Have you ever wondered how much technology actually saves a project? Well, the data is in! Here GeoWing Academy looks at a real world project that was done and compared traditional field data capture methods using pen and paper versus using a cutsomised, project specific QField app design using smart attributes to collect 830 data points. Each point had 15 fields each (some with variables) and also required field photos and GPS points of all field data.

All monetary values in Australian Dollars (AUD)

Have a look at the table and short video below, it will blow your mind!

In this deep dive conversation, we unpack what it is that GeoWing Academy is all about! What the mission is, what its all about and how easy it is for anyone and everyone to upskill themselves in all things drones and GIS.  

The cape dune mole rat is an interesting creature. Mole rats usually live in social colognes but not the large Cape Dune. These fellas are viciously territorial, liver on their own and love keeping to themeselves.

By looking at cape dune mole rat behaviour observed by Spinks, A. C., Bennett, N. C., Jarvis, J. U. M. (1999). Home range size and habitat use in the Cape dune mole-rat Bathyergus suillus. Journal of Zoology, 248(1), 39–47. and Bennett, N. C., & Faulkes, C. G. (2000). African Mole-Rats: Ecology and Eusociality. Cambridge University Press. GeoWing Academy designed a method of using drone photogrammetry outputs and GIS software to track burrowing activity over 1 week in 2024 and 1 week in 2025. The results are interesting to say the least!

See the latest vlog below for more details!

Alien Invasive Plants (AIP) have become a major threat to South Africa’s sensitive Fynbos biome. In 2017 and 2018, fires in the Western Cape region killed 8 people and destroyed over 2000 homes and devastated biodiversity in the region. The intensity of these fires was amplified by the massive amounts of AIP, in particular Black Wattle (Acacia mearnsii), Gum (Eucalyptus sp.) and Pine (Pinus sp.) that have gone unchecked and uncontrolled for decades. Now in 2025, the problem is even more pronounced and current methods of monitoring and clearing AIP are very inefficient, very time consuming and very costly. 

Drone technology and open source software can be used to map, locate, classify age, determine ease of access, determine urgency for clearing, define burn intensity and then plan removal projects based on this information. Drones can also be used to clear AIP by using precision spray methods to kill off very dense stands. This coupled with ground based removals will drastically improve current methods and we may actually have a small chance of regaining the biodiversity lost. 

This presentation was hosted by the Save Wild Project. For more information have a look at the presentation given to Western Cape municipality, government and communities on the subject of tech based applications for UAVs and AIP control here: 

ere Machine Learning and RGB drone data for plant health? GeoWing Academy decided to take a look at how effective using a custom built machine learning pipeline and RGB derived plant health indices for crop health analysis. The results are SUPER interesting!

A strawberry patch became the unwilling test subject for this experiment and two data sets where captured; one set on a cloudy day and one set on a sunny day. The machine algorithm was trained to find each strawberry within the patch and then compute each individual plants Modified Photochemical Reflectance Index (MPRI) value.

The MPR Index is an RGB-based vegetation index that compares green and red reflectance to estimate plant health. It produces higher values where plants reflect more green and absorb more red — a common sign of healthy, photosynthetically active vegetation. Higher MPRI values indicate more green reflectance relative to red, which often corresponds to healthier vegetation (since healthy plants reflect more green and absorb more red due to chlorophyll activity). Lower MPRI values suggest more red reflectance relative to green, which can indicate stressed or sparse vegetation, or non-vegetated surfaces.

The overcast scan MPRI statistic mean of 0.087 indicates that the plants in the  patch were moderately healthy which ties in well with the ground truth evaluation done by the farmer. The patch was then subject to cleaning (all dead and dying strawberry leaves removed) between the two scan dates, the next scan being conducted on a sunny day. The sunny day scan bore some very interesting result indeed!!

This index will not be as rigid as multispectral imagery and will only display surface (visual) health differences but it certainly has its place for crop management where time and money are a factor of production. So can it be a useful tool? Find out in GeoWing Academies latest vlog: