IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v8y2018i7p116-d159410.html
   My bibliography  Save this article

Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture

Author

Listed:
  • Alessandro Matese

    (Institute of Biometeorology (IBIMET), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy)

  • Salvatore Filippo Di Gennaro

    (Institute of Biometeorology (IBIMET), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy)

Abstract

High spatial ground resolution and highly flexible and timely control due to reduced planning time are the strengths of unmanned aerial vehicle (UAV) platforms for remote sensing applications. These characteristics make them ideal especially in the medium–small agricultural systems typical of many Italian viticulture areas of excellence. UAV can be equipped with a wide range of sensors useful for several applications. Numerous assessments have been made using several imaging sensors with different flight times. This paper describes the implementation of a multisensor UAV system capable of flying with three sensors simultaneously to perform different monitoring options. The intra-vineyard variability was assessed in terms of characterization of the state of vines vigor using a multispectral camera, leaf temperature with a thermal camera and an innovative approach of missing plants analysis with a high spatial resolution RGB camera. The normalized difference vegetation index (NDVI) values detected in different vigor blocks were compared with shoot weights, obtaining a good regression ( R 2 = 0.69). The crop water stress index (CWSI) map, produced after canopy pure pixel filtering, highlighted the homogeneous water stress areas. The performance index developed from RGB images shows that the method identified 80% of total missing plants. The applicability of a UAV platform to use RGB, multispectral and thermal sensors was tested for specific purposes in precision viticulture and was demonstrated to be a valuable tool for fast multipurpose monitoring in a vineyard.

