AI-Driven Innovations For Agricultural Problems: Part 1

In the previous article of this three-part series, we took a look at several of the problems agriculture faces as it tries to scale to adapt to 21st-century demand.

Now, over the next two articles, we’ll take a deep dive into the innovative solutions that have manifested to resolve these pivotal issues.

Problem: Resource Allocation

As we saw in the last article, approximately 70% of all water usage worldwide is due to agriculture. Of that, 60% is wasted. Besides, several chemicals and pesticides are used in excess and often find their way into the water supply. Here’re some of the solutions that have emerged.

Solution: Predicting Water Usage by Individual Users in a Day

A researcher at the University of Cordoba, Spain, developed a decision-making prediction model that predicts individuals’ water usage in a day. It takes in various input variables, ranging from easily quantifiable ones such as temperature, humidity, and plot land to be watered in addition to more complex values describing traditional watering methods in the area and holidays during the watering season. Then, by applying a specialised ‘fuzzy logic system’ model, curves are established for the inputs, and a neural network creates a relationship between them. Finally, it is able to predict the estimated millimetres of water usage. [1]
This data allows water users associations to accurately manage the water supply and prepare for maintenance, repair, and supply issues in advance without wasting water or negatively impacting areas requiring it.

Solution: Helping New Farmers With Water Usage

In Japan, a government-supported digital farming solution involves collecting data from soil and light sensors. Then, it uses artificial intelligence for computers to advise farmers on the quantity of water and fertilisers to be used. These data-driven insights are invaluable to new, inexperienced farmers who may otherwise be guilty of wasteful water usage and in addition, allows them to increase their productivity. [2]

Solution: Studying Water Usage in Fields to Find Issues and Improve Usage

ConserWater, founded by Caltech graduate Aadith Moorthy, is a cost-effective A.I. monitoring system that tracks water distribution data in a field through satellites and historical data with no need for ground sensors or manual inspection. Then, it uses artificial intelligence to give recommendations for fine tuning the irrigation supply and watering process to attain optimal soil moisture and simultaneously minimising excess water consumption. It also allows users to identify leaks in irrigation pipes. Users can access the system through a mobile app or desktop website. [3]

Solution: Using Artificial Intelligence to Allow Cotton Farmers to Use Pesticide Effectively

Over 55% of India’s pesticide is used in cotton farming, but overuse of it often damages crops and quality. That’s why the Indian research institute Wadhwani AI built an artificial intelligence model that can determine how many pests are in the area and send notifications to farmers advising them on pest usage. Over 18500 farmers use the low-resource intensive mobile app to send images of pests in the region and receive advice from the app on how much pesticide to use using three levels of alerts — green, yellow, and red. Experiments showed that this led to a 25% improvement in cotton crop yields due to more effectively managing pests and not wasting resources [4]

Solution: Only Using Chemicals to Eliminate Invasive Plants Where Needed

Weeds that compete with neighbouring crops for resources such as sunlight, water, and nutrients are known to cost the farming industry tens of billions of dollars a year [5]; which is why many farmers use chemicals very generously to combat them. Often, though, farmers have to spray chemical products all around the field to eliminate them, which leads to much waste and inefficiency. That’s why a team of researchers at the French Institut National des Sciences Appliquée developed a model that can take images captured by drones of regions of a farm (the model is currently restricted to fields of beets, spinach, and bean). Then, it labels areas with heavier weed concentration. This allows farmers to focus on specific areas at the right time to use chemicals and reduce waste efficiently. [6]

Problem: A Shrinking and Aging Labour Force

Even as the worldwide demand for agricultural output increases, the farmers to produce it are shrinking in number and reducing in age. Artificial intelligence-powered innovations are therefore playing a vital role in filling in this gap in the industry.

