This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica.
Increasing yields has been a key goal for farmers since the dawn of agriculture. People have continually looked for ways to maximise food production from the land available to them. Until recently, land management techniques such as the use of fertilisers have been the primary tool for achieving this.
Challenges for Farmers
Whilst these techniques give a much improved chance of an increased yield, problems beyond the control of farmers have an enormous impact:
- Parasites – “rogue” plants growing amongst the crops may hinder growth; animals may destroy mature plants
- Weather – drought will prevent crops from flourishing, whilst heavy rain or prolonged periods of cold can be devastating for an entire season
- Human error – ramblers may trample on crops inadvertently, or farm workers may make mistakes
- Chance – sometimes it’s just the luck of the draw!
AI techniques can be used to reduce the element of randomness in farming. Identification of crop condition and the classification of likely causes of poor plant condition would allow remedial action to be taken earlier in the life cycle. This can also help prevent similar circumstances arising the following season.
Computer Vision to the Rescue
Computer vision is the most appropriate candidate technology for such systems. Images or video streams taken from fields could be fed into computer vision pipelines in order to detect features of interest.
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A key issue in the development of computer vision systems is the availability of data; a potentially large number of images are required to train models. Ideal image datasets are often not available for public use; this is certainly the case in an agricultural context. Nor is the acquisition of such data a trivial exercise. Sample data is required over the entire life cycle of the plants – it takes many months for the plants to grow, and given the potential variation in environmental conditions, it could take years to gather a suitable dataset.
How Synthetic Data Can Help
The use of synthetic data offers a solution to this problem. The replication of nature synthetically poses a significant problem: the element of randomness. No two plants develop in the same way. The speed of growth, age, number and dimensions of plant features, and external factors such as sunlight, wind and precipitation all have an impact on the plant’s appearance.
Plant development can be modelled by the creation of L-systems for specific plants. These mathematical models can be implemented in tools such as Houdini. The Digica team used this approach to create randomised models of wheat plants.
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The L-system we developed allowed many aspects of the wheat plants to be randomised, including height, stem segment length and leaf location and orientation. The effects of gravity were applied randomly and different textures were applied to modify plant colouration. The Houdini environment is scriptable using Python; this allows us to easily generate a very large number of synthetic wheat plants to allow the modelling of entire fields.
The synthetic data is now suitable for training computer vision models for the detection of healthy wheat, enabling applications such as:
- filtering wheat from other plants
- identifying damaged wheat
- locating stunted and unhealthy wheat
- calculation of biomass
- assessing maturity of wheat
With the planet’s food needs projected to grow by 50% by 2050, radical solutions are required. AI systems will provide a solution to many of these problems; the use of synthetic data is fundamental to successful deployments.
Digica’s team includes experts in the generation and use of synthetic data; we have worked with it in a variety of applications since our inception 5 years ago. We never imagined that it could be used in such complex, rich environments as agriculture. It seems that there are no limits for the use of synthetic data in the Machine Learning process!
Jim Carroll
CTO, Digica
