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5 Steps To Create An Artificial Intelligence That Will Manage Your Agricultural Robotics

Ryan Pell
by Ryan Pell on Jul 16, 2019 11:33:00 AM

 

When people think of agriculture, they think of a farmer leading a horse and plow through a field. However, if you take a closer look at the way this industry is operating now, you won’t believe your eyes.

View of corn fields and farms in Southern York County, Pennsylvania.five ideas for implementing AI that manages agricultural robotics

Artificial intelligence (AI) is gaining a foothold in agriculture. Many companies plan to equip their fleet of agricultural machinery with AI-controlled robotics in the near future. According to an IDTechEx report, 80 percent of companies plan to introduce autonomous machines like tractors, small agricultural robots, mobile dairy farm robots, and drones for harvesting crops.

Agricultural enterprises are on a constant quest to increase production, and implementing AI-powered agricultural robotics is the best solution.

But where to start? What should be done to keep up with this rapidly developing technology?

Here are five ideas for implementing AI that manages agricultural robotics.

1. A Clean Bill of Soil Health

The condition of the soil that will be used for harvesting should be tested to define its health and quality. This analysis of soil conditions will allow farm operators to choose organic fertilizers that will improve the soil’s ability to transmit water and air.

AI-based solutions provide an insightful analysis of soil samples and give you actionable results. Trace Genomics, a California-based company, has developed an AI-based system that performs a DNA analysis of soil samples using specific tools and robotics. You only need to provide a small sample of soil to obtain a full DNA analysis.

The process of harvesting always begins with a field test to prevent defective crops. The soil DNA analysis includes pathogen screening and complete sets of data on soil health. These tests must be done regularly during pre-planting and post-harvesting.

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2. Shaking off Disease

Planting is one thing, but what about protecting what you’re growing? Threats like deforestation and soil dehydration can impact the quality of crops, leading to various diseases, the consequences of which can cost millions of dollars.

AI-based solutions can prevent this from happening by screening for and preventing disease outbreaks. For instance, the revolutionary Blue River Technology has created a range of weed-control robots dubbed “See & Spray” that use computer vision and machine learning.

 

 

See & Spray machine in action. Video credit: YouTube / Blue River Technology.

The machine detects weakened crops that have been brought down by weeds and sprays them. The great thing about this technology is that instead of covering the entire field with pesticides, the robot only sprays affected crops.

3. Forewarned, Forearmed

What if your crops are affected by a disease? AI-powered apps that enable robotics to detect weakened crops are a perfect solution.

Plantix offers an app that aims at improving profitability by performing a health check of the crops to provide full disease control. The biggest advantage of this app is that it boasts a complete library of plant diseases, which helps in detecting the problem and quickly coming up with a solution.

 

Video credit: YouTube / Plantix.

Apps like Plantix use machine learning to allow robots to operate according to a specific function, such as detecting weakened plants. When the robot detects a potentially disease-ridden plant, it archives all the relevant information as well as data on how the farmer can rid the crops of the disease and prevent it from happening again.

4. Will It Rain? Will It Shine?

Weather affects the health and performance of crops. Unfortunately, it’s not always possible to predict the weather with existing standard tools.

It’s getting harder to predict the weather for agricultural purposes because of the drastic effects of climate change. “Changes in temperature, as well as the growth of atmospheric carbon dioxide, have a significant effect on weather, and on agriculture consequently,” says researcher Martin Heuter.

AI solutions allow farmers to predict weather and analyze crop sustainability. For instance, aWhere, a system based on a machine-learning algorithm, uses a satellite connection to help you predict the weather. Such AI-powered technology is very effective and accurate.

5. The Fruits of AI Labor

Lastly, let’s talk about the AI-based solutions for harvesting. Harvesting is known to be quite labor-intensive. However, with AI-powered robotics, it doesn’t have to be this way.

Harvest CROO Robotics, a company established in 2013, went to market with a robot that can harvest strawberries and pack them. It can harvest up to 8 acres a day and does the work of approximately 30 humans. This AI-powered solution is a great way to maintain high production and save considerable amounts of money.

The Bottom Line

Implementing AI that manages agricultural robotics is a gradual and time-consuming process, but one that will definitely bring many benefits. Hopefully, this guide will help agricultural businesses consider how they can make use of AI in the future.

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Ryan Pell
Written by Ryan Pell
Ryan is a passionate writer who likes sharing his thoughts and experience with the readers. Currently, he works as a real estate agent at https://flatfy.ro. He is an AI enthusiast who thinks that machine learning and other technologies can help society, so he hopes his content will inspire more people to discover how to use it.
Connect with the writer:
https://flatfy.ro/

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