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Assessing the Reliability of Thermal and Optical Imaging Techniques for Detecting Crop Water Status under Different Nitrogen Levels

Author

Listed:
  • Daniele Masseroni

    (Department of Agricultural and Environmental Sciences (DiSAA), Università degli Studi di Milano, Via Celoria 2, 20133 Milan, Italy)

  • Bianca Ortuani

    (Department of Agricultural and Environmental Sciences (DiSAA), Università degli Studi di Milano, Via Celoria 2, 20133 Milan, Italy)

  • Martina Corti

    (Department of Agricultural and Environmental Sciences (DiSAA), Università degli Studi di Milano, Via Celoria 2, 20133 Milan, Italy)

  • Pietro Marino Gallina

    (Department of Agricultural and Environmental Sciences (DiSAA), Università degli Studi di Milano, Via Celoria 2, 20133 Milan, Italy)

  • Giacomo Cocetta

    (Department of Agricultural and Environmental Sciences (DiSAA), Università degli Studi di Milano, Via Celoria 2, 20133 Milan, Italy)

  • Antonio Ferrante

    (Department of Agricultural and Environmental Sciences (DiSAA), Università degli Studi di Milano, Via Celoria 2, 20133 Milan, Italy)

  • Arianna Facchi

    (Department of Agricultural and Environmental Sciences (DiSAA), Università degli Studi di Milano, Via Celoria 2, 20133 Milan, Italy)

Abstract

Efficient management of irrigation water is fundamental in agriculture to reduce the environmental impacts and to increase the sustainability of crop production. The availability of adequate tools and methodologies to easily identify the crop water status in operating conditions is therefore crucial. This work aimed to assess the reliability of indices derived from imaging techniques—thermal indices ( I g (stomatal conductance index) and CWSI (Crop Water Stress Index)) and optical indices ( NDVI (Normalized Difference Vegetation Index) and PRI (Photochemical Reflectance Index))—as operational tools to detect the crop water status, regardless the eventual presence of nitrogen stress. In particular, two separate experiments were carried out in a greenhouse, on two spinach varieties (Verdi F1 and SV2157VB), with different microclimatic conditions and under different levels of water and nitrogen application. Statistical analysis based on ANOVA test was carried out to assess the independence of thermal and optical indices from the crop nitrogen status. These imaging indices were successively compared through correlation analysis with reference destructive and non-destructive measurements of crop water status (stomatal conductance, chlorophyll a fluorescence, and leaf and soil water content), and linear regression models of thermal and optical indices versus reference measurements were calibrated. All models were significant (Fisher p -value lower than 0.05), and the highest R 2 values (greater than 0.6) were found for the regression models between CWSI and the soil water content, NDVI and the leaf water content, and PRI and the stomatal conductance. Further analysis showed that imaging indices acquired by thermal cameras (especially CWSI ) can be used as operational tools to detect the crop water status, since no dependence on plant nitrogen conditions was observed, even when the soil water depletion was very limited. Our results confirmed that imaging indices such as CWSI , NDVI and PRI can be used as operational tools to predict soil water status and to detect drought stress under different soil nitrogen conditions.

Suggested Citation

  • Daniele Masseroni & Bianca Ortuani & Martina Corti & Pietro Marino Gallina & Giacomo Cocetta & Antonio Ferrante & Arianna Facchi, 2017. "Assessing the Reliability of Thermal and Optical Imaging Techniques for Detecting Crop Water Status under Different Nitrogen Levels," Sustainability, MDPI, vol. 9(9), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:9:p:1548-:d:110271
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    References listed on IDEAS

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    1. Ors, Selda & Suarez, Donald L., 2017. "Spinach biomass yield and physiological response to interactive salinity and water stress," Agricultural Water Management, Elsevier, vol. 190(C), pages 31-41.
    2. 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.
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    1. Fan Ding & Changchun Li & Weiguang Zhai & Shuaipeng Fei & Qian Cheng & Zhen Chen, 2022. "Estimation of Nitrogen Content in Winter Wheat Based on Multi-Source Data Fusion and Machine Learning," Agriculture, MDPI, vol. 12(11), pages 1-16, October.
    2. Bhatti, Sandeep & Heeren, Derek M. & Evett, Steven R. & O’Shaughnessy, Susan A. & Rudnick, Daran R. & Franz, Trenton E. & Ge, Yufeng & Neale, Christopher M.U., 2022. "Crop response to thermal stress without yield loss in irrigated maize and soybean in Nebraska," Agricultural Water Management, Elsevier, vol. 274(C).
    3. Thomas M. Koutsos & Georgios C. Menexes & Andreas P. Mamolos, 2021. "The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    4. Urs Feller & Stanislav Kopriva & Valya Vassileva, 2018. "Plant Nutrient Dynamics in Stressful Environments: Needs Interfere with Burdens," Agriculture, MDPI, vol. 8(7), pages 1-6, July.
    5. García, Ana Belén Mira & Romero-Trigueros, Cristina & Gambín, José María Bayona & Sánchez Iglesias, Ma del Puerto & Tortosa, Pedro Antonio Nortes & Nicolás, Emilio Nicolás, 2023. "Estimation of stomatal conductance by infra-red thermometry in citrus trees cultivated under regulated deficit irrigation and reclaimed water," Agricultural Water Management, Elsevier, vol. 276(C).

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