Modern farming practices are facing a diverse scope of obstacles that call for seeking innovative solutions and paving the path forward. AI use in agriculture offers a broad scope of opportunities to enhance every stage of the farming process, from seed germination and crop integrity to the harvesting practices. Its potential is already highly promising: at the end of 2021, almost 89% of U.S. farming enterprises used AI technologies.
And that’s just the beginning. The value of AI in the agricultural sector is predicted to grow to approximately $4.7 billion by 2028. This growing popularity is easily explainable, too. It revolutionizes agriculture and makes farming much more sustainable and effective across all characteristics. AI farming is useful in analyzing vast amounts of data, predicting crop diseases, optimizing irrigation, and automating tasks.
Read on to learn more about how applications of AI in agriculture can help your farming business move forward and flourish.
How can agricultural companies benefit from AI?
Covering all the benefits of AI in agriculture might be impossible: the list is constantly expanding. Farmers can profit from applications of artificial intelligence in agriculture and improve their operations. So, what are the advantages of AI in agriculture?
Improved productivity
AI applications in agriculture solve many issues without overapplying human labor on exhaustive tasks. Automation, decrease in human labor, and the ability to reach faster and profitable solutions foster planting and harvesting, bringing in new profits for the farmers.
Cutting back on costs
Aside from initial investments of implementing AI in agriculture, expenditures will generally become much lower than before. It stems from much better revenue generation and a falling need for costly strategies, from over-hiring to investing in diverse fertilizers and tools. World Metrics mentions that labor costs can fall up to 70% due to AI advancements in agriculture. However, other factors, such as decreasing spending on outdated technologies, also deserve recognition.
Resource management
When we discuss the question, “How can artificial intelligence be used in agriculture?” its ability to handle resources with care often comes to mind first. AI can analyze the quality of the external environment and the state of plants or farm animals at any given moment.
That’s why farmers can seek AI solutions for agriculture to spend just the exact amount of water, land, and pesticides to get healthy produce. For example, some claim that the future of AI in agriculture has the potential to cut water use by half.
Promoting sustainable farming
Numerous factors, from crop and soil monitoring using AI to analyzing general environmental metrics, keep farmers and companies informed about their impact and help them minimize their environmental footprint. Due to a more harmonic link between farming and the environment, agricultural businesses can preserve limited natural resources and promote a more balanced and enduring future.
Data-based decisions and predictions
Instead of overinvesting resources in complex outdated analytical methods, farmers can get regular and accurate updates with AI. By obtaining and analyzing data from such sources as soil and water sensors, weather stations, and satellite imagery, they make more informed decisions about their operations.
Supply chain optimization
Due to its effective resource management and analysis, AI fosters efficiency of the agricultural product supply chain. By monitoring and streamlining the movement of goods from the field to the market, AI ensures products remain fresh and spoilage is minimized.
Industry prestige
Today, the number of employees in farming has been rapidly falling due to low interest in this field. AI technology in agriculture can attract new talents to companies and make them pioneers in a new era of agriculture. As the agricultural sector modernizes, it becomes more appealing to tech-savvy professionals and recent graduates drawn to industries that leverage advanced technologies. New specialists eager to work on new projects and find technologically-driven improvements will further contribute to the industry reputation.
Use cases of AI in agriculture
If you’re wondering how AI can be used in agriculture and farming, we’ve compiled this detailed list to explain diverse forms in which the link between AI and agriculture can function. This range of AI-driven tools and applications can inspire farmers to develop their own groundbreaking solutions.
Crop and soil monitoring and analysis
Numerous agriculture AI startups have recognized the value of crop and soil monitoring using cutting-edge technologies. The usage of AI-powered sensors and cameras contributes to data-driven agriculture. This technology creates real-life information on soil and crop status during any time of the day. It can inform farmers about soil moisture and temperature, nutrients, minerals, and the health of the crops. It can promote the reduction of resource waste and tap into the problem-solving process right when the situation requires it.
Farmers Edge is an excellent example of AI in agriculture, specifically targeting agronomic support for its clients. The company’s variable rate technology provides a customized fertility plant for land and covers zone mapping, soil testing, and long-term support with prescription instructions. It also offers nutrient management solutions covering such details as composite soil testing and nutrient management in every land element.
Pest and weeds control
You can implement AI in your farming operations to predict outbreaks, detect threats, and quickly protect the crops. Instead of relying on personal observations that can easily be missed in vast territories under few people’s management, AI-driven systems can do it faster and better. Accurate evaluations can identify pests and weeds before their significant expansion and alert workers. Pests and weeds are responsible for significant crop losses (38% and 34% respectively). By adopting AI pest control, farmers can greatly improve their production outcomes.
