How ai is changing agriculture


Artificial Intelligence in agriculture is transforming the way of food production. Moreover, AI technology helps control and manage any unusual natural condition. Currently, many startups in agriculture are adapting AI solutions to enhance the efficiency of agricultural production.

AI systems are helping to improve the overall harvest quality and accuracy – known as precision agriculture. AI technology helps in detecting disease in plants, pests and poor nutrition of farms. AI sensors can detect and target weeds and then decide which herbicide to apply within the region.


How AI is transforming the agriculture industry?

Worldwide, agriculture is a $5 trillion industry, and now the industry is turning to AI technologies to help yield healthier crops, control pests, monitor soil and growing conditions, organize data for farmers, help with workload, and improve a wide range of agriculture-related tasks in the entire food supply chain.

How can Ai be leveraged by farmers?

While it depends upon the specific application that is being utilized, AI can generally be leveraged by farmers in the following ways: AI can be used to make predictions, and one of the most practical applications of this capability is in predicting crop yields.

What is the forecast period of AI market in agriculture?

The AI Market in Agriculture market is studied from 2018 – 2026. What is the growth rate of AI Market in Agriculture? The AI Market in Agriculture is growing at a CAGR of 21.52% over the next 5 years. Which region has highest growth rate in AI Market in Agriculture?

How can AI help with plant growth?

By collecting data on plant growth, AI can help produce crops that are less prone to disease and better adapted to weather conditions. AI systems can conduct chemical soil analyses and provide accurate estimates of missing nutrients.


How do bots help farmers?

These bots can harvest crops at a higher volume and faster pace than human laborers, more accurately identify and eliminate weeds, and reduce costs for farms by having a round the clock labor force.

Why is seasonal forecasting important?

Seasonal forecasting is particularly valuable for small farms in developing countries as their data and knowledge can be limited. Keeping these small farms operational and growing bountiful yields is important as these small farms produce 70% of the world’s crops.

Why do farmers need workers?

Traditionally farms have needed many workers, mostly seasonal, to harvest crops and keep farms productive. However, as we have moved away from being an agrarian society with large quantities of people living on farms to now large quantities of people living in cities less people are able and willing to tend to the land.

What are chatbots used for?

Chatbots help answer a variety of questions and provide advice and recommendations on specific farm problems. Chatbots are already being used in numerous other industries with great success.

What can farmers analyze in real time?

With the help of AI, farmers can now analyze a variety of things in real time such as weather conditions, temperature, water usage or soil conditions collected from their farm to better inform their decisions.

How does AI help in agriculture?

Precision agriculture uses AI technology to aid in detecting diseases in plants, pests, and poor plant nutrition on farms. AI sensors can detect and target weeds and then decide which herbicides to apply within the right buffer zone.

What is the oldest profession in the world?

Agriculture and farming is one of the oldest and most important professions in the world. Humanity has come a long way over the millennia in how we farm and grow crops with the introduction of various technologies. As the world population continues to grow and land becomes more scarce, people have needed to get creative …


The most significant way ML is impacting crop management is through yield estimation and evaluation. Computer vision techniques can be applied to crops to evaluate which are ready to harvest, allowing farmers to better plan their activities.


We’re all familiar with facial recognition, with many of us using it multiple times a day to unlock our phones. Now, we’re beginning to turn this technology on a new face, that of the cow. Bovine facial recognition is being used to identify and track individuals within a herd without the need for radio frequency identification (RFID) tags.

Water and soil

Understanding precipitation and evapotranspiration (the process of water transferring from the land and plants into the atmosphere) is essential to resourceful agricultural management. The processes involved are complex, with temperature, relative humidity, solar radiation, and wind speed all playing a role.

How to get started with ML in agriculture

The wealth and complexity of the data that agriculture offers makes it a fascinating industry for ML. Not only that, but agriculture’s expected increase in demand means that the time is ripe to harvest that data and turn it towards transforming agriculture into a more efficient, less labor-intensive, and more profitable industry.

How does AI help farmers?

Farmers use AI for methods such as precision agriculture; they can monitor crop moisture, soil composition, and temperature in growing areas, enabling farmers to increase their yields by learning how to take care of their crops and determine the ideal amount of water or fertilizer to use.

