133–1138

    Google Scholar 

  • Jones H (1999) Use of thermography for quantitative studies of spatial and temporal variation of stomatal conductance over leaf surfaces plant. Cell Environ 22:1043–1055

    Google Scholar 

  • Jones HG (1999) Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling. Agric meteorol 95:139–149

    Google Scholar 

  • Jones HG, Hutchinson PA, May T, Jamali H, Deery DM (2018) A practical method using a network of fixed infrared sensors for estimating crop canopy conductance and evaporation rate. Biosyst Eng 165:59–69

    Google Scholar 

  • Jones HG, Stoll M, Santos T, Sousa CD, Chaves MM, Grant OM (2002) Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine. J Exp Bot 53:2249–2260

    CAS  Google Scholar 

  • Kayad A, Sozzi M, Gatto S, Marinello F, Pirotti F (2019) Monitoring within-field variability of corn yield using Sentinel-2 and machine learning techniques. Remote Sens 11:2873

    Google Scholar 

  • Kelly J et al (2019) Challenges and best practices for deriving temperature data from an uncalibrated UAV thermal infrared camera. Remote Sens 11:567

    Google Scholar 

  • Lee W, Searcy S (2000) Multispectral sensor for detecting nitrogen in corn plants. ASAE annual international meeting. Midwest express center, Milwaukee, Wisconsin, pp 9–12

    Google Scholar 

  • Li L, Zhang Q, Huang D (2014) A review of imaging techniques for plant phenotyping. Sensors 14:20078–20111

    Google Scholar 

  • Li W, Awais M, Ru W, Shi W, Ajmal M, Uddin S, Liu C (2020) Review of sensor network-based irrigation systems using iot and remote sensing. Adv Meteorol 2020:1–14

    Google Scholar 

  • Majidi B, Bab-Hadiashar, (2005) A Real time aerial natural image interpretation for autonomous ranger drone navigation. Digital Image Comput Tech Appl 20(8):65–65

    Google Scholar 

  • Mangus DL, Sharda A, Zhang N (2016) Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse. Comput Electron Agric 121:149–159

    Google Scholar 

  • Maritim T, Kamunya S, Mireji P, Mwendia C, Muoki R, Cheruiyot E, Wachira FN (2015) Physiological and biochemical response of tea [Camellia sinensis (L.) O. Kuntze] to water-deficit stress. J Hortic Sci Biotechnol 90:395–400

    CAS  Google Scholar 

  • Matese A et al (2018) Estimation of water stress in grapevines using proximal and remote sensing methods. Remote Sens 10:114

    Google Scholar 

  • Mesas-Carrascosa F-J et al (2018) Drift correction of lightweight microbolometer thermal sensors on-board unmanned aerial vehicles. Remote Sens 10:615

    Google Scholar 

  • Möller M et al (2007) Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. J Exp Bot 58:827–838

    Google Scholar 

  • Mulla D, Khosla R (2016) Historical evolution and recent advances in precision farming. Soil-Specif Farm Precis Agric 9(9):1–35

    Google Scholar 

  • Ortega-Farías S et al (2015) Estimation of olive evapotranspiration using multispectral and thermal sensors placed aboard an unmanned aerial vehicle. VIII Int Symp Irrig Hortic Crop 1150:1–8

    Google Scholar 

  • Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man cybern 9:62–66

    Google Scholar 

  • Park S, Ryu D, Fuentes S, Chung H, Hernández-Montes E, O’Connell M (2017) Adaptive estimation of crop water stress in nectarine and peach orchards using high-resolution imagery from an unmanned aerial vehicle (UAV). Remote Sens 9:828

    Google Scholar 

  • Poblete-Echeverría C, Sepulveda-Reyes D, Ortega-Farias S, Zuñiga M, Fuentes S (2014) Plant water stress detection based on aerial and terrestrial infrared thermography: a study case from vineyard and olive orchard. XXIX Int Hortic Congr Hortic Sustain Lives Livelihoods Landsc 1112:141–146

    Google Scholar 

  • Pou A, Diago MP, Medrano H, Baluja J, Tardaguila J (2014) Validation of thermal indices for water status identification in grapevine. Agric Water Manag 134:60–72

    Google Scholar 

  • Remorini D, Massai R (2003) Comparison of water status indicators for young peach trees. Irrig Sci 22:39–46

    Google Scholar 

  • Reza MN, Na IS, Baek SW, Lee K-H (2019) Rice yield estimation based on K-means clustering with graph-cut segmentation using low-altitude UAV images. Biosys Eng 177:109–121

    Google Scholar 

  • Ribeiro-Gomes K, Hernández-López D, Ortega JF, Ballesteros R, Poblete T, Moreno MA (2017) Uncooled thermal camera calibration and optimization of the photogrammetry process for UAV applications in agriculture. Sensors 17:2173