Carbon Robotics, a company offering AI-powered LaserWeeder, has brought an innovative solution to the industry. A creative example of how AI is used in agriculture, it utilizes high-resolution cameras to find and identify weeds or crops. Once it finds the former, LaserWeeder destroys them with laser beams. Its key goal is to reduce the usage of herbicides in farming, bringing its cutting-edge AI crop monitoring to a new level.
John Deere’s See & Spray technology fights weeds by detecting and selectively targeting weeds, applying herbicides only where needed. It decreases time expenditures through its two-pass approach, using a dual-tank method to use targeted spray and broadcast simultaneously. It decreases the crop’s exposure to chemicals and proposes more effective and long-lasting improvements.
Intelligent herbicide and pesticide application
How is artificial intelligence used in agriculture to target large areas? AI-powered drones enter the picture. With the assistance of advanced sensors, such drones locate areas affected by pests to target them specifically. Previously, farmers would cover an entire field in pesticides to prevent a severe pest outbreak. Nowadays, however, smart use of herbicides and pesticides allows agriculture specialists to avoid toxic compounds and improve crop’s natural growth.
DJI Agriculture provides drones that collect data on crop health and detect pest infestations. Then, this information is processed by its AI algorithms to determine the exact areas that need to be targeted. DJI Agriculture, with its innovative spraying technology, ensures optimal pesticide and herbicide use, improving crop yields while minimizing chemical use.
Smart irrigation systems for water conservation
AI in farming drives adequate water usage, recognizing that this limited resource needs a measured application. Crops receive the right amount of water with smart irrigation systems that remain in touch with data sources derived from monitoring soil moisture levels and other weather conditions impacting water retention. Within the next two decades, water scarcity will become a central challenge in over 80% of croplands worldwide. Smart irrigation systems offer a rational response and solution to this disturbing trend.
WiseConn’s example as a creator of an irrigation automation system proves that an AI irrigation system can expand its market proposition and substantially grow for almost 20 years. Its system uses a wireless network that analyzes the crop state. After analysis, it develops and adjusts the watering schedule that responds to the needs of every plant perfectly. Supporting all-device access, this company helps farmers control their crops distantly and can propose changes if necessary.
Monitoring livestock health
Modern use cases for AI in agriculture prioritize livestock well-being and rarely treat them as expendable resources; now, most companies use tech to maintain animal wellness. They use AI tools for agriculture, including the newest sensors and computer vision, to analyze the movement and health of animals. Global educational institutions receive grants and support to invest in this field of AI for agriculture due to its high-promising opportunities.
CattleEye is a relatively new company specializing in AI in livestock management. Proclaiming its goal to create the first fully autonomous cattle monitoring system, it has invented a deep learning video analytics method. Its AI algorithms process photo and video images to detect any disturbing signs in animals.
Smartbow combines AI and IoT in its eartag device that delivers real-time rumination monitoring, heat detection, and accurate localization. Combining the knowledge of a cow’s biology and behavior data, this company promises high levels trustworthiness by making accurate updates on the health and well being of the livestock possible.
Read also: IoT in Agriculture: 8 Technology Use Cases for Smart Farming (and Challenges to Consider)
Yield mapping and predictive analytics
Yield mapping and predictive analytics are examples of artificial intelligence in agriculture that employ diverse sources to analyze the data. Farmers now gain insights from satellite images and sensors that create detailed maps of crops. Informed and detailed analytics of crop growth amplify AI prediction for future yields.
For instance, Climate FieldView has pushed the relationship between artificial intelligence and agriculture to an entirely new extent. It collects and stores data with its FieldView tech, visualizes it, and proposes fast seamless interventions with impressive data connectivity.
Use of weather forecasting
AI weather forecasting holds a central place in supporting healthy crop growth. Due to climate change, the need for AI forecasting has grown pressing. A temperature increase of just 1°C could cause wheat and rice production to decrease by 6% and 3.2%, respectively. How is AI used in agriculture to address it? Exploring a broad scope of data to collect information about the current weather, it can predict future weather patterns.
The Watson Decision Platform for Agriculture from IBM taps into this possibility. It creates an Electronic Field Record (EFR) and fills it with soil, weather, equipment, and workflow information to make smarter predictions. The company’s AI uses in agriculture bring data and reduced-risk operations together.
