The Malthusian nightmare is upon us. This trope was an integral part of thinking about global population growth and the ability of the world to feed itself.
Yet the technological agriculture innovations always were a step ahead. With the advent of the green revolution in the 1960s, new technologies including artificial intelligence, computer vision and a variety of data sources are taking the center stage in the 21st century.
Just like introduction of chemical fertilizers, agrochemicals, and controlled water-supply boosted crop yields nearly half-a-century ago, an even greater productivity boost is expected from artificial intelligence in agriculture and utilization of latest technologies.
Are you ready for the Fourth Agricultural Revolution?
The concept of turning agriculture into an exact science might have started a few decades ago. Yet practical applications started to emerge in the late 1990s.
Precision agriculture encompasses a diverse set of technologies:
- Geolocation. GPS was an integral part to introduce movement precision and to advance the autonomy of agricultural machines.
- Geospatial data. The satellite-eye view enabled an unprecedented vantage point for field mapping. The improvements in satellite imaging definition also provided vast amounts of data for crop yield and soil analysis.
- Sensors and Internet-of-Things. Ability to measure water levels, soil quality and other relevant aspects was introduced with a lot of low-cost sensors that can harness the field data in near-real time.
This is just a technological shortlist, as there are many more solutions that enable the Fourth Agricultural Revolution. Yet they serve as the foundation for precision agriculture.
So where does the artificial intelligence in agriculture fit in?
Artificial Intelligence in Agriculture
All precision agriculture technologies have one thing in common – they enrich farmer decision making with vast amounts of data. Remember the aspect of turning farming into exact science? Farmers are becoming true crop scientists. The technologies enable them to make scientific predictions, as well as employ data-based mitigation strategies.
Farming is entering the realm of big data. And big data analysis requires big data technologies.
For one, artificial intelligence in agriculture can be employed for visual crop analysis. Whether it is satellite imagery, or aerial drone footage, computer vision models can analyse crop growth and pinpoint particular problem spots. This can be done in semi-autonomous fashion, with specifically trained machine learning models and constant input of latest aerial data. With a periodic report or a simple alert, the farmer can then employ mitigation strategies in the precise location.
Employed at different vantage points and with specific crops in mind, artificial intelligence in agriculture can be employed to monitor for plant malnutrition or plant diseases.
In a fully controlled growth environment (such as a greenhouse), AI can monitor growth, temperature, hydration and many other aspects basically turning your greenhouse into an agricultural laboratory.
Yet even in a geographically vast area, computer vision monitoring can harness data where other methods (such as sensors) – when it is impractical to employ them due to sheer size of fields or no internet access.
Your Partner for Artificial Intelligence in Agriculture
As mentioned before, technology is turning farmers into precision scientists. The advent of autonomous agricultural machinery will further remove farmers from manual work and put them in the shoes of Chief-Growth-Officers making informed farming decisions.
At EasyFlow we develop artificial Intelligence and computer vision models to enable precision farming.
The Contact us today to discuss how artificial intelligence can tackle your daily farm management challenges.