Unmanned aerial survey drones (UAV’s or drones) equipped with multifunctional sensors are increasingly being used by corporate farmer groups, agronomists, biologists, and environmental scientists. Drones enable better decision making that may lead to boosts in yield and/or reduce costs associated with production from the perspective of the effects of heat and drought or other abiotic/biotic stress.
Early identification of plant stresses (abiotic and biotic) is an essential part of farming in Australia. Ensuring that maximum yields can be achieved (although in relation to heat and drought stress in broadacre agriculture might not always be the case), relies on timely visualisation of a farmer’s main asset which is his livelihood. To identify plant stresses early diagnosis is vital to enable agricultural decision making around potential solutions. Research undertaken using aerial imagery by Perth based company, Scientific Aerospace, will drive the development of a new generation of agricultural tools in the farmers’ toolbox, which can potentially increase profit margins.
Research has shown that Australian farms, including irrigated farms (market garden, viticulture, dairy), and non-irrigated farms (broadacre grain and livestock), can be made more able to cope with a range of biotic and abiotic stresses, including heat, drought, or frost, or pests and diseases by using UAV’s for early identification and diagnostics.
Our research has shown that farmers want to be able to understand how survey quality contour and 3D maps, digital surface and terrain models, and or geotagged vegetation index maps can be used to enable better decision making. Processed data is made visible in various ‘layers’ so that farmers get answers to specific questions about, for example, soil temp, soil moisture, crop nutrient status, biomass prediction, grain yield prediction, or other traits, which can be used to better understand how their crop is performing. Although most farmers may say that they can do this on their own PC and with their own drone, there is a lot of data processing involved that most home PC’s can’t handle with standard software like Excel. So that service can be provided on site, at a farm, or overnight through access to cloud-based services if a datalink is available.
The system integration capabilities needed for this have been developed by Scientific Aerospace which enables for precision, targeted, agricultural surveying specific to whichever abiotic or biotic stress is dominant at any time. The main arsenal includes, NDVI, thermal, multispectral, and, a new miniature spectrometer recently developed by UWA’s microelectronics laboratories. These technologies produce raw data images that make use of a significant amount of 3rd party processing software such as Pix4D and Context Capture, and, require scientific analyses to make use of the information contained within the data packages. This analytical service is currently being developed in house.
The benefit to potential clients is that Scientific Aerospace in Perth has made a significant investment integrating the optical output directly with the 3rd party drone software using Application Programming Interfaces (API’s) built and developed in house. Scientific Aerospace also utilises a custom application for Android tablets which is customised prior to each mission for a specific flight based on each client’s needs.
Case Study in Collaboration – Assessing Heat Stress Resilience in Wheat and Barley
To discover which wheat and barley varieties and soil combinations provide the best resilience to a combination of abiotic stress (heat and drought), Scientific Aerospace has teamed up with Murdoch University researcher and PhD candidate, Karl Svatos, to help develop a ‘proof of concept’ drone experiment. This was performed in the field at a Katanning Research Station.
The research is an extension of study about evapotranspiration and the combined effects of heat drought stress. For simplicity, the algorithm for the internal energy status of plant systems is not shown here, but it involves a number of temperature parameters, emissivity, heat transport and leaf resistance functions, which Karl hopes to publish as part of his PhD.
Two drones were equipped with either the NDVI multispectral and optical lenses or a thermal camera and flown at both the coldest time of day (dawn) and later in the afternoon at the hottest time of the day. Ground truth testing was also used to provide reference absorption, reflectance and saturation benchmarks, so that any deviation in the raw drone data could be corrected during data processing. The UAV was flown at altitudes to capture high resolution data specific to each flight requirement. The sensors looked vertically and sideways to capture 3D imagery.
Usable data was gathered and transferred to the on-board computer and saved for processing after the flight. Geotagged photos were extracted from the files and a grid overlain on data. These reference photos will be then used to determine individual aspects of the traits in question specific to each plot in the trial. The aerial data may be further processed using index map solutions such as SMS, AgPixel, ArcGIS, and other GIS systems to produce other data ‘layers’ that provide further, value-added insights.
The results from the various images (NDVI, thermal, RGB, multispectral) will be used to identify which wheat and barley varieties and soil combinations provide the best resilience to the combination of heat and drought stress in the field. The goal is that this may then be used to produce better varieties suited to more marginal environments.
Sci Aero envisions a world where automated systems are a part of everyday life saving time and money. That is why we are also collaborating in the development of a software pipeline associated with integrated autonomous systems, engineering software to incorporate climate records, rainfall, meteorological logs and real time drone data.
It is the vision of the collaborators that this ‘real time’ ‘big data’ API will be able to be remotely accessed from the field in the paddock by the farmer, providing real time support in response to desired traits for assessing the stress situation, thus saving time and money.
Prescription maps will be generated which can then be uploaded to a Farm Management Information System (FMIS) system or to the seeder, spreader, tractor, combine harvester or header for sowing, watering, fertilising, nitrogen optimisation, weed control yield monitoring and harvest timing management in real time.
Karl Svatos, PhD Candidate Murdoch University
Geoff Trowbridge, Business Development Director, Sci Aero Group
This paper was published by Future Directions International Pty Ltd.
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