Can artificial intelligence help improve agricultural productivity

How artificial intelligence can revolutionise agriculture?

  • In farming, different weather factors such as Rainfall, temperature, and humidity play an important role. …
  • For a better crop, it is necessary that the soil should be productive and have the required nutrition, such as Nitrogen, Phosphorous, and Potassium. …
  • In the agriculture lifecycle, it is required that we save our crops from weeds. …

How does artificial intelligence help farmers?

The Future of Farming: Artificial Intelligence and Agriculture

  • Global Warming and Agriculture: A Vicious Cycle. Global warming continues to threaten every aspect of our everyday lives, including crop production. …
  • The Benefits of AI for Environmentally-Conscious Agriculture. This is where AI enters the scene. …
  • The Risks of AI. …
  • Looking Forward: The Next Steps for AI in Agriculture. …

Is artificial intelligence the future of farming?

The vision system, built on artificial intelligence, will work with GPS to track the movement of the tractor inch by inch as it moves across the field. It allows the tractor to till a field and plants seeds in a straight line. The vehicle will also stop if an animal runs out in front of it.

What can AI and IoT do for agriculture?

  • Improved use of data collected from agriculture sensors;
  • Managing and governing the internal procedures within the smart agriculture environment including the management of the harvesting and storage of several crops;
  • Waste reduction and cost-saving;
  • Increasing production efficiency using automating traditional processes; and

More items…

How does AI improve farming?

9. Finding irrigation leaks, optimizing irrigation systems and measuring how effective frequent crop irrigation improves yield rates are all areas AI contributes to improving farming efficiencies. Water is the scarcest resource in many parts of North America, especially in communities that rely most on agriculture as their core business. Being efficient in using it can mean the difference between a farm or agricultural operation staying profitable or not. Linear programming is often used to calculate the optimal amount of water a given field or crop will need to reach an acceptable yield level. Supervised machine learning algorithms are ideal for ensuring fields and crops get enough water to optimize yields without wasting any in the process.

How can AI be used in agriculture?

7. Optimize the right mix of biodegradable pesticides and limiting their application to only the field areas that need treatment to reduce costs while increasing yields is one of the most common uses of AI and machine learning in agriculture today. By using intelligent sensors combined with visual data streams from drones, agricultural AI applications can now detect a planting area’s most infected areas. Using supervised machine learning algorithms, they can then define the optimal mix of pesticides to reduce pests’ threat spreading further and infecting healthy crops.

How will AI help the world?

AI, machine learning (ML) and the IoT sensors that provide real-time data for algorithms increase agricultural efficiencies, improve crop yields and reduce food production costs. According to the United Nations’ prediction data on population and hunger, the world’s population will increase by 2 billion people by 2050, requiring a 60% increase in food productivity to feed them. In the U.S. alone, growing, processing and distributing food is a $1.7 trillion business, according to the U.S. Department of Agriculture’s Economic Research Service. AI and ML are already showing the potential to help close the gap in anticipated food needs for an additional 2 billion people worldwide by 2050.

How can AI improve crop yield prediction?

2. AI and machine learning improve crop yield prediction through real-time sensor data and visual analytics data from drones . The amount of data being captured by smart sensors and drones providing real-time video streaming provides agricultural experts with entirely new data sets they’ve never had access to before. It’s now possible to combine in-ground sensor data of moisture, fertilizer and natural nutrient levels to analyze growth patterns for each crop over time. Machine learning is the perfect technology to combine massive data sets and provide constraint-based advice for optimizing crop yields. The following is an example of how AI, machine learning, in-ground sensors, infrared imagery and real-time video analytics all combine to provide farmers with new insights into how they can improve crop health and yields:

What is yield mapping?

Yield mapping is an agricultural technique that relies on supervised machine learning algorithms to find patterns in large-scale data sets and understand the orthogonality of them in real-time – all of which is invaluable for crop planning. It’s possible to know the potential yield rates of a given field before a vegetation cycle is ever started. Using a combination of machine learning techniques to analyze 3D mapping, social condition data from sensors and drone-based data of soil color, agricultural specialists can now predict the potential soil yields for a given crop. A series of flights are completed to get the most accurate data set possible. The following graphic shows the result of a yield mapping analysis:

How does drone data help in pest management?

Using infrared camera data from drones combined with sensors on fields that can monitor plants’ relative health levels, agricultural teams using AI can predict and identify pest infestations before they occur. An example of this is how the UN is using working in conjunction with PwC to evaluate data palm orchards in Asia for potential pest infestations, as is shown in the image below:

How can AI and machine learning be used in agriculture?

1. Using AI and machine learning-based surveillance systems to monitor every crop field’s real-time video feeds identifies animal or human breaches, sending an alert immediately. AI and machine learning reduce domestic and wild animals’ potential to accidentally destroy crops or experience a break-in or burglary at a remote farm location. Given the rapid advances in video analytics fueled by AI and machine learning algorithms, everyone involved in farming can protect their fields and buildings’ perimeters. AI and machine learning video surveillance systems scale just as easily for a large-scale agricultural operation as for an individual farm. Machine-learning based surveillance systems can be programmed or trained over time to identify employees versus vehicles. Twenty20 Solutions is a leader in the field of AI and machine learning-based surveillance and has proven effective in securing remote facilities, optimizing crops and deterring trespassers by using machine learning to identify employees who work onsite. An example of Twenty20 Solutions’ real-time monitoring is shown here:

How can artificial intelligence help the world?

