AI in Farming: Great 11 Practical Applications and Benefits

AI in Farming
  • AI in farming;
  • The roles of AI in agriculture production;
  • Practical applications of AI in agriculture;
  • AI tools in farming;
  • Challenges faced by farmers in AI adoption;
  • Farm Management System by JetRuby
  • Conclusion

When you finish reading this practical guide to the AI and ML trends in agriculture, your awareness of AI-enhanced farming solutions will hit fresh highs.

You already know that the mechanisms inspired by self-learning are taking the lead across all industries. This AI advancement significantly impacts agriculture in general and the farming industry.
By delegating the job to artificial intelligence, we make hard and high-volume work less labor-intensive. With more than 40 essential processes to be taken care of in the field, the robotization offered by AI and ML farming technologies become increasingly important.

How exactly can farmers benefit from AI and ML advancement in agriculture? Getting straight to the top benefits of using artificial intelligence in agriculture, we can see them from the perspective shaped by their effect.

AI in farming:

    • Prevent

Detecting infectious diseases among plants and animals based on machine learning has become one of the best practices in the industry. By matching historical data with satellite imagery of objects, the system will deliver detailed reports on the health conditions of on-farm livestock and plants to let you prevent the spread of infections early on. It prevents yield loss and farm animals and generates recommendations for better results. For example, an AI-based soil analysis goes beyond the standard PH test and spots soil deficiencies in seconds. Farmers run this analysis on a small device. They will adjust soil texture and maximize their production by taking it one step further.

    • Predict

Farmers can decide the best time for plowing, irrigation, and harvesting with historical data. These production decisions, for example, can initiate delayed or early irrigation of specific fields.

When moving to the harvesting season, the program informs you which fields will be ready to harvest first based on the field’s biomass. That’s how a crop management process benefits from the AI-enabled solution at all stages.

Producing more accurate weather forecasts is another important direction of AI-based predictive analysis.

    • Optimize

Agriculture production consumes more than 70% of the water on the earth. Finding and implementing solutions to this problem heavily relies on the potential opened up by using artificial intelligence in agriculture.
Overapplication of water will be significantly reduced if the data-driven mechanism is equipped with a water-holding capacity.

With linear programming, an optimal amount of water can be calculated for a given field to prompt necessary adjustments.

Moreover, reducing the wastage of ag resources and chemicals is one of the favored goals of AI applications.

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Practical applications of AI in agriculture

The AI-inspired opportunities for farming are countless, bringing tangible results to all farming enterprises. It’s a common perception that cutting-edge technologies are among the perks of big corporations. However, the format and accessibility of many AI devices make them a perfect fit for small family-run farms.

If you look for more advanced ways to achieve sustainable crop production, improve harvesting and sowing, map and monitor your fields, and identify diseases or pests on farms, consider giving AI technologies a chance.

Use Case of AI in Farming – Example I

One of Microsoft’s projects in India has launched an AI sowing app to recommend farmers on sowing dates, land preparation, soil test-based fertilization, farmyard manure application, seed treatment, optimum sowing depth, etc. This work has resulted in a 30% increase in average crop yield per hectare.

AI by market development

Use Case of AI in Farming – Example II

A seed production company in the United States. For some crops, cross-pollination is a huge issue. An AI-based module was used to ensure the genetic isolation of crop types and prevent cross-pollination. The potential sources of threats where volunteer crops grow were identified thanks to satellite imagery and assessment of the previous maps. This information became the clue for further crop isolation and rotation. As a result, seed production quality was improved with less time and effort than traditional methods.

Use Case of AI in Farming – Example III

A farmer cooperative in the United States. It was an email report generated by the cloud-based analytics platform that brought the soil stress alert to the notice of the company’s agronomists. The platform analyzes satellite-provided soil and weather data to provide scenarios for best crop performance practices. So the highlighted stress area was examined, and the outbreak of rootworm was found. The issue received immediate treatment, and the company saved crops.

