Application of AI in Agriculture

Digitization has brought new terms to the vocabulary used in agriculture. Today it is common to hear: precision agriculture, sensors, augmented and/or virtual reality, the internet of things, massive data analysis or Agriculture 4.0. Although there is one that stands out above all: artificial intelligence.

AI in agriculture, also known as precision agriculture, is the application of artificial intelligence (AI) solutions in the agricultural industry. The technology is used for field harvesting, health monitoring, weed and pest control, detection of nutrient deficiencies in soil, and other tasks.

Then we can ask ourselves, how will artificial intelligence transform agriculture?

In an environment as delicate as that of agriculture, with multiple factors involved and a very narrow margin of error, artificial intelligence has arrived to provide elements for an improved and comprehensive management of the entire process, from the decision of what and when to cultivate until it reaches the final consumer. Something that has made its inclusion in the sector key to competitiveness.

AI can potentially change the way we see agriculture, enabling farmers to achieve more results with less effort while bringing many other benefits. However, AI is not a technology that works independently. As the next step on the way from traditional to innovative farming, AI can supplement already implemented technologies. Agribusinesses need to know that AI isn’t a panacea. However, it can bring tangible benefits to small everyday things and simplify the lives of farmers in many ways.

Analyzing the situation and market forecasts for artificial intelligence in agriculture, the prospects are very encouraging. An example of this are the figures offered by Markets and Markets, according to which it will grow to 4,000 million dollars in 2026, with a CAGR of 25.5%. The main objective sought by agricultural professionals with artificial intelligence in agriculture is to improve the productivity of their farms and thus obtain a competitive advantage in their environment.

According to my research, AI is transforming agriculture in the following aspects:

·????????Before sowing: An interesting option is that predictive models can be used to generate seed varieties that are better adapted to the conditions of each environment. Something highly appreciated in especially complex scenarios for agriculture due to its climate, soil conditions or other factors. The importance of data collected on crops is very useful in order to make decisions related to the best products to grow, the ideal time and other issues that will influence the return on investment of the business.

·????????Monitoring and surveillance: Once the crops are underway, artificial intelligence is a great ally in order to control their growth. With its application and RGB, thermal or ultrasonic cameras, we can determine at any time its maturity degree, amount of water in the fruit, sugar levels... some determining parameters to estimate the best moment for its harvest. Also, it is important the role of artificial intelligence in pest control, with sensors that monitor and identify insects, recognizing those that are potentially dangerous for the crop.

·????????Harvest: The data on the state of the food can be crossed with that of the market itself, the weather, the amount of product to be collected and the availability and wages of day laborers to estimate the best time for collection, since the point of view of quality of the product, and from the economic one, finding the balance between both. With this, it is possible to minimize the costs involved, as well as maximize the return on investment.

·????????Classification, separation and quality control: Once the collected food is brought to the plant, it is usual to screen it. Thus, those that do not meet minimum standards are discarded. In addition, these can be classified into different ranges or to manufacture products made from them, such as juices. Here the presence of defects, size, color, maturity, and percentage of water come into play. With the use of automated systems, this process is accelerated and errors resulting from human fatigue and the disparity of criteria between operators are avoided.

·????????Equipment predictive maintenance: When the time comes to package or process agricultural products, it is key that the equipment involved is in operation as long as possible. Otherwise, excessively long stops could spoil the food or that it does not reach the consumer in the ideal state, with the consequent losses for the business. To avoid this, it is crucial to apply predictive maintenance techniques that notify us of which equipment or its components are most likely to fail in the near future. With this, we will be able to act in advance and ensure the most continuous production possible.

·????????Logistics: From the warehouse itself to the final distributor, artificial intelligence has a lot to say in the logistics of agricultural products. In the warehouse, artificial vision can perform an immediate reading of the labels, always keeping each batch under control. When embarking on the path to commerce, various sensors can control environmental variables at all times such as the temperature or humidity in which they are located and the system will launch alerts if it foresees a deterioration of these. On the other hand, it can determine the most optimal routes for carriers at all times, even responding to incidents such as retentions, road closures or any other.

·????????Comprehensive traceability: All these data represent the foundations to establish a total traceability of each product. We will always be able to know every detail of the process you have experienced from the field to the table. This, for example, makes it easier to identify batches that must be withdrawn from the market because they pose a risk to consumer health.

To sum up, Artificial intelligence in agriculture is synonymous with efficiency, quality, use of resources, productivity, cost reduction, food safety, customer satisfaction and many more benefits that are an obvious trend in the sector.

AI, machine learning (ML) and IoT sensors (which provide real-time data for algorithms) basically provide solutions for:

·????????Increase agricultural efficiency,

·????????Improve crop yields,

·????????Reduce food production costs and

·????????Reduce the use of pesticides and other pollutants.

?

Advantages of artificial intelligence in agriculture

·????????Data: obtaining data allows greater decision-making power in a specific situation. Agriculture depends on many different factors, the more information we have, the greater control and power of decision and forecast we will have.

·????????Environmental and economic sustainability: the use of agricultural artificial intelligence allows us to adjust the doses of planting, irrigation and phytosanitary products and fertilizers, which allows us to save a lot on phytosanitary products. This has a positive economic impact on our farm and the environment.

·????????Food safety: control of all processes both in the field and post-harvest allows exhaustive control of crop traceability and food safety.

Disadvantages of artificial intelligence in agriculture

·????????Economic cost: although this is not always the case, applying artificial intelligence can sometimes be more expensive than the economic benefit that it can bring you. Technology has advanced a lot in recent years but there is still technology that cannot be applied since its cost is greater than the benefit that ends up affecting the accounts of agricultural companies.

·????????Technology maintenance: requires regular maintenance in any type of sector. In the case of agriculture, technology is often exposed to different situations and weather or adverse weather conditions, which requires greater attention and maintenance.

·????????Training: the use of artificial intelligence requires basic knowledge of technology.

Farmers tend to perceive AI as something that applies only to the digital world. They might not see how it can help them work the physical land. This is not because they’re conservative or wary of the unknown. Their resistance is caused by a lack of understanding of the practical application of AI tools. New technologies often seem confusing and unreasonably expensive because AgriTech providers fail to clearly explain why their solutions are useful and how exactly they should be implemented. This is what happens with artificial intelligence in agriculture. Although AI can be useful, there’s still a lot of work to be done by technology providers to help farmers implement it the right way.

The benefits of AI in agriculture are undeniable. Smart farming tools and vertical farming systems can perform small, repeatable, and time-consuming tasks so farm workers can use their time for more strategic operations that require human intelligence. However, it’s important to realize that unlike a tractor, one can’t just buy AI and start it. AI is not something tangible. It’s a set of technologies that are automated through programming.

Artificial intelligence is essentially a simulation of thinking; it’s learning and problem-solving based on data. AI is just the next step in the development of smart farming, and it needs other technology to actually work. In other words, to reap all the benefits of AI, farmers first need a technology infrastructure. It will take some time, possibly even years, to develop that infrastructure. But by doing so, farmers will be able to build a robust technology ecosystem that will stand the test of time.

AI for agriculture addresses the three most critical issues of farming: overuse of chemicals, tedious manual labor, and process efficiency. The technology is making quick progress, so many agricultural businesses are expected to operate AI solutions within the next few years.