The growing population on planet Earth and the deteriorating environment are leading humanity to a swift depletion of resources. As the consumption of protein increases in line with the growing population, it is impossible to eliminate or decrease protein consumption.

For the past decades, scholars have suggested breeding insects as an alternative source of protein. Edible insects contain high-quality protein, vitamins, and amino acids and an excellent food conversion rate.

For example, crickets need six times less feed than cattle, four times less than sheep, and twice less than pigs and broiler chickens to produce the same amount of protein. Therefore, insects are a great potential source of protein, either for direct human consumption or as a protein source for feedstock mixtures.

However, like in any new industry, there are production challenges, and insect farms, unfortunately, experience high rates of mortality mainly driven by unbalanced feed, wrong temperature and humidity, insect diseases, and harmful external conditions. The lack of automation in this sphere is a growing hazard to the safe growth and development of the insects breeding stock.

Why automatize the insect farming process?

Automatization is critical in minimizing labor needs and increasing production. Still, it will not have an immediate effect, as it had in other agricultural domains. This is linked to several reasons.

Firstly, each farm has at least 10 million insects being bred at the same time in one production cycle. Human labor simply can’t process this amount of information and customize breeding.

Secondly, organic waste consumed by Black Soldier Flies varies tremendously as well as the grass consumed by crickets. All this requires online customization of the breeding process.

Finally, too many parameters influence the growth of the insects: temperature, humidity, water quality, pressure, lighting, organic waste type, and quality. All these parameters, as well as the short life cycle of each colony, make it almost impossible to keep the farm effective without thorough control.

An increasing number of farms install sensors to efficiently analyze each stage of growth but that is proving to be not enough. Sensors and manual control based on such data allow only supervision of drastic changes in parameters and not the fine-tuning the product.

A game changer

AI technology in insect farming can become a major game changer for the industry, particularly because it can process several gigabytes of data per second and make fast and effective decisions. AI adoption in agriculture is already in process, but still not going fast enough due to the long cycles of growing (sometimes a year for different crops), not enough digitalization (most of the process is hard to measure and digitalize), and restricted controlled environment (open fields).

On the other hand, industrial farming has no such problems as the living cycle of insects is usually less than a month, giving farms a very good controlled environment that makes measurements simple. This allows the implementation of so-called supervised learning – the most common form of machine learning. With supervised learning, a set of examples is submitted as input to the system during the training phase. Each input is labeled with a desired output value. In this way, the system can analyze the output when the input comes. Comparable easy training AI will allow the introduction of very effective IT systems to manage farms.

Solving manpower crunch

AI can solve yet another problem in the industry: the lack of experts. For years entomology was not the science attracting top talents. With the boom of industrial farming within the last decade, we still lack the knowledge and subject matter experts who know how to develop really effective processes in farming. Training the top talents and research in industrial insects will take decades.

The role of AI comes into play here, where it can find correlations and process data at a very fast scale. For example, AI can define correlations between temperature regimes and organic waste types. It can also help with additives tests and effects on the protein profile of final products and much more. These can automate research and allow fast experiments that will help with the lack of practical knowledge.

Customizing the farming process

Finally, industrial farming isn’t using enough customization. Customization can bring a new level of efficiency, meaning that future farms can adopt the breeding process in seconds after an organic waste change. Additionally, farms can change breeding parameters to focus on needed product qualities based on market requests such as poultry food, fish food, fat oil required qualities, etc. Nowadays, customized processes don’t exist due to the low level of technologies, and AI can change this.

The future of AI in insect farming

Unfortunately, the maximum level of IT on industrial farms so far is limited to Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) systems, and minimal dashboards with sensor data. AI engineers are not really involved in the update of industrial farms as datasets are not available. One can hardly find any scientific publications on AI applications in industrial farming. This means that the industry is just being prepared for a major revolution shift.

We can expect in the nearest future multiple video recognition tools that will capture the breeding of crickets or larvae, acoustic systems to diagnose crickets’ conditions based on stridulation, and complicated sensor systems.

This process will come after the massive automatization of modern farms. AI can make great diagnostics, but to influence the process of breeding, we have to go through a merge of AI and automatization in the nearest future.

entoverse, ai, foodtech, technode global insider
Dr. Dmitry Mikhaylov is the Chief Technology Officer (CTO) and Co-Founder, EntoVerse.

TechNode Global INSIDER publishes contributions relevant to entrepreneurship and innovation. You may submit your own original or published contributions subject to editorial discretion.


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