Design and Method of an Agricultural Drone System Using Biomass Vegetation Indices and Multispectral Images

Abstract

Manual power is not sufficient to solve agricultural tasks. Heavy tasks are creating problems of soil contamination and seed contamination. It affected the plant after the locust and plant diseases spread. Drone mapping technology and the classification of DSM ortho mosaic image techniques provided the solutions to the problems. Vegetation indices helped with the identification of plant growth with the help of a drone. Drone mapping and surveys capture hyperspectral images. The images can be calculated using pix4Dmapper. The process is based on initial processing in stage 1, point clouding, meshes generation in stage 2, generation of the index, and DSM and ortho mosaic images in stage 3. We converted 1200 multispectral images and calculated vegetation index values. We measured plant height, plant temperature, the distance between plants, growth vegetation, the soil index of the agricultural land, the water index of the agricultural land, the disease index of the agricultural land, and the vegetation index of the agricultural land. This research proposed identifying the vegetation index on a single agricultural land using an NDVI multispectral image and a hyperspectral image (Geli et al. [1]). We utilized some vegetation indices using drone mapping. The research work started with multispectral image analysis. We collected over 1200 multispectral images in Tif format. It includes NIR band images, Red_edge band images, Green band images, Blue band images, and Red band images. All images are analyzed and tested for calculating vegetation indices of different agricultural land. We have extracted and classified remote sensing images of the agricultural land in a different direction [2]. In the future, we can find the vegetation value of agricultural land and plants using multispectral thermal images for deciding on water irrigation for agricultural places. Our outcome results are displayed as the following: plant growth areas, diseased plant areas, locust damaged plant areas, water detection areas, and soil quality index using the Vegetation index.