How Artificial Intelligence Is Transforming the Agriculture Sector

Digital innovation is revolutionizing the way the agri-food sector is working. Incorporating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI) and blockchain, opens new horizons for agriculture, maximizing efficiency and making production more sustainable. Currently impacted by hurdles such as limited land holdings, labor shortages, climate change, environmental issues, and diminishing soil fertility — the modern agricultural landscape is evolving, branching out in various innovative directions; increasingly taking advantage of technological capabilities in particular AI. 

Benefits of AI in Agriculture
Artificial Intelligence (AI) is helping to revolutionize the agricultural industry by offering a wide array of benefits that streamline processes, improve efficiency, and enhance crop production.

Here are specific use cases for integrating AI in agriculture:

1. Precision Farming:

AI enables precision farming by analyzing data from various sources such as satellites, drones, and sensors to optimize agricultural practices. This level of precision helps farmers in making informed decisions about irrigation, fertilization, and pest control, leading to higher crop yields and resource efficiency.

An example of software for precision agriculture is the software from xFarm. As per Matteo Vanotti, CEO of xFarm Technologies: “We have developed a platform that allows the various actors involved in the agri-food supply chains to manage all aspects of an agricultural company: from the management of the machines to that of crops, irrigation, bureaucracy, treatments”. Through its digital platform, xFarm Technologies today supports and simplifies the work of 130 thousand farms spread over 1.8 million hectares in over 100 countries around the world. “Our platform also solves the problem of technological fragmentation, because it allows you to manage both machines that are not yet digitalised, thanks to appropriate integrations, and fleets with different brands: everything directly from the app”, follows Vanotti. In this way, farmers can always have under control, through a simple and intuitive platform, their processes, vegetative indices, stocks, silo capacities, but also the characteristics of the plots to optimize field interventions and reduce the environmental impact.

2. Predictive Analytics:
By utilizing AI algorithms, farmers can predict crop yields, optimize planting patterns, and foresee potential disease outbreaks. This proactive approach allows for better resource allocation and risk management, ultimately improving overall productivity.

3. Crop Monitoring and Management:
AI-powered systems can monitor crop health, detect diseases, and assess the need for specific interventions. This proactive monitoring helps in early identification of issues, allowing farmers to take corrective actions promptly and prevent extensive crop damage.

A Smart agriculture automation and energy management use case is highlighted by Carlo Gavazzi Group. It has created cutting-edge control systems and monitoring tools specifically for the agricultural sector. Their solution manages energy consumption, improve overall farm efficiency and optimize irrigation practices, by combining AI and IoT functionalities. The solution covers: Sensors for power systems; from capacitive sensors that control seed and grain levels in feeding systems and monitor the filling process, to photoelectric sensors that ensure maximum performance and resistance in these harsh environments. Components for harvesting machines, such as inductive proximity sensors, which are used for non-contact measurements, for example to count axle revolutions in machines or to track the bucket position in a backhoe loader. Capacitive level sensors, mounted on sprayers with externally attached plastic tanks, can also detect the presence of compounds in a water base while ignoring the formation of foam, film or buildup.

4. Labor Savings:
AI automation reduces the manual labor involved in various agricultural tasks such as harvesting, sorting, and packaging. This not only saves time and labor costs but also addresses labor shortage challenges in the industry. How to integrate AI into the workforce in the fields? Through the use of robotics, as Roberto Minetto, CEO of Hortobot, explains. His made in Italy company, presented during the Milanese edition of Smau (a reference event in Italy on innovation and startups) offers the first solution of this type in the world for multi-crop fields. “Hortobot is an autonomous robot that allows you to automate some processes in agriculture: from soil preparation to sowing up to the finished crop cycle. The collection takes place in a collaborative manner: the robot carries the person, he is seated comfortably, uses voice commands and has his hands free to carry it out". A robot designed entirely by the company, from mechanics to electronics.

5. Sustainable Practices:
AI assists in implementing sustainable agricultural practices by optimizing resource usage, reducing chemical inputs, and minimizing environmental impact. By leveraging AI, farmers can adopt more eco-friendly approaches to agriculture.

6. Market Insights and Decision-Making:
AI tools can analyze market trends, consumer behavior, and demand patterns to help farmers make informed decisions about what to produce and when to bring products to market. This insight leads to better business strategies and improved market competitiveness. A good example of AI being leveraged to collect real-time data is Seeed Studio, a leading IoT hardware provider, which has helped to develop a wide range of sensor modules and connectivity solutions specifically designed for agriculture. Their products enable farmers to collect real-time data on soil moisture, temperature and other essential parameters, facilitating decision-making and optimizing resources. By improving last mile access, and ensuring the right digital solutions reach people in need, the smart village model helps accelerate progress towards achieving several Sustainable Development Goals (SDGs) – such as health, trade, education and agriculture – through an integrated approach to digital development.

7. Weather Forecasting and Risk Mitigation:
AI algorithms can process large volumes of weather data to provide accurate forecasts, enabling farmers to plan operations and mitigate risks associated with extreme weather events.

8. Automation

Examples of how robotics are being used in the agricultural sector is a Dutch company RobotOne which is used for mechanical and laser weeding It is from the Dutch Pixelarming Robotics RobotOne, which is equipped with ten robotic arms, all independently adjustable, which can use different types of tools to remove weeds, which it recognizes thanks to artificial intelligence. Suitable for vast fields rich in biodiversity, it is equipped with solar panels for recharging the battery, a GPS antenna and 14 cameras that detect depth.

Another example is this French company: Traxx Concept H2 by Exxact Robotics: the world's first hydrogen-powered agricultural robot The French company Exxact Robotics has introduced a completely innovative concept with the Traxx Concept H2, the world's first autonomous hydrogen-powered straddle tractor, designed to work in vineyards. This new technology uses a powerful hydrogen fuel cell, combined with small batteries to provide additional power when needed, bringing the total power to 35 kW. The Traxx Concept H2 is able to operate autonomously in the vineyards without impacting the ground, thanks to its low weight of only 1,300 kg, excluding equipment. This vehicle enjoys autonomy that allows it to work for an entire day without interruptions.

In conclusion, the integration of AI in agriculture offers numerous benefits, ranging from increased productivity and cost savings to sustainable practices and enhanced decision-making. As AI technology continues to advance, its potential to revolutionize the agricultural sector and address global food security challenges becomes increasingly apparent.