By Yogita Kinha, Consultant and BloggerThe Green Revolution during the 1950s and 1960s remarkably drove up the global food production around the world, saving a billion people from starvation.
The revolution led to the adoption of new technologies like high-yielding varieties (HYVs) of cereals, chemical fertilizers and agro-chemicals, better irrigation and mechanization of cultivation methods.
India followed suite and adopted the use of hybrid seeds, machine, fertilisers and pesticides.
While these practices solved the food shortage problem, they created some problems too in terms of excessive use of fertilisers and pesticides, depletion of ground-water, soil degradation etc.
These problems were exacerbated by lack of training to use modern technology and awareness about the correct usage of chemicals etc.
According to the UN Food and Agriculture Organization, the global population will increase by 2 billion by 2050.
With limited arable land available and exponentially increasing mouths to feed, we’re now in need of a second Green Revolution.
A Green Revolution that is smarter, agile & environmentally conscious — a Green Revolution driven by big data, Internet of Things (IoT), artificial intelligence (AI), and machine learning.
Some of the challenges faced by farmers from seed sowing to harvesting of crops are as follows:The major advantage of focusing on AI-based methods is that they tackle each problem separately and rather than generalising, provide customised solutions to a specific problem.
Farmers are deploying robots, ground-based wireless sensors, and drones to assess growing conditions.
Many researchers and pilot projects have been conducted to test the implications of the involvement of AI applications in improving agriculture.
Many big organisations and start-ups are working on to develop applications and IOT enables devices to deploy AI applications to help farmers on a large scale.
From detecting pests to predicting what crops will deliver the best returns, artificial intelligence can help humanity confront one of its biggest challenges: feeding an additional 2 billion people by 2052, even as climate change disrupts growing seasons, turns arable land into deserts, and floods once-fertile deltas with seawater.
Farmers can use AI to determine the optimal date to sow crops, precisely allocate resources such as water and fertilizer, identify crop diseases for swifter treatment, and detect and destroy weeds.
Machine learning makes these activities smarter over time.
It can also help farmers forecast the year ahead by using historical production data, long-term weather forecasts, genetically modified seed information, and commodity pricing predictions, among other inputs, to recommend how much seed to sow.
References:The Future of AI in Agriculture – Intel A robotic lens zooms in on the yellow flower of a tomato seedling.
Images of the plant flow into an artificial.
https://www.
mindtree.
com/sites/default/files/2018-04/Artificial%20Intelligence%20in%20Agriculture.
pdfhttps://cloudblogs.
microsoft.
com/2018/11/29/feeding-the-world-with-ai-driven-agriculture-innovation/ https://www.
sciencedirect.
com/science/article/pii/S2589721719300182Originally published at https://www.
edvancer.
in on June 30, 2019.
Bio: Yogita Kinha is a competent professional with experience in R, Python, Machine Learning and software environment for statistical computing and graphics, hands on experience on Hadoop ecosystem, and testing & reporting in software testing domain.
Original.
Reposted with permission.
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