LandWISE 2015 Presenter, Dan Bloomer
In an attempt to understand variability in crops, smartphone photos were processed to assess canopy size. By geo-referencing such images, they can be used for spatial analysis. Preliminary results showed considerable promise and a tool has been developed.
One use is for detailed nutrient planning and variable rate application, which requires spatial knowledge of final yield. A case study of an onion crop at the LandWISE MicroFarm is used as an example. Onions have a potential yield of around 100t/ha but the mean national yield in an average year is only 35t/ha.
The onion crop was planted on 7 June 2014. Fertiliser was applied at three intervals, 2 August, 27 September and 24 October 2014. Yields before curing within this 1ha paddock ranged from 0 – 85 t/ha.
Overhead photos of the crop were taken across 18 crop beds on 1 October, 28 October and 14 November 2014. The images were processed by ASL Software Ltd to determine an estimate of ground cover. The crop was lifted on 8 January 2015 and fresh weights taken from each bed. These final yield results were compared to the images taken during crop development.
Data collected on 14 November show strong correlation with final yield; R2 = 0.86.
However, these data were collected three weeks after our final fertiliser application.
Photographs taken on 1 October had an average ground cover of 4.6% (range of 1.1 – 8.4%).
The measurements at this stage showed good correlation with final yield; R2 = 0.71 once one image with areas of surface algae was discounted.
The October images were taken four days after the second fertiliser application, which could easily have been delayed. They were collected three weeks before the third fertiliser application.
This research suggests simple image analysis can provide early indications of final yield. It also suggests such images can provide timely information for adjusting rates and variable rate fertiliser application in onion crops.
To investigate the potential to create canopy maps, we automatically captured GPS referenced images. The images were processed to determine ground cover and displayed on Google Earth.
All image capture and processing was built into a smartphone application by ASL Software. There was a strong spatial pattern that could allow variable rate application.
The accuracy of the smartphone GPS may be adequate for large scale assessment of crops in big paddocks. However, it was not able to correctly locate the images and subsequent ground cover factors within the correct onion bed. Connection to an accurate GPS signal is being included to better locate each image point.
We thank ASL Software for app development, the LandWISE MicroFarm sponsors Ballance AgriNutrients, BASF Crop Protection, Centre for Land and Water and MicroFarm supporters for access to the onion crop.