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Remote Sensing Analysis of Agricultural Drone

Journal of the Indian Society of Remote Sensing volume 49, pages 689–701 (2021)Cite this article

Abstract

Farmers have more requirements for the completion of cultivations. Remote sensing is a big technology for reducing this requirement. Now, we need an organic spraying system at a low cost. We have two methods, first one neural network algorithm of quantum geographic information system (QGIS) and another one global positioning system (GPS) with drone. This paper describes the analysis of drone remote sensing using the normalized difference vegetation index (NDVI)/Near-infrared band (NIR) sensor in a multispectral view of agricultural land. NIR and NDVI images had water content values and precision values which is mixed in managing water resources. NDVI sensors are loaded to produce high-density images. Real-time monitoring coupled in NIR imaging geometrically and radiometrically adjusted to measure temperature. Multispectral and hyperspectral views had used for analyzing the tested data. Standard irrigation level is 60% to produce the plant growing. Irrigation techniques followed the treatment of the plant within continuous data per second. The implemented view focused only on growth controlling of plant in-depth irrigation between 30 and 90 cm in 60% deviation. NDVI, green normalized difference vegetation index (GNDVI), soil brightness index (SBI), green vegetation index (GVI), degree of yellow vegetation index (YVI), nitrogen sufficiency index (NSI), perpendicular vegetation index (PVI), transformed vegetation index (TVI), soil adjusted vegetation index (SAVI) and vegetation condition index (VCI) vegetation indices are used to the correlation of plant growth control with managing leaf strength and import python packages display the Vegetation various Real-time value in QGIS. Correlation of plant growth p ≤0.01, r = 0.77 and − 0.77 with conductance. It measured degree and demonstrated GPS view using irrigation techniques to control water stress. It had used to estimate the leaf conductance rate with the variation of atmospherically changing. It can calculate real-time leaf stress analysis. This report provided a drone survey analysis of compost percentage and vegetation indices of agricultural land.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, M.Kumarasamy College of Engineering, Thalavapalayam, Karur, 639113, Tamil Nadu, India

    S. Meivel

  2. Department of Electrical and Electronics Engineering, Kongu Engineering College, Thoppupalayam, Perundurai, 638060, Tamilnadu, India

    S. Maheswari

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  1. S. Meivel
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  2. S. Maheswari
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Meivel, S., Maheswari, S. Remote Sensing Analysis of Agricultural Drone. J Indian Soc Remote Sens 49, 689–701 (2021). https://doi.org/10.1007/s12524-020-01244-y

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  • DOI: https://doi.org/10.1007/s12524-020-01244-y

Keywords

  • NDVI sensor
  • Thermal sensor
  • Remote sensing
  • Vegetation indices
  • Leaf conductance and stress
  • Deep neural network theorem