AI tools in farming

Robots

Autonomous robots can be set for farming tasks round-the-clock. Instead of spending the whole day in the field in demanding conditions, farmers will dedicate their time and effort to achieving sustainability in their production.

What robots can do:

  • Robots spot weeds and treat these spots with herbicides. After drones have captured the photo records of weed locations, a self-driven robot filled with herbicides will check on them and start the treatment.
  • Going through the field, the robot finds pests. The next step will be to spray chemicals where needed without wasting them on the entire area.
  • Among robotic assistants, there’ll be another machine that sows and harvests wheat that a tractor won’t be able to pass.
  • The list of tasks for farm robots can’t be exhaustive, so it is to be continued.

Use case example:

Unlike traditional soil sampling, robotic sampling ensures 3 times more accurate data. Finally, it leads to a 10% increase in fertilizer effectiveness. At the same time, poor sampling is worse than having no sample analysis, so reliance on accurate metrics is vital. With inch-level precision, an automated system captures the whole core sample of soil. The analysis results allow for saving the input of nutrients in areas that need less, thereby achieving better profitability per acre.

Universality

Delivering a customizable and adaptable robot that can be set for a particular task is among the latest agriculture trends. Fill it with necessary implements and release it for various goals: seeding, spraying, weeding, etc. The production standard for this type of robot implies advanced safety (leave it on the field and monitor its performance with the help of surveillance cameras), low weight, and RTK/GPS guidance.

AI-powered mobile and web apps

By all accounts, smartphone and web applications are the primary sources of first-hand AI experience for farmers of all types. Apps’ assistance is indispensable in many routines, such as weather tracking and predictions, keeping up with market prices, treating sick plants and animals, field mapping, searching, and adjusting crop performance. Apps alert growers on the outbreak of diseases and supply them with recommendations relevant to the present moment.

Use case example:

The irrigation tool is in your hands. By monitoring sensors and pumps, crop growers can remotely monitor irrigation equipment. Starting, pausing, or stopping irrigation with your smartphone will reshape your irrigation experience.

Drones

AI-based drones have proven their mastery in agriculture by assisting with pollination, cultivation, examination of plants, and various other tasks. It’s been a while since drones became a standard part of the daily on-farm routine for all scales of ag enterprises. Drones can be customized with farm software and used in numerous ways:

  • See pasture weeds, count cattle, measure fields, and track changes in cattle weight by following a preprogramming route.
  • Check the hay feeder to know whether it should be filled or not
  • Take aerial videos of fields and ranches
  • Check on livestock, ponds, and feeders. Drones encourage an inventive approach: a farmer blows a whistle on the drone to make cows think it is their flying herd dog.

Use case examples:

Apple-picking: every harvester knows that fruit can remain unpicked. On a global scale, this waste goes up to 10% every year. But there is an easy solution. One of the latest developments, the apple-picking AI-enhanced system, is as helpful in finding which apples can be picked as in picking them.

Removing clogs from pipes: your pair of eyes inside the tile is the underground device equipped with front and rear cameras. Putting a lightweight and waterproof drone underground to the tile will make the clog location known to you and let you decide on the tile repair.

Intelligent spraying

The unmanned spraying process saves operator costs and cuts downtime. One of the most illustrative applications of this system is the irrigation of vineyards, orchards, and berries. All steps are powered by a laptop, where the program prompts spraying of the specific tree and spending an exact amount of material needed.

Spraying a preset amount of herbicides and pesticides is another popular application of this method in agriculture.

Self-driving technology

The auto-guidance mechanisms for field tractors and harvesters are among the cutting-edge and truly disruptive AI technologies, boosting the farmers’ productivity. Equipped with the GNSS and RTK navigation, they would move along working paths on fields that can be flat or have slopes. Precision agriculture cannot be imagined without auto-guidance kits for agriculture machinery. The relevant software can be integrated into carriers, such as agriculture trucks and tractors.

