AI in Agriculture

Do you imagine an industry that is riskier than agriculture? You reap what you sow, they say. But what we forget to add in the above sentence is  “Only if you’re lucky.” When the climate strikes or crops are attacked by the disease, farmers have to forget about yields. Or while global pandemic runs, it gets even more difficult to manage and run multiple processes because most are not digital.

Global urbanization is continuing and the population is growing at the same time. Twenty years from now, the number of people to feed will be increased. And because the amount of fertile soil is limited, there will also be a requirement to move beyond traditional farming. Above all, we will need to find ways to make farmers more profitable, then only the next generation will even think of farming as their main occupation.

AI can possibly change the way we perceive agriculture, empowering farmers to gain more results with less effort including many other benefits. However, AI can be combined with technologies which are already in existence allowing to convert traditional farming into innovative farming.

How AI can be useful in agriculture

Agriculture includes a number of stages with multiple processes and the crucial ones are still manual. By complementing already used farming technologies, AI can help in complex but routine tasks. It can collect and process big data then can come up with the best plan of action, and also can start that action when merged with other technologies. 

Combining AI and agriculture can be profitable in following ways:

Analyzing market demand

AI can easily help in sorting and selection. It can help farmers to know which crops will be most beneficial.

Managing risk

Farmers can utilize forecasting and predictive analytics to decrease errors in overall processes of the farming and business side of things to minimize the risk of crop failures.

Breeding seeds

Plant breeding uses principles from a variety of sciences to improve the genetic potential of plants. The process involves combining parental plants to obtain the next generation with the best characteristics. Breeders improve plants by selecting those with the greatest potential based on performance data, pedigree, and more sophisticated genetic information. Plants are improved for food, fiber, fuel, shelter, landscaping, ecosystems services, and a variety of other human activities.

By gathering data on plant growth, AI helps to produce crops that are less likely to catch diseases and better suited to weather conditions.

Monitoring soil health

AI systems can manage chemical soil analyses and return accurate estimates of lacking nutrients.

A German-based tech start-up PEAT has developed an AI-based application called Plantix that can identify the nutrient deficiencies in soil including plant pests and diseases by which farmers can also get an idea on which fertilizer to use which helps them to improve harvest quality. This app uses image recognition-based technologies. The farmer can capture images of plants using smartphones. We can also see soil restoration techniques with tips and other solutions through short videos on this application.

Protecting crops

AI can observe the state of plants to spot and predict diseases, identify and remove weeds, and suggest effective treatment of pests.

Nutriate crops

AI is useful for classifying optimal irrigation patterns and nutrient application times and predicting the optimal mixture of agronomic products.


Automating harvesting and even predicting the best time for it is possible using AI.

How is AI beneficial for agriculture?

  • AI enables better decision-making

Predictive analytics can play a major role to automate agriculture. Farmers can gather and process significantly more data and do it faster with AI than traditional methods. Forecasting prices,  analyzing market demand, and determining the optimal time for sowing and harvesting are major challenges farmers can resolve with AI.

Having said that, AI can also collect soil health insights, give fertilizer recommendations, monitor the climate, and track the readiness of produce. All of these allow farmers to make more informed decisions at every stage of the crop cultivation cycle.

  • AI brings cost savings

One specific farm management approach(precision agriculture) can enable farmers to get more yields with fewer resources. Precision farming merges the best soil management methods, mutable rate technology, and the most effective data management systems to help farmers maximize yields and minimize expenses.

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 to enhance food production while minimizing the use of resources. The result is reduced usage and better harvest quality, higher profits, herbicides,  and significant cost savings.

  • AI addresses labor shortages

Agriculture is difficult, and labor shortages are the most common problems. Farmers can solve this issue with the help of automation. Smart irrigation and fertilizing systems, smart spraying, driverless tractors, and AI-based robots for harvesting are few examples of how farmers can get the job done without hiring more people. AI-driven tools are faster and more accurate compared with any human farm worker.

Challenges farmers may face implementing AI

  • The lengthy technology deployment process
  • Absence of experience with emerging technologies
  • Privacy and security issues
  • Price to adopt this newly emerging technology

How AI should be combined with other technologies

  • Big data for informed decision-making

The real purpose of producing and collecting data is to put it into use. In farming, data analytics may lead to extensive productivity increases and significant cost savings. By combining AI with big data, farmers can get proper recommendations based on well-sorted real-time information on crop needs. This will take away the guesswork and facilitate more precise farming practices such as fertilizing, crop protection, irrigation, and harvesting.

  • IoT sensors for capturing and analyzing data

Farmers may utilize IoT sensors and other supporting technology (e.g. drones, GIS (Geographical Information System), and other tools) to monitor, project, and store data from fields on a variety of metrics in real-time. By combining AI farming equipment with IoT devices and software, farmers can generate more accurate information quicker. Better data means better decisions and less time and money spent on trial and error.

  • Automation and robotics for minimizing manual work

AI combined with autonomous tractors and IoT can fix one of the most common problems in farming: a shortage of labor. These technologies are also conceivably cost-effective because they’re more accurate and thus reduce errors. Brought together, AI, IoT, and autonomous vehicles are the key to precision agriculture.

Another less popular but rapidly developing technology is robotics. Agricultural robots are already in use for manual work, such as picking fruits and vegetables and thinning lettuce. Here is a link for a more resource video describing automated robots and their work. 


AI in agriculture not only supports farmers to automate farming but also moves to precise improvement for higher crop yield and better quality using fewer resources.

Companies concerned to improvise Artificial Intelligence-based products and services like training data for agriculture and automated machine making will get technological advancement in the future and will provide more useful applications to this sector helping the world deal with food production issues for the growing population.

Please put your suggestions, questions, and queries below. To know more things about the technologies or if you want to build some product around it then contact us for the consultation.

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