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The use of Artificial Intelligence (AI) in Agriculture

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Artificial Intelligence is fast growing and is currently expected to grow from $40.17 billion to $51.56 billion in 2021 at a compound annual growth rate (CAGR) of 28.4%.

In the agriculture sector, the use of artificial intelligence has become more evident. Farmers are beginning to opt for more technological methods to complete certain tasks. Why is that? Through farming and livestock keeping countries can provide for their nations and with the current increase in the global population, food production needs to increase. However traditional farming methods are not enough to handle the demand that comes with population growth.

As a result, AI is steadily emerging as part of the agriculture industry’s technological evolution. The challenge is to increase the global food production by 50% by 2050^2 to feed an additional two billion people.

agtech automation TX harvester advanced.farm
Image: Advanced Harvester

How is AI used?

Artificial Intelligence in agriculture is used in many ways such as:

Intelligent spraying of chemicals: Through AI systems, there is technology that assists in improving the quality and the accuracy of the produce by detecting diseases in the plants, poor nutrition and detecting weeds and from the information gathered systems detect the best herbicide to apply.

Farm Harvesting: Machines and robots after a complete farming season go in and do the manual labor of picking produce from the land. This is an advantage because more yield is picked up in less time and machines are more accurate and leave cleaner results at the end.

Predictive Analytics: As you may have guessed, AI is used for estimation purposes for the future in terms of farming. There is technology that can be used in many ways such as predicting the best time to sow and the conditions that are considered would be the prevailing weather, winds in that region. Looking at the soil health is another important factor that is considered before planting new crops.

Another factor for prediction is crop yield and price forecasts, this means that technology once again is put in place to predict the prices that could be set by farmers for their produce. This is done by collecting data from the markets and crunching numbers to set suitable prices that don’t leave farmers running at a loss. To predict the crop yield with the help of big technologies like big data and machine learning, companies can detect the disease and pest infestation and estimate the output and yield as well as forecast prices. This guides the farmers and governments on future price patterns and demand level.

This may be an advantage in the long run in the sense that farming becomes easier, faster, more accurate and there is less waste. At the same time it allows for farmers to maximize in terms of profits as well as staying on top of the business market. This also allows for trading between boarders throughout different countries in order to aid in increasing a countries GDP.

However, at the same time this may lead to an increase in unemployment rates as machines begin to takeover human tasks. Overtime if systems aren’t updated timely incorrect data may be collected and this may go back to initial problems such as waste problems. This systems may increase the money put into farming which may drain the farmers as well as government expenditure when funding some farmers.


Sources:

https://www.businesswire.com/news/home/20210816005399/en/Global-Artificial-Intelligence-Market-Report-2021-to-2030—COVID-19-Growth-and-Change—ResearchAndMarkets.com

https://www.wipro.com/holmes/towards-future-farming-how-artificial-intelligence-is-transforming-the-agriculture-industry/