Supply chain optimization and demand forecasting
Cutting the costs of the supply chain often remains a focal point for AI software development. Faster travel solutions make it easier to connect produce with buyers, but poor management can adversely contribute to the environment and expenditures. In 2022, the global agricultural supply management market size reached $358.6 million. Artificial intelligence use in agriculture for supply chain predicts market trends, inventory, and even travel management.
AgriDigital offers customizable propositions with its trendsetting relationship between blockchain and AI in the farming industry. Assisting farmers with digitizing their grain management, this brand gives rapid live updates on the major processes. It balances inventory management and product delivery with contract monitoring and payment processing.
Genetic optimization
The use of AI in agriculture has moved far beyond superficial analytics; today, gene editing drives the search for high-performing crops with the best quality. Nowadays, companies using AI in agriculture can locate genetic traits to improve crop yield, damage resistance, and environmental impact. Such decisions emerged as a response to the global need to grow more crops in the same or even smaller fields.
SEEDesign by Inari Agriculture greatly expands on the role of AI in agriculture by combining predictive design and applying complex gene editing. SEEDesign detects optimal crop traits to modify them similarly to traditional breeding but with better accuracy.
Challenges to consider when implementing AI in agriculture
Before finally committing to shifting to AI in agriculture and farming, you need to account for the possible roadblocks. Recognizing the challenges of AI in agriculture will ensure a better alignment of one’s goals with crop management and risk analysis.
Cost and investment
Upfront costs of integrating artificial intelligence in agriculture can be overwhelmingly high for small farms. To develop AI software and maintain it, you might need to face the creeping costs. The returns on it can be relatively delayed due to market price fluctuations and seasonality, complicating the justification of such expenditures. Asking yourself how to use AI in agriculture should go in tandem with starting small and being flexible to scale up later.
Infrastructural barriers
The potential of advanced agriculture depends on technological accessibility and regional infrastructure. Recent statistics highlight that farms in far rural areas struggle with poor Internet access and lack of services ready to provide constant maintenance. For example, only 73% of U.S. citizens in rural homes typically have access to high-speed Internet, compared to 86% in suburban and 77% in urban areas.
It leads to a greater dependency on smartphone Internet access, making up to 15% of them being smartphone-reliant. In Europe, the disparity is even more evident: only half of Europeans in rural households have Internet access, unlike 81% of Europeans living in cities and towns.
Such a digital divide can deepen the tech access among various farmer areas, putting some of them at a disadvantage. In this case, farmers might need to add infrastructural costs that will be profitable long-term, though not immediately. A sequential growth process might be most suitable for small and medium-sized farms.
Technical expertise
Any rapid adaptation calls for staff training, and an effective introduction of novel technologies demands knowledge of AI algorithms, data analytics, and operation of the advanced machinery. Because many workers might not even understand how AI works in agriculture, it requires gradual adaptation. Costs of training vary vastly based on the type of education and operating tools. Failure of training can lead to errors and equipment malfunction.
Employee resistance
Rapid change can trigger fear and distress. Some workers might ask you, “What is the use of AI in agriculture, and why can’t I do better?” Emotional pressure of learning, especially during the initial months, can be detrimental to performance and employee morale.
Farmers need to prepare for handling demotivation and leveraging incentives to encourage their employees. Many farmers remain resistant to new implementations because they are afraid of losing their jobs. You can minimize such fears by emphasizing how artificial intelligence can be used in agriculture to benefit your business and your customers.
Final thoughts
What is the future of AI in farming? We can hypothesize. Global changes require multi-level adjustments, and agriculture can be an excellent example for other industries. AI can bridge the gap between modernity and agriculture and connect sustainability to progress. It will drive your farming to a whole new level and optimize your resources, maximizing your operational and productive potential.
Even more so, the impact of AI in agriculture goes beyond purely transactional factors: more and more clients are demanding modern AI-powered solutions. Companies that hope to maintain a competitive edge should consider using AI in farming, or they might fall behind.
Are you considering new agricultural solutions but not sure where to kick off? Our skilled team has extensive experience in creating customized AI propositions in this industry. Contact us to develop a smart and unique proposition for your needs.
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About the author:
Alexey Shalimov, CEO at Eastern Peak
As CEO at Eastern Peak, a professional software consulting and development company, Alexey ensures top quality and cost-effective services to clients from all over the world. Alexey is also a founder and technology evangelist at several technology companies. Previously, as a CEO of the Gett (GetTaxi) technology company, Alexey was in charge of developing the revolutionary Gett service from ground up and deploying the operation across the globe from New York to London and Tel Aviv.
