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Precision Agriculture and Unmanned Aerial Vehicles (UAVs)

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Unmanned Aerial Vehicle: Applications in Agriculture and Environment

Abstract

Farming in developing countries is majorly dependent on the traditional knowledge of farmers, with unscientific agricultural practices commonly implemented, leading to low productivity and degradation of resources. Moreover, mechanization has not been integral to farming, and thus managing a farm is a time-consuming and labor-intensive process. Consequently, precision agriculture (PA) offers great opportunities for improvement. Using geographic information and communication technology (Geo-ICTs) principles, PA offers the opportunity for a farmer to apply the right amount of treatment at the right time and at the right location in the farm. However, in order to collect timely high-resolution data, drone-based sensing and image interpretation is required. These high-resolution images can give detailed information about the soil and crop condition, which can be used for farm management purposes. Leaf area index, normalized difference vegetation index, photochemical reflectance index, crop water stress index, and other such vegetation indices can provide important information on crop health. Temporal changes in these indices can give vital information about changes in health and canopy structure of the crop over time, which can be related to its biophysical and biochemical stress. These stresses may have occurred due to insufficient soil nutrient, inappropriate soil moisture, or pest attack. Through UAV-based PA, stressed areas can be identified in real time, and some corrective measures can also be carried out (e.g., fertilizer and pesticide spraying). Moreover, the advantages and different approaches to integrate the UAV data in the crop models are also described.

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References

  • Bellvert J et al (2014) Mapping crop water stress index in a ‘Pinot-noir’vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precis Agric 15(4):361–376

    Article  Google Scholar 

  • Berni JAJ et al (2009) Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans Geosci Remote Sens 47(3):722–738

    Article  Google Scholar 

  • Burud I, Lange G, Lillemo M, Bleken E, Grimstad L, From PJ (2017) Exploring robots and UAVs as phenotyping tools in plant breeding. IFAC-PapersOnLine 50(1):11479–11484

    Article  Google Scholar 

  • Calderón R et al (2013) 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 139:231–245

    Article  Google Scholar 

  • Chen P et al (2010) New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sens Environ 114(9):1987–1997

    Article  Google Scholar 

  • Comparetti A et al (2011) Precision agriculture: past, present and future. Conference: international scientific conference “Agricultural engineering and environment. (Accessed from Researchgate)

    Google Scholar 

  • Di Paola A, Valentini R, Santini M (2016) An overview of available crop growth and yield models for studies and assessments in agriculture. J Sci Food Agric 96:709–714

    Article  Google Scholar 

  • Eitel JUH et al (2006) Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. For Ecol Manag 229(1–3):170–182

    Article  Google Scholar 

  • Fukai S, Fischer KS (2012) Field phenotyping strategies and breeding for adaptation of rice to drought. Front Physiol 3:282

    Google Scholar 

  • Gago J et al (2015) UAVs challenge to assess water stress for sustainable agriculture. Agric Water Manag 153:9–19

    Article  Google Scholar 

  • Gitelson AA (2004) Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J Plant Physiol 161(2):165–173

    Article  Google Scholar 

  • Haboudane D et al (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens Environ 81(2–3):416–426

    Article  Google Scholar 

  • Hadria R, Duchemin BI, Lahrouni A, Khabba S, Er Raki S, Dedieu G, Chehbouni A, Olioso A (2006) Monitoring of irrigated wheat in a semi-arid climate using crop modelling and remote sensing data: impact of satellite revisit time frequency. Int J Remote Sens 27:1093–1117

    Article  Google Scholar 

  • Haghighattalab A, Pérez LG, Mondal S, Singh D, Schinstock D, Rutkoski J, Ortiz-Monasterio I, Singh RP, Goodin D, Poland J (2016) Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods 12(1):35

    Article  Google Scholar 

  • Hardisky MA, Klemas V, Smart M (1983) The influence of soil salinity, growth form, and leaf moisture on spectral radiance of spartina alterniflora canopies. Photogramm Eng Remote Sens 16(9):1581–1598

    Google Scholar 

  • Holman F, Riche A, Michalski A, Castle M, Wooster M, Hawkesford M (2016) High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sens 8(12):1031

    Article  Google Scholar 

  • Huang X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z, Li M, Fan D (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42(11):961

    Article  Google Scholar 

  • Hunt Jr, Raymond E, Rock BN (1989) Detection of changes in leaf water content using near-and middle-infrared reflectances. Remote Sens Environ 30(1):43–54

    Article  Google Scholar 

  • Hunt ER et al (2005) Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precis Agric 6(4):359–378

    Article  Google Scholar 

  • Jackson RD et al (1980) Hand-held radiometry: a set of notes developed for use at the workshop of Hand-held radiometry. USDA, Oakland

    Google Scholar 

  • Jiang Z et al (2008) Development of a two-band enhanced vegetation index without a blue band. Remote Sens Environ 112(10):3833–3845

    Article  Google Scholar 

  • Jin X, Kumar L, Li Z, Feng H, Xu X, Yang G, Wang J (2018) A review of data assimilation of remote sensing and crop models. Eur J Agron 92:141–152

    Article  Google Scholar 

  • Jonckheere I et al (2004) Methods for leaf area index determination. Part I: theories, techniques and instruments. Agric For Meteorol 121:19–35

