Skip to main content

PhD fellowship in UAV based remote sensing for agriculture research Agriculture Victoria






PhD fellowship in UAV based remote sensing for agriculture research Agriculture Victoria
Are you enthusiastic about remote sensing in high-throughput phenotyping approaches for crop breeding research? If you are interested in employing UAVs, multispectral and LiDAR in extracting phenotyping parameters and understanding the underlying mechanism that governs crop yield, this PhD fellowship can be for you.
Understanding the interaction of genotype with the environment is of prime importance, which can be achieved by the measurement of phenotypic traits of the crop. The field of phenomics is a large-scale collection of data-set to study, analyze and understand the interaction of genomic variations with the varying environment by revealing the relation between genotype and phenotypes. Traditionally, plant phenotyping has been achieved by manually collecting the data from the plants to select the best performing genotype. Technological advancement in the plant phenotyping has been a topic of interest among interdisciplinary researchers in recent years. The efforts have been put into using and optimizing the available technologies to adapt to the need for plant phenotyping. From the perspective of non-invasive measurement of phenotypic traits, the state-of-the-art remote sensing technology of UAV based multispectral, photogrammetric and LiDAR is promising.
Project Outline and Tasks
Your primary responsibilities will be to:
· Optimise multispectral, photogrammetric and LiDAR sensor systems integrated on UAV platforms for high-throughput field phenotyping in grain crops, considering factors such as data sampling rate, field-of-view, sensitivity and modalities required for sensor operation.
Identify best practises in data processing and analysis for plant phenotyping, including generation of point cloud metrices, vegetation and morphological indices, segmentation, voxelization, classification, and 3D reconstruction of crops.
Participate in scientific conferences and workshops as well as events in the area of plant phenotyping and remote sensing.
Publish your findings in scientific journals.
Qualifications
Candidates are required to have:
· A Masters or Bachelors in agricultural science, remote sensing, computer science, electronics, or similar.
Experience with analysis of large data sets and scientific programming.
Desirable though not necessary,knowledge in computer vision, machine learning, data processing, spatial data analysis and GIS software will be valued.
Clear and concise communication skills in English.
A positive attitude, a strong drive and eagerness to learn.
Who is eligible?
Citizen form all nations are eligible and encouraged to apply. Australian citizens or Permanent Residents will be preferred considering the current situation with COVID-19 travel restrictions to timely start the PhD.
Assessment
The assessment of the applicants will be made by Dr. Surya Kant, Senior Research Scientist, Agriculture Victoria, Department of Jobs, Precincts and Regions | Principal Fellow Honorary, The University of Melbourne.
We offer
Established in 1853, the University of Melbourne is a public-spirited institution that makes distinctive contributions to society in research, learning and teaching and engagement. It's consistently ranked among the leading universities in the world, with international rankings of world universities placing it as number 1 in Australia and number 32 in the world (Times Higher Education World University Rankings 2017-2018).
Agriculture Victoria is a government enterprise that works with the agriculture industry on research, development and extension to improve production, connect the sector with international markets, support development and maintain effective biosecurity controls.
The successful candidate will receive:
A $33,000 p.a. (tax-free) scholarship for up to three and a half years
Professional development programs
Access to state-of-the-art technologies
The PhD fellowships will be based at Agriculture Victoria, Grains Innovation Park, Horsham, Victoria, Australia
Further information
Further information may be obtained from Dr. Surya Kant, Email: surya.kant[a]agriculture.vic.gov.au.
Application
Please submit your application to Dr. Surya Kant by Email: surya.kant[a]agriculture.vic.gov.au and Dr. Bikram Banerjee bikram.banerjee[a]agriculture.vic.gov.au
The application must include:
A letter motivating the application (cover letter)
Curriculum vitae
Grade transcripts for Bachelors or Masters degree
Thesis copy
Job Type: Full-time
Salary: $33,000.00 /year
Work Eligibility:
The candidate can work permanently with no restriction on hours (Preferred)
Work Remotely:
Temporarily due to COVID-19


....

Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://g.page/vineeshvc
🌏🌎
🌐🌍

Comments

Popular posts from this blog

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...

Isodata clustering

Iso Cluster Classification in Unsupervised Image Classification Iso Cluster Classification is a common unsupervised classification technique used in remote sensing. The "Iso Cluster" algorithm groups pixels with similar spectral characteristics into clusters, or spectral classes, based solely on the data's statistical properties. Unlike supervised classification, Iso Cluster classification doesn't require the analyst to predefine classes or training areas; instead, the algorithm analyzes the image data to find natural groupings of pixels. The analyst interprets these groups afterward to label them with meaningful information classes (e.g., water, forest, urban). How Iso Cluster Classification Works The Iso Cluster algorithm follows several steps to group pixels: Initial Data Analysis : The algorithm examines the entire dataset to understand the spectral distribution of the pixels across the spectral bands. Clustering Process :    - The algorithm starts by divid...

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature  A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...

Applications of Remote Sensing

Remote sensing is the science of obtaining information about the Earth's surface without direct contact, using sensors mounted on satellites, aircraft, or drones. It is widely applied in environmental monitoring, agriculture, urban studies, and disaster management due to its synoptic coverage, temporal continuity, and objective data acquisition.  Vegetation, Soil, and Water Studies 1.1 Vegetation Studies Vegetation analysis using remote sensing is based on the spectral reflectance behavior of plants . Healthy vegetation absorbs most of the red light for photosynthesis and reflects strongly in the near-infrared (NIR) region due to internal leaf structure. Key Concepts and Terminologies Spectral Signature : Unique reflectance pattern of vegetation across wavelengths. Chlorophyll Absorption : Strong absorption in blue and red wavelengths. Canopy Reflectance : Combined reflectance of leaves, branches, and background soil. Important Vegetation Indices NDVI (Normalized Difference Vegetat...

Image Transformation

In remote sensing, image transformation is used to create new images by manipulating existing ones, enhancing the information extracted from each pixel. Multi-image manipulation and other transformations allow analysts to highlight specific features, compare time-series data, or reduce data dimensions for better interpretation. 1. Multi-Image Manipulation This technique involves applying mathematical operations to multiple images, typically from different bands, dates, or sensors, to highlight specific features or changes. Common methods include: a. Image Addition Purpose : Highlights areas of similarity or enhances features when the same objects appear in multiple images. How it Works : By adding pixel values from two images, the output image emphasizes areas where brightness is high in both. Example : Enhancing overall brightness or detecting seasonal changes by adding images from different times. b. Image Subtraction Purpose : Identifies differences between images, useful for chan...