AI Is Enhancing Global Agriculture
By: Aravind Sanjeev
Tags: Agriculture, Future, AI, Technology, Artificial Intelligence

AI Is Enhancing Global Agriculture

With over 25% increase in population by 2050, there is only a 4% increase in agricultural land. AI is helping to make more out of the preexisting land. Aravind Sanjeev Follow Dec 18, 2020 · 5 min read

Farmer holding freshly harvested crop. Image for representational purpose only.

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The United Nations Food and Agriculture Organization estimates that the world population will increase by an additional 2 billion in 2050. At the same time, the land available for agriculture will only increase by 4%. That’s over 25% increase in population to only a 4% increase in agricultural land. There is a need for the world to accommodate the exponentially increasing population to a marginally increasing food production.

Rapid industrialization in the 19th and 20th centuries allowed agriculture to flourish. Our food production has reached skyrocketing heights with most of the industrialized world managing a surplus. Along with large food availability and developments in medical science helped improve the average human life expectancy. This has resulted in a population boom that is currently overrunning our resource supplies including the supply of food.

We are in desperate need of another agricultural revolution. Like how the previous revolution was lead by the invention of mechanical machines, fertilizers, and pesticides, the new revolution is likely to be lead by artificial intelligence. Nazrini Siraji is a Google developer that developed an app called “Farmers Companion”. She developed it using Google’s open-source machine learning platform, TensorFlow. The app allows you to detect Fall Armyworm (FAW), a crop-destroying caterpillar that’s affecting African agriculture. The app even identifies which stage the worm is in its lifecycle and suggest appropriate remedies.

What we saw here is disease detection, one of the ways AI can help increase our crop productivity. A similar story came from Microsoft, which is working with farmers from the Indian state of Andhra Pradesh. They were able to achieve 30% more crop yield per hectare just by detecting the appropriate time to sow. Traditionally, farmers used to sow using conventional wisdom. AI has not just helped the farmers by aiding appropriate sowing time, it also forecasted the price of commodities which helped the state’s policymakers to determine the minimum support price.

Blue River Technology is a company that specializes in AI-based weed control. Weed Science Society of America (WSSA) has estimated that the US and Canada are losing over $43 billion annually due to uncontrollable weeds. The company has developed a device called See & Spray which uses machine learning and computer vision to identify weeds and spray optimal amount to control them. This significantly reduces the amount of pesticide that is being used up on farms. The company claims up to an 80% reduction in the use of pesticides. The United States reportedly uses over 1 billion pounds of pesticides every year.

When it comes to weed detection, drone imagery can be used to cover large chunks of land. An estimated 60 hectares of land in just 30–40 minutes. The same is also used to check plant health, drainage and irrigation problems, pest infestations, etc.

Another problem plaguing the agricultural industry is labor cost and the availability of workers. Agriculture is a field most people are increasingly opting to stay out of. Labor in the agricultural industry is also seasonal making it an unreliable choice. AI seeks to solve this problem by developing automated machines. Machine learning is used to detect an appropriate harvest. Root AI is one such company looking to develop machines capable of automating harvest. Their Virgo harvesting robot identifies ripe fruit and picks them using mechanical arms.

A second problem with human workers is the need for regular recess, inability to work for long periods, and inability to work during nights. The robot could work up to 20 hours straight during daytime or night. This massively improves the speed and efficiency of the harvesting process.

Soil erosion is the next key factor that is dramatically affecting global agriculture. The United States Department of Agriculture has estimated that soil erosion causes approximately $44 billion in losses in the agricultural sector. Berlin-based tech startup PEAT tries to solve this problem with their deep learning application Plantix. It detects nutrient deficiencies in the soil and also provides restoration tips. The app is also capable of detecting plant diseases.

A similar app comes from California-based tech startup Trace Genomics. The app works similar to Plantix and analyzes soil to learn its strengths and weakness. The knowledge is used to increase the potential for healthy crop production.

Another important application of AI in agriculture is weather prediction. The Weather Company now acquired by IBM is leading the industry in AI-assisted weather prediction. The new IBM Global High-Resolution Atmospheric Forecasting System (IBM GRAF) is helping it push even greater heights. IBM GRAF has replaced the traditional 12 KM range between data points to 3 KM. This has increased the accuracy of weather prediction. It has also added more places like Beijing, New Delhi, and Brazil which are traditionally off-limits in typical weather prediction systems. This lead to the wide democratization of weather prediction as it gathers data across the entire globe and shares predictions in real-time. It is also getting more accurate as machine learning improves over time.

All this encompasses the concept of precision farming. The ultimate aim of precision farming is to increase profitability, efficiency, and sustainability. It manages to accomplish it via the combination of AI-powered disease detection, soil analysis, weather prediction, and suggested optimized prevention methods.

Now, implementing AI in farming is not without its challenges. Although such advance in AI has helped eliminate a lot of guesswork in agriculture and instead lead farmers to opt for meaningful decisions, there is a lot to move forward. The system isn’t perfect but it is in a rapidly maturing phase. Since crops are grown during seasons, there is usually only a once-in-a-year chance to study the farming situation. This is stretching the learning time by years. The machines are prone to giving inaccurate results when their data set is shallow. However, as time goes, the machines are getting better and better. The data pool available for the machines is increasing. It already displayed immense accuracy in its premature phase. As the next decade replaces this one, we are looking at a very possible AI-led agricultural revolution.

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