Industrial IoT Technologies and Applications. 4th EAI International Conference, Industrial IoT 2020, Virtual Event, December 11, 2020, Proceedings

Research Article

Correlation of NDVI with RGB Data to Evaluate the Effects of Solar Exposure on Different Combinations of Ornamental Grass Used in Lawns

Download
128 downloads
  • @INPROCEEDINGS{10.1007/978-3-030-71061-3_13,
        author={Jos\^{e} F. Mar\^{\i}n and Lorena Parra and Jaime Lloret and Salima Yousfi and Pedro V. Mauri},
        title={Correlation of NDVI with RGB Data to Evaluate the Effects of Solar Exposure on Different Combinations of Ornamental Grass Used in Lawns},
        proceedings={Industrial IoT Technologies and Applications. 4th EAI International Conference, Industrial IoT 2020, Virtual Event, December 11, 2020, Proceedings},
        proceedings_a={INDUSTRIALIOT},
        year={2021},
        month={7},
        keywords={GreenSeeker Matlab Camera Solar radiation Turf NDVI RGB},
        doi={10.1007/978-3-030-71061-3_13}
    }
    
  • José F. Marín
    Lorena Parra
    Jaime Lloret
    Salima Yousfi
    Pedro V. Mauri
    Year: 2021
    Correlation of NDVI with RGB Data to Evaluate the Effects of Solar Exposure on Different Combinations of Ornamental Grass Used in Lawns
    INDUSTRIALIOT
    Springer
    DOI: 10.1007/978-3-030-71061-3_13
José F. Marín1, Lorena Parra2, Jaime Lloret3, Salima Yousfi2, Pedro V. Mauri2
  • 1: Area verde MG Projects SL.
  • 2: Instituto Madrileño de Investigación y Desarrollo Rural, Agrario y Alimentario (IMIDRA)
  • 3: Universitat Politècnica de València

Abstract

In the urban areas, the use of water to irrigate the green areas must be improved by the use of technology to reach water efficiency. Normalized Difference Vegetation Index (NDVI) is the most important indexes to evaluate the vegetation vigour, but the required equipment for its gathering have a high cost. In this paper, we present the use of NDVI and pictures taken with a regular camera to evaluate the status of two groups of plots under different solar exposure. Besides, we study the possibilities to correlate data obtained from regular pictures with NDVI, offering a low-cost option for monitoring plant status. From the 18 evaluated plots, which include 3 different grass combinations, the mean value of NDVI and one picture is taken. Then, we obtain the red, green, and blue histograms of each picture using Matlab software. The histograms were included in Statgraphics to search for correlations between histograms and Normalized Difference Vegetation Index of each plot. The highest correlation was found with the data of red histogram (R = 0.58 and high significance level). Finally, the variance of both evaluated variables is analyzed, and we have determined that both variables are useful in determining the solar exposure of studied plots. Significance level was higher in NDVI than with data of the histogram, but both of them have a P-Value lower than 0.05 in the analysis of variance.