How does AI work?

AI works by processing large quantities of data, interpreting patterns in that data, and then translating these interpretations into actions that resemble those of a human being. Scientists have used it to develop self-driving cars and chess-playing computers, but the technology has expanded into another domain: agriculture. AI has the potential to spur more efficient methods of farming in order to combat global warming, but only with expanded regulation of its development.

How many farmers in China could lose their jobs due to automation?

This program does not bode well for the future of jobs in agriculture: many of China’s 250 million farmers could lose their jobs due to increased automation.

What will happen to the sea level in 2100?

The result is growing food insecurity. And rising sea levels only compound the problem. By the year 2100, sea levels are expected to rise by one meter, which will have a detrimental impact on growers on the coasts whose crops cannot survive in areas where the water is too salty. However, agriculture is not just a victim of global warming, …

Is AI expensive?

Additionally, AI can be expensive. Farmers might go into debt and will not be able to maintain the technology on their own as it suffers everyday wear-and-tear. Those unable to secure access to the technology will lose out to larger farms that can implement AI on a wide scale.

Can AI find weeds?

Furthermore, some AI is being developed that can find and target weeds in a field with the appropriate amount of herbicide, eliminating the need for farmers to spread chemicals across entire fields and pollute the surrounding ecosystem. Some countries are already implementing AI into their agricultural methods.

Advantages of AI implementation in Agriculture

The use of the AI approach in agriculture assists farmers to comprehend valuable data insights like temperature, wind speed, solar radiation, and precipitation. The best thing about the implementation of AI in agriculture is that it does not involve the risk of eliminating the jobs of farmers rather it enhances their processes.

Agricultural Robotics

AI businesses are creating robots that can perform several tasks easily in farming fields. Agricultural robots are trained not only to control weeds but also to harvest crops at a faster speed and with greater volumes compared to humans.

AI system to detect pests

Pests are bad enemies of farmers which damage crops. AI solutions employ satellite images and then compare them with previous data using AI algorithms. That’s how they detect if any insect landed like a locust, grasshopper, etc. AI systems then send alerts to farmers so that the farmers can take precautions and use pest control measures.

AI helps analyze farm data

Farms produce many thousands of data points every day. AI helps farmers analyze several things in real-time like weather conditions, soil usage, and temperature so that the farmers can make better decisions.

Precision Agriculture

AI systems also help farmers to better harvest quality and even improve accuracy. Precision agriculture employs an AI approach to help detect pests and diseases in plants. AI sensors easily detect and target weeds to decide which herbicides should be applied within the right buffer zone.

Autonomous Tractors

There is a huge investment in developing autonomous tractors for several needs. The agricultural sector will get a lot of benefits from these driverless tractors. With AI technology and machine learning data for agriculture, the agricultural sector will be revolutionized by the use of autonomous tractors.

Crops Health Monitoring

Continuous deforestation and deterioration of soil quality are a big challenge for countries producing food on a large scale.

What are some examples of farmers getting their work done without hiring more people?

Driverless tractors, smart irrigation and fertilizing systems, smart spraying, and AI-based robots for harvesting are some examples of how farmers can get the work done without having to hire more people. Compared with any human farm worker, AI-driven tools are faster, hardier, and more accurate.

What is precision farming?

One particular farm management approach — precision agriculture — can help farmers grow more crops with fewer resources. Precision agriculture powered by AI could become the next big thing in farming. Precision farming combines the best soil management practices, variable rate technology, and the most effective data management practices to help farmers maximize yields and minimize spending.

How can farmers use IoT?

Farmers can use IoT sensors and other supporting technology (e.g. drones, GIS, and other tools) to monitor, measure, and store data from fields on a variety of metrics in real time. By combining AI farming tools with IoT devices and software, farmers can get more accurate information faster.

What is the key to precision agriculture?

Taken together, AI, autonomous tractors, and IoT are the key to precision agriculture. Another less common but rapidly growing technology is robotics. Agricultural robots are already being used for manual work, such as picking fruits and vegetables and thinning lettuce.

