Image analysis aplications in precision agriculture

Abstract

Unmanned Aircraft Vehicles (UAVs) are currently used for multiple applications in various fields: forestry, geology, the livestock sector and security. Among the most common applications, it is worth to stand out the image acquisition, irrigation, transport, surveillance and others. The study that one presents treats of the implementations that are realized by means of aerial images acquired with UAVs directed to the farming. Images acquired until recent years had been using satellites, however due to the high costs that are incurred and low accessibility to these technologies, UAVs, have become a tool for greater precision and scope for making decisions in agriculture. Information from databases of international magazines, groups and research centers is taken to determine the current state of implementations in Precision Agriculture (PA). This article describes tasks such as: soil preparation; limits and land areas, vegetation monitoring; classification of vegetation, growth, height, plant health; diseases management, pests and weeds, fertilization and inventory developed from analysis of aerial images acquired with UAVs.

Drones in Agriculture 4.0

“…Para las siguientes fases del proyecto se plantea probar qué tipo de algoritmos especializados se implementan a través de procesamiento de imágenes. Dentro de las opciones están Numpy y Scipy, que se basan en la manipulación básica de arreglos y los pasos para el reconocimiento y medición de variables, la adquisición de imágenes, el procesamiento, la segmentación, la extracción de características y la clasificación [35], [36], [37], [38].…”

Cherry Tree Crown Extraction from Natural Orchard Images with Complex Backgrounds

“…At present, field-based sensors are widely adopted for automatic tree identification, such as Visible-Near-Infrared imaging [10,11], stereo imaging [12,13] and thermal imaging [14,15]. Target extraction based on RGB (Red Green Blue) digital cameras has seen a wide application in precision farming due to its low cost and non-contact data collecting [16][17][18]. In this process, color index-based segmentation techniques are mostly applied to complete the crucial background removal.…”