In recent years, the agri-food sector has been undergoing a profound transformation, driven by the need to produce more with fewer resources in the face of increasingly complex environmental, economic and social challenges. In this scenario, artificial intelligence (AI) is proving to be a key tool for innovating this sector, improving process efficiency and ensuring smarter and more sustainable management of natural resources. Far from being just a technology used by a few and limited to niche sectors, artificial intelligence is now a concrete reality in fields, warehouses and throughout the agrifood supply chain.
One of the main areas of application is agricultural information management: sensors, drones, satellite imagery and other digital tools generate huge amounts of data every day, which AI can process to provide accurate guidance on irrigation, fertilization, crop health and soil conditions. This “data-driven” approach enables field workers to make more informed decisions, reduce waste and increase productivity.
Optimization of agricultural processes is another front on which AI is making great strides: from automated planting and robotic harvesting to predictive disease and pest management, the technology is making agriculture more precise, targeted and sustainable. However, integrating AI into this sector is not without its obstacles: lack of digital expertise, high upfront costs, and data security and privacy issues are still open challenges.
Having seen in the article Artificial intelligence in the agri-food sector [part 1] the main applications of AI in the agri-food sector, In this article we analyze the role of AI in the information management cycle and its optimization in processes, assessing the benefits, limitations and future prospects.
The role of AI in the agricultural information management cycle
Managing agricultural data with AI can be beneficial in many ways:
- Risk management: predictive analytics reduces errors in agricultural processes.
- Plant selection: the AI used plant growth data to provide additional advice on which crops are most resistant to extreme weather conditions, diseases or harmful pests.
- Soil and crop health analysis: artificial intelligence algorithms can analyze the chemical composition of soil samples to determine which nutrients may be deficient. Artificial intelligence can also identify or even predict crop diseases.
- Crop feeding: artificial intelligence in irrigation is useful in identifying optimal patterns and timing of nutrient application while predicting the optimal mix of agronomic products.
- Harvesting: artificial intelligence is useful for improving crop yields and can even predict the best time to harvest.
AI optimization for agriculture and agricultural processes
Although the benefits of AI in agriculture are obvious, it cannot work without other digital technologies already in place, such as big data, sensors and software. Similarly, other technologies need AI to work properly. In the case of big data, the data itself is not particularly useful. What matters is how it is processed and implemented.
- Big data for informed decision making: by combining artificial intelligence with big data analytics, farmers can receive recommendations based on accurate, real-time information, thereby increasing productivity and reducing costs.
- IoT sensors for data acquisition and analysis: IoT sensors, along with other supporting technologies (AI drones, GIS and other tools), can monitor, measure and store real-time training data on various metrics. By combining these devices with AI and agriculture, farmers can quickly obtain accurate information.
- Smart automation and robotics to minimize manual labor: AI combined with autonomous tractors and IoT helps solve the common problem of labor shortage. Robotics is also important: agricultural robots are already being used for manual tasks such as harvesting produce. Robots are more advantageous for agricultural work because of their ability to work longer, greater accuracy, and less susceptibility to errors.
AI challenges in agriculture
Many people perceive AI as something that applies only to the digital world, with no relevance to physical agricultural activities. This belief is usually based on a lack of knowledge of AI tools. Most people do not fully understand how AI works in agricultural biotechnology, especially those in non-technology sectors, leading to slow adoption of AI in the agricultural sector. Although agriculture has seen countless developments in its long history, many farmers are more familiar with traditional methods. It is unlikely that most farmers have worked on projects involving AI technology.
In addition, software and consulting companies often fail to clearly explain the benefits of new technologies and how to implement them. Technology providers have to do an enormous amount of work to help people understand the application of AI in agriculture. Considering the benefits of AI for sustainable agriculture, implementing this technology may seem like a logical step for every farmer. However, there are still some challenges to overcome.
High upfront costs
Although AI solutions can be cost-effective in the medium- to long-term, the initial investment cannot be avoided. With many farms and agribusinesses struggling financially, adoption of AI may be impossible for the time being, especially in the case of small farmers and those in developing countries. However, the cost of AI implementation may decrease as technologies develop. Companies also have the opportunity to explore financing resources such as government grants or private investment.
