The role of machine learning in tomorrow’s agriculture technologies
FARMERS are met with many challenges in their daily activities due to unpredictable weather and geographical conditions, market factors, and local, regional, and national policies.
All of these variables directly impact agricultural production and ultimately have a negative effect on the farmer’s income.
Further, if agricultural activities are disrupted, present needs for food and textiles cannot be met, and if disruptions are prolonged, future needs might be compromised as well.
To ensure the sustainability of the agriculture industry, farmers need to be able to make sense of all existing variables and be fully informed about the current state of the ecosystem. This will help them charter the right strategies.
In order to do so, a lot of data needs to be collected, processed, learned, and analyzed. However, humans (especially farmers) generally do not have extensive analytical capabilities. Even data scientists need time to work through the masses of data to make informed decisions.
Machine learning (ML), therefore can play a significant role. The technology makes it possible for farmers to use predictive analytics to better preparing for unfavorable conditions using data from multiple sources, analyzing them, simulating different scenarios, and provide in-depth insights.
The technology can help farmers take real-time weather conditions, soil nutrients levels, geographical changes, and stock trends into account to ensure they make the best decisions for themselves.
A common issue, for example, is price inflation due to a disrupted livestock supply – highly unpredictable in itself but its effects on farmers’ livelihoods is catastrophic.
ML not only helps farmers prepare for such events through its predictive analytics features, but can also provide suggestive insights and recommendations. Farmers, then, can mitigate the risks effectively and execute accurate countermeasures to change the dynamics in the market.
Recently, an Argentinian farming association decided to deploy ML-powered solutions to process data and generate real-time insights. It also provides visibility into each stage of farming which encourages ‘precision agriculture’.
In Asia, farmers can adopt similar solutions to ensure resources are maintained and production costs are reduced.
All in all, agriculture needs to be sustainable to counter the variables that are unexpected or beyond our control in the future, and the key lies in making farming smarter with the right technology solutions.
This is especially crucial to remember as the United Nations announced that the world population is rapidly growing. To meet future demand, farmers need to learn to make better decisions.