    Google Scholar 

  • Romero P, Botia P, Garcia F (2004) Effects of regulated deficit irrigation under subsurface drip irrigation conditions on vegetative development and yield of mature almond trees. Plant Soil 260:169–181

    CAS  Google Scholar 

  • Rud R et al (2014) Crop water stress index derived from multi-year ground and aerial thermal images as an indicator of potato water status. Precis Agric 15:273–289

    Google Scholar 

  • Sagan V et al (2019) UAV-based high resolution thermal imaging for vegetation monitoring, and plant phenotyping using ICI 8640 P, FLIR Vue Pro R 640, and thermomap cameras. Remote Sens 11:330

    Google Scholar 

  • Santesteban L, Di Gennaro S, Herrero-Langreo A, Miranda C, Royo J, Matese A (2017) High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agric Water Manag 183:49–59

    Google Scholar 

  • Sedwick R, Schweighart S (2001) Development and analysis of a high fidelity linearized J (2) model for satellite formation flying. In:AIAA space 2001 Conference and exposition. 4744

  • Sheng H, Chao H, Coopmans C, Han J, McKee M, Chen Y (2010) Low-cost UAV-based thermal infrared remote sensing: Platform, calibration and applications. In: Proceedings of 2010. IEEE/ASME International conference on mechatronic and embedded systems and applications, IEEE, pp 38–43

  • Sona G, Pinto L, Pagliari D, Passoni D, Gini R (2014) Experimental analysis of different software packages for orientation and digital surface modelling from UAV images. Earth Sci Inf 7:97–107

    Google Scholar 

  • Stagakis S, González-Dugo V, Cid P, Guillén-Climent ML, Zarco-Tejada PJ (2012) Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices. ISPRS J Photogramm Remote Sens 71:47–61

    Google Scholar 

  • Su J, Liu C, Hu X, Xu X, Guo L, Chen W-H (2019) Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery. Comput Electron Agric 167:105035

    Google Scholar 

  • Sugiura R, Noguchi N, Ishii K (2005) Remote-sensing technology for vegetation monitoring using an unmanned helicopter. Biosyst Eng 90:369–379

    Google Scholar 

  • Tran QH, Han D, Kang C, Haldar A, Huh J (2017) Effects of ambient temperature and relative humidity on subsurface defect detection in concrete structures by active thermal imaging. Sensors 17:1718

    Google Scholar 

  • Tucker C (1979) Monitoring the grasslands of the sahel 1984–1985. Remote Sens Environ 8:127–150

    Google Scholar 

  • Vecchio Y, Agnusdei GP, Miglietta PP, Capitanio F (2020) Adoption of precision farming tools: the case of Italian farmers. Int J Environ Res Publ Health 17:869

    Google Scholar 

  • Waldemar M, Klecha D (2015) Modeling of atmospheric transmission coefficient in infrared for thermovision measurements. In: Proceedings of the Sensor.

  • Weiss M, Jacob F, Duveiller G (2020) Remote sensing for agricultural applications: a meta-review. Remote Sens Environ 236:111402

    Google Scholar 

  • Zarco-Tejada PJ et al (2005) Assessing vineyard condition with hyperspectral indices: leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens Environ 99:271–287

    Google Scholar 

  • Zhang L, Niu Y, Zhang H, Han W, Li G, Tang J, Peng X (2019) Maize canopy temperature extracted from UAV thermal and RGB imagery and its application in water stress monitoring. Front Plant Sci 10:1270

    Google Scholar 

  • Zhao T, Doll D, Wang D, Chen Y (2017) A new framework for UAV-based remote sensing data processing and its application in almond water stress quantification. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), 2017a. IEEE, pp 1794–1799

  • Zhao T, Stark B, Chen Y, Ray AL, Doll D (2017) Challenges in water stress quantification using small unmanned aerial system (suas): Lessons from a growing season of almond. J Intell Robot Syst 88:721–735

    Google Scholar 

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Acknowledgements

We acknowledge support from “Belt and Road" Innovation Cooperation Project of Jiangsu Province (No.BZ2020068), Independent Innovation Fund Project of Agricultural Science and Technology in Jiangsu Province (No.CX (20)2037), and Synergistic Innovation Center of Jiangsu Modern Agricultural Equipment and Technology (No.4091600014).

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Awais, M., Li, W., Cheema, M.J.M. et al. Assessment of optimal flying height and timing using high-resolution unmanned aerial vehicle images in precision agriculture. Int. J. Environ. Sci. Technol. 19, 2703–2720 (2022). https://doi.org/10.1007/s13762-021-03195-4

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