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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.

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