Suggested Citation

  • Alessandro Matese & Salvatore Filippo Di Gennaro, 2018. "Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture," Agriculture, MDPI, vol. 8(7), pages 1-13, July.
    • Handle: RePEc:gam:jagris:v:8:y:2018:i:7:p:116-:d:159410
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/8/7/116/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/8/7/116/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gontia, N.K. & Tiwari, K.N., 2008. "Development of crop water stress index of wheat crop for scheduling irrigation using infrared thermometry," Agricultural Water Management, Elsevier, vol. 95(10), pages 1144-1152, October.
    2. Santesteban, L.G. & Di Gennaro, S.F. & Herrero-Langreo, A. & Miranda, C. & Royo, J.B. & Matese, A., 2017. "High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard," Agricultural Water Management, Elsevier, vol. 183(C), pages 49-59.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marco Ammoniaci & Simon-Paolo Kartsiotis & Rita Perria & Paolo Storchi, 2021. "State of the Art of Monitoring Technologies and Data Processing for Precision Viticulture," Agriculture, MDPI, vol. 11(3), pages 1-20, February.
    2. Bhoomin Tanut & Rattapoom Waranusast & Panomkhawn Riyamongkol, 2021. "High Accuracy Pre-Harvest Sugarcane Yield Forecasting Model Utilizing Drone Image Analysis, Data Mining, and Reverse Design Method," Agriculture, MDPI, vol. 11(7), pages 1-21, July.
    3. Yorghos Voutos & Phivos Mylonas & John Katheniotis & Anastasia Sofou, 2019. "A Survey on Intelligent Agricultural Information Handling Methodologies," Sustainability, MDPI, vol. 11(12), pages 1-23, June.
    4. Ivana Rendulić Jelušić & Branka Šakić Bobić & Zoran Grgić & Saša Žiković & Mirela Osrečak & Ivana Puhelek & Marina Anić & Marko Karoglan, 2022. "Grape Quality Zoning and Selective Harvesting in Small Vineyards—To Adopt or Not to Adopt," Agriculture, MDPI, vol. 12(6), pages 1-22, June.
    5. Ezenne, G.I. & Jupp, Louise & Mantel, S.K. & Tanner, J.L., 2019. "Current and potential capabilities of UAS for crop water productivity in precision agriculture," Agricultural Water Management, Elsevier, vol. 218(C), pages 158-164.
    6. Alessia Cogato & Franco Meggio & Massimiliano De Antoni Migliorati & Francesco Marinello, 2019. "Extreme Weather Events in Agriculture: A Systematic Review," Sustainability, MDPI, vol. 11(9), pages 1-18, May.
    7. Anzhen Qin & Dongfeng Ning & Zhandong Liu & Sen Li & Ben Zhao & Aiwang Duan, 2021. "Determining Threshold Values for a Crop Water Stress Index-Based Center Pivot Irrigation with Optimum Grain Yield," Agriculture, MDPI, vol. 11(10), pages 1-16, October.
    8. Joanna Paziewska & Antoni Rzonca, 2022. "Integration of Thermal and RGB Data Obtained by Means of a Drone for Interdisciplinary Inventory," Energies, MDPI, vol. 15(14), pages 1-18, July.
    9. Rigas Giovos & Dimitrios Tassopoulos & Dionissios Kalivas & Nestor Lougkos & Anastasia Priovolou, 2021. "Remote Sensing Vegetation Indices in Viticulture: A Critical Review," Agriculture, MDPI, vol. 11(5), pages 1-20, May.
    10. Mohammad Fatin Fatihur Rahman & Shurui Fan & Yan Zhang & Lei Chen, 2021. "A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture," Agriculture, MDPI, vol. 11(1), pages 1-26, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Liyuan & Zhang, Huihui & Zhu, Qingzhen & Niu, Yaxiao, 2023. "Further investigating the performance of crop water stress index for maize from baseline fluctuation, effects of environmental factors, and variation of critical value," Agricultural Water Management, Elsevier, vol. 285(C).
    2. Erdem, Yesim & Arin, Levent & Erdem, Tolga & Polat, Serdar & Deveci, Murat & Okursoy, Hakan & Gültas, Hüseyin T., 2010. "Crop water stress index for assessing irrigation scheduling of drip irrigated broccoli (Brassica oleracea L. var. italica)," Agricultural Water Management, Elsevier, vol. 98(1), pages 148-156, December.
    3. Puppala, Harish & Peddinti, Pranav R.T. & Tamvada, Jagannadha Pawan & Ahuja, Jaya & Kim, Byungmin, 2023. "Barriers to the adoption of new technologies in rural areas: The case of unmanned aerial vehicles for precision agriculture in India," Technology in Society, Elsevier, vol. 74(C).
    4. Ramírez-Cuesta, J.M. & Intrigliolo, D.S. & Lorite, I.J. & Moreno, M.A. & Vanella, D. & Ballesteros, R. & Hernández-López, D. & Buesa, I., 2023. "Determining grapevine water use under different sustainable agronomic practices using METRIC-UAV surface energy balance model," Agricultural Water Management, Elsevier, vol. 281(C).
