drone

The major objective is to develop indigenous prototype for drone based crop and soil health monitoring system using hyperspectral remote sensing (HRS) sensors. This technology could also be integrated with satellite-based technologies for large scale applications Drone technology based unmanned aerial vehicle (UAV) has ability for smooth scouting over farm fields, gathering precise information and transmitting the data on real time basis.
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Research objectives Use hyperspectral data from UAV platform for crop/weed discrimination Generation of spraying task /application maps for SSWM and validation The research will focus on two kind of weeds i.e. thistles in grassland and bindweeds in maize fields for case study. Field aerial hyperspectral images of thistles will be collected at the different growth stages and different flight height. The aerial images of bindweeds will also be taken in the early growth stage in the maize fields.
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Remote sensing using small unmanned airborne systems (sUAS) is a rapidly emerging technology. sUAS-based remote sensing offers possibilities for cost-efficient data capture with desired spatial and temporal resolutions, offering completely new business opportunities in various application fields. The sUAS markets are expected to grow explosively. Examples of key applications include surveying and mapping, precision agriculture and forestry, water quality monitoring, energy and power, infrastructure, law enforcement, public safety, and science and education.
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Precision Agriculture is becoming a hot topic in the research community as intrigued stakeholders investigate novel ways to improve farming practices with the introduction of sensing and communication technologies. Recently there has been particular interest in the use of unmanned aerial vehicles (drones) for observing large areas of farmland that would otherwise be done on foot or in a relatively large wheel-based vehicle.
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WASHINGTON, May 2015 – After a rigorous competition, the U.S.
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Summary This project builds on the current RIRDC project: PRJ007477, Rice NIR and Remote Sensing. It involves maintaining the NIR instrument at Yanco and updating the panicle initiation (PI) tissue, grain and straw nitrogen calibrations. The instrument and calibrations are used to determine the PI tissue nitrogen (N) content of the samples submitted to the NIR Tissue Test Service. The NIR, using numerous calibrations, is also used to analyse grain, straw and tissue samples in several other RIRDC research projects.
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PhD: Self-guided drones for tracking irrigation in a cotton field
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This project uses UAV technologies to look at urine patches on farm. Funded by Lincoln Agritech Ltd. Case Study: Linking nitrogen application to nitrogen requirements INDUSTRY: AGRICULTURE The Need: Smarter and more responsible application of nitrogenous fertilisers to NZ's pastoral farms - reducing environmental damage while maintaining or improving productivity.
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This project uses hyperspectral sensors to look at nutrients in the landscape. The ultimate goal is to be able to evaluate fertiliser requirements in hill country without having to do traditional soil and pasture testing. This project is under a "Primary Growth Partnership" (PGP) with Ravensdown and Ministry for Primary Industries (MPI) New Zealand. The Pioneering to Precision programme, led by Ravensdown, seeks to improve fertiliser practice on hill country farms through remote sensing of the nutrient status of the farms and precision application of fertiliser.
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The goal of this project is therefore to substantially advance the state-of-the-art in the direction of overcoming the above-mentioned issues by employing a team of Micro Aerial Vehicles (MAVs) as robotic platform, with a particular focus on quadrotor MAVs (a widespread and easily customizable mobile robotic platform with low cost, high agility and pervasiveness in 3D space).
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To fight the development of golden flavescence the DAMAV project aims to develop an automated testing solution vine diseases rollover parcels via a micro-drone. The objective is to provide a turnkey tool for winemakers to allow the search for potential outbreaks, and more generally, any type of disease detectable vine foliage. To enable this diagnosis, the partners propose to study the foliage with a drone and a multispectral high-resolution camera.
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The project is part of the strategic and economic context of precision farming. The objective is to maximize farmers revenue by minimizing the environmental impact of agricultural practices. One of the idea is to modulate practices in intra-plot level. The main technical obejctive is to develop a long-range drone imaging system and the image processing algorithms and associated agronomic models to supply the tools for decision system needed to implement precision agriculture crop Wheat, Maize, Sunflower and Rapesee.
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The focus of this project is to develop and enable an intelligent system that will apply precision management to whole farm grassland and grazing systems. The goal is to optimize grass quality, utilization efficiency, and ultimately profitability, with minimal labour requirement and maximum objectivity. To precisely allocate to the cow herd the absolutely correct area of grass, it is necessary to have an accurate ‘real-time’ measure of grass quality (as well as quantity).
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We are investigating the application of UAV in the landscape context in order to drive regulatory compliance and business performance.
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Abstract Commercial production of wheat crops in the UK is currently highly dependent on timely applications of fungicides to optimise yield and the development of improved varieties by plant breeders with resilience to diseases and abiotic stresses. The bottleneck is now in the ability to conduct field-based discovery and evaluation of traits (phenotyping) which are currently laborious, time consuming and inefficient.
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