    Article  Google Scholar 

  • Jones JW, Antle JM, Basso B, Boote KJ, Conant RT, Foster I, Godfray HCJ, Herrero M, Howitt RE, Janssen S et al (2017) Brief history of agricultural systems modeling. Agric Syst 155:240–254

    Article  Google Scholar 

  • Kim MS et al (2002) Assessment of environmental plant stresses using multispectral steady-state fluorescence imagery, Air Pollution and Plant Biotechnology. Springer, Tokyo, pp 321–341

    Google Scholar 

  • LandInfo: “Buying Satellite Imagery: GeoEye, WorldView 1, 2, 3, QuickBird, IKONOS, Pléiades.” [Online]. Available: http://www.landinfo.com/satellite-imagery-pricing.html. Assessed on 10 Jan 2018

  • Liaghat S, Balasundram SK (2010) A review: the role of remote sensing in precision agriculture. Am J Agric Biol Sci 5(1):50–55

    Article  Google Scholar 

  • Matese A et al (2015) Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sens 7(3):2971–2990

    Article  Google Scholar 

  • Nguy-Robertson A et al (2012) Green leaf area index estimation in maize and soybean: combining vegetation indices to achieve maximal sensitivity. Agron J 104(5):1336–1347

    Article  Google Scholar 

  • Primicerio J et al (2012) A flexible unmanned aerial vehicle for precision agriculture. Precis Agric 13(4):517–523

    Article  Google Scholar 

  • RAMI: Radiative Transfer Model Intercomparison (RAMI). http://rami-benchmark.jrc.ec.europa.eu/HTML/RAMI3/MODELS/4SAIL2/4SAIL2.php. Accessed on 06 Jan 2018

  • Rondeaux G, Steven M, Baret F (1996) Optimization of soil-adjusted vegetation indices. Remote Sens Environ 55(2):95–107

    Article  Google Scholar 

  • Roujean J-L, Breon F-M (1995) Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens Environ 51(3):375–384

    Article  Google Scholar 

  • Satimagingcorp World view-4 Satellite imagery and satellite sensor specifications | satellite imaging corp. [Online]. Available: http://www.satimagingcorp.com/satellite-sensors/geoeye-2/. Assessed on 10 Jan 2018

  • Schmale III, David G, Dingus BR, Reinholtz C (2008) Development and application of an autonomous unmanned aerial vehicle for precise aerobiological sampling above agricultural fields. J Field Robot 25(3):133–147

    Article  Google Scholar 

  • Schneider K (2003) Assimilating remote sensing data into a land-surface process model. Int J Remote Sens 24:2959–2980

    Article  Google Scholar 

  • Stimson HC et al (2005) Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Juniperus monosperma. Remote Sens Environ 96(1):108–118

    Article  Google Scholar 

  • Tokekar P et al (2016) Sensor planning for a symbiotic UAV and UGV system for precision agriculture. IEEE Trans Robot 32(6):1498–1511

    Article  Google Scholar 

  • Wallace C, Walker J, Skirvin S, Patrick-Birdwell C, Weltzin J, Raichle H (2016) Mapping presence and predicting phenological status of invasive buffelgrass in southern Arizona using MODIS, climate and citizen science observation data. Remote Sens 8(7):524

    Article  Google Scholar 

  • Wallach D, Makowski D, Jones JW, Brun F, Jones JW (2014) Working with dynamic crop models. Academic, Cambridge, MA, pp 407–436

    Book  Google Scholar 

  • Watanabe K, Guo W, Arai K, Takanashi H, Kajiya-Kanegae H, Kobayashi M, Yano K, Tokunaga T, Fujiwara T, Tsutsumi N, Iwata H (2017) High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling. Front Plant Sci 8:421

    Article  Google Scholar 

  • Zarco-Tejada PJ et al (2001) 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 39(7):1491–1507

    Article  Google Scholar 

  • Zarco-Tejada PJ, González-Dugo V, Berni JAJ (2012) Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens Environ 117:322–337

    Article  Google Scholar 

  • Zarco-Tejada PJ et al (2013) Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agric For Meteorol 171:281–294

    Article  Google Scholar 

  • Zhang C, Kovacs JM (2012) The application of small unmanned aerial systems for precision agriculture: a review. Precis Agric 13(6):693–712

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Indian Institute of Technology Bombay – Monash Research Academy, Mumbai, Maharashtra, India

    Rahul Raj

  2. Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Soumyashree Kar, Rohit Nandan & Adinarayana Jagarlapudi

Authors
  1. Rahul Raj
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  2. Soumyashree Kar
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  3. Rohit Nandan
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  4. Adinarayana Jagarlapudi
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Corresponding author

Correspondence to Adinarayana Jagarlapudi .

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Editors and Affiliations

  1. Faculty of Environmental Earth Science, Hokkaido University, Sapporo, Hokkaido, Japan

    Ram Avtar

  2. Faculty of Environmental Earth Science, Hokkaido University, Sapporo, Hokkaido, Japan

    Teiji Watanabe

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Raj, R., Kar, S., Nandan, R., Jagarlapudi, A. (2020). Precision Agriculture and Unmanned Aerial Vehicles (UAVs). In: Avtar, R., Watanabe, T. (eds) Unmanned Aerial Vehicle: Applications in Agriculture and Environment. Springer, Cham. https://doi.org/10.1007/978-3-030-27157-2_2

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