    5. Chen, Jiazhou & Lin, Lirong & Lü, Guoan, 2010. "An index of soil drought intensity and degree: An application on corn and a comparison with CWSI," Agricultural Water Management, Elsevier, vol. 97(6), pages 865-871, June.
    6. Candogan, Burak Nazmi & Sincik, Mehmet & Buyukcangaz, Hakan & Demirtas, Cigdem & Goksoy, Abdurrahim Tanju & Yazgan, Senih, 2013. "Yield, quality and crop water stress index relationships for deficit-irrigated soybean [Glycine max (L.) Merr.] in sub-humid climatic conditions," Agricultural Water Management, Elsevier, vol. 118(C), pages 113-121.
    7. Zhang, Yu & Han, Wenting & Zhang, Huihui & Niu, Xiaotao & Shao, Guomin, 2023. "Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach," Agricultural Water Management, Elsevier, vol. 275(C).
    8. Zhang, Xiaoyu & Zhang, Xiying & Liu, Xiuwei & Shao, Liwei & Sun, Hongyong & Chen, Suying, 2015. "Incorporating root distribution factor to evaluate soil water status for winter wheat," Agricultural Water Management, Elsevier, vol. 153(C), pages 32-41.
    9. Pou, Alícia & Diago, Maria P. & Medrano, Hipólito & Baluja, Javier & Tardaguila, Javier, 2014. "Validation of thermal indices for water status identification in grapevine," Agricultural Water Management, Elsevier, vol. 134(C), pages 60-72.
    10. Al-Kayssi, A.W. & Shihab, R.M. & Mustafa, S.H., 2011. "Impact of soil water stress on Nigellone oil content of black cumin seeds grown in calcareous-gypsifereous soils," Agricultural Water Management, Elsevier, vol. 100(1), pages 46-57.
    11. Katimbo, Abia & Rudnick, Daran R. & DeJonge, Kendall C. & Lo, Tsz Him & Qiao, Xin & Franz, Trenton E. & Nakabuye, Hope Njuki & Duan, Jiaming, 2022. "Crop water stress index computation approaches and their sensitivity to soil water dynamics," Agricultural Water Management, Elsevier, vol. 266(C).
    12. Kumar, Navsal & Adeloye, Adebayo J. & Shankar, Vijay & Rustum, Rabee, 2020. "Neural computing modelling of the crop water stress index," Agricultural Water Management, Elsevier, vol. 239(C).
    13. Ezenne, G.I. & Jupp, Louise & Mantel, S.K. & Tanner, J.L., 2019. "Current and potential capabilities of UAS for crop water productivity in precision agriculture," Agricultural Water Management, Elsevier, vol. 218(C), pages 158-164.
    14. Anzhen Qin & Dongfeng Ning & Zhandong Liu & Sen Li & Ben Zhao & Aiwang Duan, 2021. "Determining Threshold Values for a Crop Water Stress Index-Based Center Pivot Irrigation with Optimum Grain Yield," Agriculture, MDPI, vol. 11(10), pages 1-16, October.
    15. de Almeida, Ailson Maciel & Coelho, Rubens Duarte & da Silva Barros, Timóteo Herculino & de Oliveira Costa, Jéfferson & Quiloango-Chimarro, Carlos Alberto & Moreno-Pizani, Maria Alejandra & Farias-Ram, 2022. "Water productivity and canopy thermal response of pearl millet subjected to different irrigation levels," Agricultural Water Management, Elsevier, vol. 272(C).
    16. O’Shaughnessy, Susan A. & Evett, Steven R. & Colaizzi, Paul D., 2015. "Dynamic prescription maps for site-specific variable rate irrigation of cotton," Agricultural Water Management, Elsevier, vol. 159(C), pages 123-138.
    17. Apolo-Apolo, O.E. & Martínez-Guanter, J. & Pérez-Ruiz, M. & Egea, G., 2020. "Design and assessment of new artificial reference surfaces for real time monitoring of crop water stress index in maize," Agricultural Water Management, Elsevier, vol. 240(C).
    18. Ramírez-Cuesta, J.M. & Ortuño, M.F. & Gonzalez-Dugo, V. & Zarco-Tejada, P.J. & Parra, M. & Rubio-Asensio, J.S. & Intrigliolo, D.S., 2022. "Assessment of peach trees water status and leaf gas exchange using on-the-ground versus airborne-based thermal imagery," Agricultural Water Management, Elsevier, vol. 267(C).
    19. Benjamin T. Fraser & Christine L. Bunyon & Sarah Reny & Isabelle Sophia Lopez & Russell G. Congalton, 2022. "Analysis of Unmanned Aerial System (UAS) Sensor Data for Natural Resource Applications: A Review," Geographies, MDPI, vol. 2(2), pages 1-38, June.
    20. Luan, Yajun & Xu, Junzeng & Lv, Yuping & Liu, Xiaoyin & Wang, Haiyu & Liu, Shimeng, 2021. "Improving the performance in crop water deficit diagnosis with canopy temperature spatial distribution information measured by thermal imaging," Agricultural Water Management, Elsevier, vol. 246(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:8:y:2018:i:7:p:116-:d:159410. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.