Reduce fatigue with driverless tractors

With self-driving vehicles, machine operators do not suffer from fatigue as much as when paving paths on paddocks and have to stick to them. In other words, most of the time, the drivers had to concentrate on navigation, which increased the probability and cost of mistakes. While the auto-guidance takes care of the path and navigation, a farmer can look around and sow seeds.

Use case example:

Soil compaction has been long-known trouble for agriculture. It heavily affects plant health due to the deterioration of biological activity. Self-driving tractors optimize routes and maintain soil quality by not letting soil compaction go too far. Additionally, tractors work out the routes to make them more passable for machinery during wet weather.

Modules and programs backed by AI

Combining several AI-enhanced methodologies can map a workflow for the entire project. The possibilities of complex solutions can be illustrated by how the monitoring was once arranged for crop performance on distance. A group of fields and paddocks was spanned across different countries. The monitoring methods included crop modeling, advising on data-based decisions, and using remote sensor data. The module started with mapping fields, using satellite imagery data and machine learning to understand it.

The farmers could navigate through the growth stages of a crop. Throughout a growing season, they were notified if any areas needed more attention as problematic — the early assessment of field data by soil samples detected stress areas. If the decisions made for more minor spots turned out to be efficient, they were extrapolated to wider areas. These decisions resulted in increased productivity of crops across several fields.

Challenges Faced by Farmers on the Way to AI Adoption

1) Government policies and adoption of smallholders to new technologies

In many countries, the agriculture market is dominated by smallholders, who see many financial, educational, and infrastructure obstacles on their way to AI. Modernization requires upskilling and investments to switch to new technologies. Creating affordable financial and subsidy schemes for farmers must be on the agenda of the state and world community policies.

2) Agriculture offers no universal methodology or simple solution based on AI

The peculiarity mentioned above is instead a characteristic than a problem by itself, but it may affect the choice of AI as a working method compared to others. Learning from success stories and staying up-to-date with industry reports is essential.

3) Improving the precision of machine learning techniques

Machine learning is intrinsically connected to the evolution principle. However, almost every ML task sets its own individual goals, so each time, it is about training the system and walking through all steps of the training process. It means that the precision level is always achieved for a particular goal. For example, the image recognition level must be above 90% to qualify for commercial success.

4) Cost of mistakes

Like the previous challenge, this hindrance deals with the impossibility of a 100% accuracy level for some predictions, engaging the AI methodology. A wrong recommendation can destroy business results. That’s why it became a common practice to try those recommendations on a smaller scale first.

Farm Management System by JetRuby

Among the opportunities and challenges of the AI world in agriculture, expert guidance is the backbone of a sustainable project. With more than 250 successful app releases in its portfolio and proven agritech expertise, JetRuby embraces a wide range of industry needs, including but not limited to grain cultivation, aquaculture, viticulture, husbandry, and farming.

The Farm Management System (FMS) is a professional response to the current AI challenges caused by the lack of centralized data sources and universal methodology. FMS harnesses the power of AI and ML to help businesses generate accurate forecasts and planning, take care of farm animals and plants, and increase crop yield by optimizing water, fertilizer & soil management. FMS is a customizable platform with expanded possibilities to design the system catering to your particular needs as a farmer.

A global high-tech corporation Osram is one of the companies using JetRuby’s development — an urban farming solution based on IoT technology. A universal dashboard was designed to let farmers digitally control the parameters of their plant growth and increase their crop yield by 32%.

Bottom line

According to estimates, U.S. farms alone can add up to $65 billion worth’s value to the national economy by implementing AI-driven solutions. These predictions demonstrate that farmers are willing to explore the opportunities of AI & ML and find these goals viable. Therefore, the agricultural sector is doubling down on its AI approach, looking forward to its automation potential and cost-efficiency.

The overview you’ve just read outlines major applications, practical benefits, and challenges of AI adoption in agriculture. Without a doubt, you share our conviction that artificial intelligence is all about giving you resourceful, innovative, and often affordable tools for driving the business value of farming.

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