Reluctance to embrace new technologies and processes
Unfamiliarity often makes people hesitant to adopt new technologies, creating difficulties for farmers in fully embracing AI, even when it offers undeniable advantages. Resistance to innovation and reluctance to take risks on new processes hold back the development of agricultural methods and the profitability of the industry in general. Farmers need to understand that AI is just a more advanced version of simpler field data processing technologies. To convince farmers to adopt AI, the public and private sectors must provide motivation, resources and training. Governments must also develop the necessary regulations to assure workers that the technology is not a threat.
Lack of practical experience with new technologies
Aspects of the agricultural industry differ in their technological advancement around the world. Some regions could take full advantage of AI, although there are some obstacles in countries where next-generation agricultural technologies are not widespread. Technology companies hoping to do business in regions with emerging agricultural economies may need to take a proactive approach. In addition to providing their products, they need to offer ongoing training and support to farmers and farm owners who are ready to adopt innovative solutions.
A long process of technology adoption
In addition to a lack of understanding and experience, the agricultural sector generally lacks the infrastructure needed to operate AI. Even farms that already have some technology in place may find it difficult to move forward. Infrastructure is also a challenge for technology companies. One of the main ways to overcome this problem is to approach farmers gradually: for example, by first offering the use of a simpler technology, such as an agricultural trading platform. Once farmers become accustomed to a less complicated solution, vendors can add more tools and features, eventually leading to fully AI-based farms.
Technological constraints
Because AI is still under development, the technology will have limitations. Accurate models depend on diverse and high-quality data, which may be scarce in agriculture. For sensor-equipped robots, limitations may make it difficult to adapt to changes in the agricultural environment. Overcoming these limitations requires continued research and data analysis. Farmers should also continue to participate in decision-making, rather than ceding control completely to AI. Manual monitoring of AI decisions can be useful in the early stages of adoption.
Privacy and security concerns
In all areas, there is still a general lack of regulations regarding the use of AI. In particular, the implementation of AI in precision agriculture and smart farming raises several legal issues. For example, security threats, such as cyberattacks and data leaks, can cause serious problems for farmers. It is even conceivable that AI-based agricultural systems could be targeted by hackers with the goal of disrupting food supplies.
What is the future of AI in agriculture?
Artificial intelligence is sure to play an increasingly important role in agriculture and food sustainability in the coming years. Technology has always been at the forefront of agriculture, from primitive tools to irrigation, from tractors to AI. Each development has increased efficiency and reduced the challenges of agriculture.
Most importantly, the benefits of AI in agriculture are undeniable. Smart farming tools, intelligent automation and AI-based products perform time-consuming repetitive tasks so that workers can focus on more strategic operations that require human judgment. Increasingly accessible computer vision and agricultural robotics have the potential to accelerate the progress of AI in agriculture.
AI has the tools to address the challenges posed by climate change, environmental concerns and growing demand for food. It will revolutionize modern agriculture by improving efficiency, sustainability and resource allocation, as well as real-time monitoring for healthier and higher quality produce.
However, it is not enough to buy AI to start using it. AI is not something tangible-it is a set of technologies that are automated through programming. In essence, an AI algorithm mimics the way people think: it first learns and then solves problems based on data. The AI-driven transformation of agriculture will require changes in the industry. Farmers need to be educated and trained on how to use AI-based solutions.
What does this mean for workers in the agricultural industry? AI is likely to change the role of farmers from manual workers to planners and supervisors of intelligent agricultural systems. Understanding computer solutions and agribusiness intelligence will potentially become more useful than the ability to use conventional tools or perform physical work.
Although AI and machine learning, along with MLOps services, have the potential to radically transform agriculture, they need other technologies to work in sync. To take full advantage of AI, farmers need a technology infrastructure. It may take years to develop that infrastructure, but doing so could result in a robust and future-proof technology ecosystem. Understanding how AI works and how to best integrate technical knowledge into real-world processes is critical to maximizing its benefits. That is why working with freelancer developers and experienced software development teams is an excellent first step. Technology companies have an important role to play. Each must consider how to improve their tools, address challenges, and clearly convey the measurable benefits of AI and machine learning. If this can be achieved, the future of AI in agriculture will surely be fruitful.
The success of human society depends essentially on optimizing its agricultural systems. Traditional agricultural methods are becoming obsolete and need advanced technological solutions. Around the world, the impact of automation on industries has always been considerable. Digital technology is now playing a huge role in the transformation of agriculture, and the impact of artificial intelligence in agriculture is bound to be vast.