6 Amazing Ways Artificial Intelligence Can Improve Agriculture And Help Fight Hunger In The World. The world is heading toward a huge hunger crisis. According to the United Nations, we will need to increase the world’s food production by 70% to feed the world’s population by 2050. Artificial intelligence (AI) systems have been deployed …

How can AI help farmers?

AI technology such as FarmView can help researchers figure out the right genetic makeup to create seeds that generate the highest yield, the most nutrition and the most disease resistant strains of staple crops . There are 40,000 varieties of sorghum, a valuable cereal crop in developing countries such as Ethiopia and India. AI can be used to experiment with these varieties to develop the perfect crop. All the growth, genetic and environmental data collected during research will be given to an AI model to process. AI algorithms are better able to review all the variables and varieties to identify patterns and insights faster than humans. Deep learning AI will be able to comprehend the complex genetics of plants that will support better breeding of plants. Those more efficient plants will improve our food production.

How many data points will IoT generate in 2050?

The growth of IoT devices should generate 4 million data points each day by 2050 according to one provider, OnFarm. All of this data about soil conditions to crop health and climate change will be powerful for deep learning AI and its ultimate application to make agriculture smarter and more efficient.

How is AI impacting our lives?

AI is already impacting our everyday lives—when Facebook recognises faces of friends to tag them, when Netflix suggests what you should watch next, and Alexa sets a timer per your request—but now AI is beginning to be used to solve the world’s biggest problems including the fight to end hunger in the world.

What is the purpose of the LettuceBot?

Instead of the “spray and pray” approach to herbicide application, the LettuceBot from Blue River is working to distinguish between a weed and a sprout of lettuce based on learning from more than a million images of 5,000 young plants. When it identifies a weed, it sprays it directly. This has cut losses by up to 90 percent.

How is deep learning used in agriculture?

Deep learning AI is being used to help machines identify the health of crops. Once the machine’s ability improves, farmers around the world will be able to leverage its learnings through an app that can diagnose an issue so the farmers can take action before the losses are catastrophic.

Why is AI better than humans?

AI algorithms are better able to review all the variables and varieties to identify patterns and insights faster than humans. Deep learning AI will be able to comprehend the complex genetics of plants that will support better breeding of plants. Those more efficient plants will improve our food production.

How is artificial intelligence changing our lives?

Artificial intelligence is changing many things in our lives, including the way our food is produced . Technologies like machine learning, image recognition, and predictive modeling are being applied in the agriculture industry as ways to boost productivity and efficiency. These approaches could be important steps in the effort to produce more food for a growing global population by helping farmers reduce chemical inputs, detect diseases sooner, buffer against labor shortages, and respond to weather conditions as the climate changes.

What is Plantix app?

A German company called PEAT has created Plantix, a mobile app that uses image recognition to detect plant diseases, pests, and soil deficiencies affecting plant health. Farmers and gardeners take a simple smartphone picture of their affected, and PEAT’s server identifies the pathogens or pests that are affecting the plants using deep learning built into image recognition software. Plantix then automatically recommends control options back to the user’s smartphone.

What is John Deere’s robot?

John Deere recently invested $305 million to acquire Blue River Technology, a seven-year-old tech company that developed a robot called “See and Spray.” It uses computer vision, robotics, and machine learning to precisely manage weeds. Instead of spraying an entire field, the system can find and spray only where the weeds are. The system is not only efficient in the sense that it is faster than humans, but it also reduces up to 90% of the volume of chemicals normally sprayed and helps reduce herbicide resistance, according to the company.

How to grow more food close to home?

Share land and resources in your community to grow more food close to home.

How much will AI grow in agriculture by 2025?

AI in agriculture expected to grow exponentially by 2025. According to the new research report on the “AI in Agriculture Market by Technology – Global Forecast to 2025”, the market is expected to grow by 22.5% to reach $2.6bn by 2025 from $518.7m in 2017.

What do we think about agriculture?

But although many of us might think that the agricultural community is behind the curve when it comes to implementing new technologies, there is lots of evidence that farmers are actually moving quite quickly to modernize almost everything about the farming process – they’re using artificial intelligence in new and amazing ways to bring the process of food cultivation into the future.

What are some examples of agricultural activities?

Individual agricultural activities on the farm take effort, for example planting, maintaining, and harvesting crops need money, energy, labour and resources. What if we can use technology to replace some of the human activities and guarantee efficiency? That’s where artificial intelligence comes in. Read more

Is agriculture digitized?

Agriculture, currently one of the world’s least digitised major industries, is expected to go through a transformation as data acquisition, agricultural robotics and analytic companies grow.

Can Artificial Intelligence help improve agricultural productivity?

When l reflected on the future of agriculture, l could not avoid thinking about the power of technology to solve problems bedevilling this sector. Climate change, population growth and food security concerns have pushed for innovative technological solutions to farming.

Leave a Comment