Solution: Harvesting Crops and Picking Produce Using AI-powered Robots

Traditionally, produce on agricultural land is picked by farmers physically. But as their strength in numbers reduces, agrarian robots are taking their place, which are capable of bulk harvesting at a much-increased accuracy and speed without requiring vacation or time off. These machines help improve the yield’s size and reduce waste from crops being left in the field. [7]

There are several variations of this category of robots. Harvest Automation’s HV-100 is a farmer’s assistant that can move potted plants with ease and precision. Several cost-effective fruit picking systems also exist — whether it’s citruses with the Energid Citrus Picking System that can pick citruses every three seconds, the Agrobot E-Series that in addition to picking strawberries can identify individual strawberry’s maturity, a robotic vacuum-powered apple picker, an EU campaign-backed robot called Sweeper that picks peppers based off their colour, or an MIT robot gardener that connects wirelessly to several sensors attached to plants which call the water robot. [8]

Solution: Using Driverless Tractors

In addition to using AI-powered physical solutions, self-driving tractors are also an effective method of combating agricultural labour force issues. Driverless tractors with several advanced AI and non-AI powered features can ease some of the burdens on farmers. Tractors can autosteer themselves in a straight line and change direction without any farmer intervention allowed due to a combination of AI and GPS technology. Tractors such as Mahindra & Mahindra’s driverless tractor can steer to the next row for continuous operation without any intervention of the driver. [9] This allows them to spray, plant, plow and weed cropland without any human intervention necessary. [10]

Where Next?

In the next post in two weeks, we’ll go over some of the several solutions to the other issues we outlined in our first post that we haven’t covered here yet.

References

1. “Smart Farms? How AI Can Solve Water Wastage | Earth.org — Past | Present | Future.” 2019. Earth.org — Past | Present | Future. Earth.org. April 10, 2019. https://earth.org/smart-farms-how-ai-can-solve-water-wastage- /.
2. “Artificial Intelligence Saves Water for Water Users Associations.” n.d. EurekAlert! Accessed November 28, 2020. https://www.eurekalert.org/pub_releases/2018-07/uoc-ais072318.php.
3. Orr, Nathan. 2018. “ConserWater: Innovating AgriTech Using A.I.” The Burn-In. October 25, 2018. https://www.theburnin.com/thought-leadership/conserwater-innovating-agritech-using-a-i/.
4. Puja Das. 2020. “AI Is Reducing Indian Cotton Farmers’ Pesticide Use.” Analytics Insight. November 6, 2020. https://www.analyticsinsight.net/ai-is-reducing-indian-cotton-farmers-pesticide-use/.
5. “Crop Loss | Weed Science Society of America.” n.d. https://wssa.net/wssa/weed/croploss-2/.
6. Salian, Isha. 2019. “AI Helps Farmers Whack Weeds, Pesticide Use | NVIDIA Blog.” The Official NVIDIA Blog. November 1, 2019. https://blogs.nvidia.com/blog/2019/11/01/ai-helps-farmers-whack-weeds/.
7. “Towards Future Farming: How Artificial Intelligence Is Transforming the Agriculture Industry — Wipro.” n.d. Www.Wipro.com. https://www.wipro.com/en-IN/holmes/towards-future-farming-how-artificial-intelligence-is-transforming-the-agriculture-industry/.
8. NT, Baiju. 2019. “Top 14 Agricultural Robots for Harvesting and Nursery.” RoboticsBiz. April 10, 2019. https://roboticsbiz.com/top-14-agricultural-robots-for-harvesting-and-nursery/.
9. www.ETAuto.com. n.d. “Mahindra Unveils First-Ever Driverless Tractor in India — ET Auto.” ETAuto.com. Accessed November 28, 2020. https://auto.economictimes.indiatimes.com/news/automotive/farm-equipment/mahindra-unveils-first-ever-driverless-tractor-in-india/60746286.
10. Bloomberg. 2019. “Coming Soon: A Driverless Tractor.” @businessline. The Hindu BusinessLine. May 16, 2019. https://www.thehindubusinessline.com/economy/agri-business/coming-soon-a-driverless-tractor/article27151916.ece.

Arjun Sharma is a Student Ambassador in the Inspirit AI Student Ambassadors Program. Inspirit AI is a pre-collegiate enrichment program that exposes curious high school students globally to AI through live online classes. Learn more at https://www.inspiritai.com/.