How does AI help farmers?

AI can provide farmers with real-time insights from their fields, allowing them to identify areas that need irrigation, fertilization, or pesticide treatment. Also, innovative farming practices like vertical agriculture may help increase food production while minimizing the use of resources.

What is predictive analytics?

Predictive analytics can be a real game-changer. Farmers can collect and process significantly more data and do it faster with AI than they would otherwise. Analyzing market demand, forecasting prices, and determining the optimal time for sowing and harvesting are key challenges farmers can solve with AI.

Why do farmers resist AI?

This is not because they’re conservative or wary of the unknown. Their resistance is caused by a lack of understanding of the practical application of AI tools.

Insect and plant disease detection

We’ve seen how AI computer vision can detect and analyze crop maturity and soil quality, but what about agricultural conditions that are less predictable?

Livestock health monitoring

So far we’ve focused mainly on plants, but there’s more to agriculture than wheat, tomatoes, and apples.

Intelligent spraying

We’ve seen that computer vision is good at spotting disorders in agriculture, but it can also help with preventing them.

Automatic weeding

Intelligent sprayers aren’t the only AI getting into weed… er, weeding. There are other computer vision robots taking an even more direct approach to eliminating unwanted plants.

Aerial survey and imaging

At this point it’s probably unsurprising that computer vision also has some terrific applications for surveying land and keeping an eye on crops and livestock.

Produce grading and sorting

Finally, AI computer vision can continue to help farmers even once the crops have been harvested.

The future of AI in Agriculture: Farmers as AI engineers?

Throughout human history, technology has long been used in agriculture to improve efficiency and reduce the amount of intensive human labor involved in farming. From improved plows to irrigation, tractors to modern AI, it’s an evolution that humans and agriculture have undergone since the invention of farming.

Why is AI important in agriculture?

Because of the rise of data in agricultural processes, AI can operate with the information it needs to inform better decision-making practices across the board. This is possible through processes unique to AI, such as machine learning. With machine learning, a system can adapt its processes without even being explicitly programmed to do so simply by learning from new data sets.

How is IoT used in agriculture?

IoT is being adopted at high rates across the agricultural industry, growing by 20% annually . These devices give farmers the power to automate and control their work with unprecedented insights, all generated from data-collecting sensors. The implications of this control can seep into every step of the farm-to-table process, streamlining food growth and supply chains.

What is XAG drone?

XAG is driving AI-powered intelligent devices such as drones and sensors to establish digital farming infrastructure in rural areas and enable precision agriculture which, for example, accurately target pesticides, seeds, fertilizers and water to wherever it is needed.

What is the AI market?

Market Overview. The Artificial Intelligence (AI) Market in Agriculture was valued at USD 766.41 million in 2020 and is expected to reach USD 2468.02 million by 2026, at a CAGR of 21.52% over the forecast period 2021 – 2026.

What is ESDAC in Europe?

The European Soil Data Centre (ESDAC) is the thematic centre for soil related data in Europe, where its ambition is to be the single reference point for and to host all relevant soil data and information at European level.

What is the market for artificial intelligence in agriculture?

The artificial intelligence (AI) market in agriculture is driven by the increasing adoption of robots, in agriculture. The increasing consumption and rising requirement of better yields of crops are fueling the demand for robots, in agriculture. Precision farming is in demand as around 70-80% of the new equipment purchases have been deemed to contain some form of precision farming tools along with the demand of smart green applications.

What is the Internet of the Soil?

AI firms are managing ‘Internet of the Soil’, which is a software and hardware solution for monitoring soil conditions like humidity, temperature, electrical conductivity, and more in European countries. Their sensors connect wirelessly to a cloud-based platform where it can be accessed by any internet-connected device.

What is machine learning algorithm?

Machine learning, in particular, deep learning algorithms, take decades of field data to analyze crops performance in various climates and based on this data one can build a probability model that would predict which genes will most likely contribute a beneficial trait to a plant.

What is species selection?

Species selection is a tedious process of searching for specific genes that determine the effectiveness of water and nutrients use, adaptation to climate change, disease resistance, as well as nutrients content or a better taste.


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