According to UN estimates, in 30 years, the global population will reach 9.7 billion people. This means there will be a need to provide 50% more food by 2050. Just like centuries ago, the agrarian sector will face a new transformation in the decades to come.
With this goal in mind, governments and the private sector are seeking ways to revolutionize farming. One way is through artificial intelligence. As of today, AI technology has already made a difference in areas such as precision farming, smart animal husbandry and robotics. Common use cases of AI in agriculture include:
• Monitoring applications. These allow farmers to monitor crop conditions with helmets and goggles driven by machine learning (ML).
• Autonomous tractors. Equipped with radio navigation and a laser gyroscope, the tractor follows the route previously drawn up by the driver. AI learns to carry out instructions with minimal human intervention or no intervention at all.
• Intelligent irrigation with AI support. Modern ML-driven irrigation technologies can distinguish crops from weeds and spray the latter with the right quantity of herbicides.
• Predictive analytics. There are a variety of use cases in this area. These include solutions that predict crop yields using ML algorithms and satellite data. In addition, in the fintech space, where my company focuses, AI algorithms can evaluate the creditworthiness of farm borrowers.
The Promise Of Predictive Analytics
One of the most promising areas for the farming industry is predictive analytics. We live in an increasingly data-driven world, and the agricultural sector is no exception. Modern farmers have access to previously inaccessible sources of information: satellite and unmanned aerial vehicle images, readings from humidity sensors, ground-based weather stations, etc. At the same time, new monitoring and control systems that can offer individualized and more accurate analyses and forecasting are appearing in the market.
Precision farming will optimize the processes of monitoring the state of soil, crops and efficiently use reclamation systems to achieve the highest quality yield indicators. In precision farming, detectors do this job, as well as a central computer that in conjunction with the navigation system receives signals from detectors.
Apart from precision farming, AI allows farmers to monitor the health of animals, identify their genetic characteristics and manage the number of livestock. Smart devices also help control their movement and track feed requirements. Smart animal husbandry is an agro-technological area that involves the use of IoT (Internet of Things) technologies to collect data: milk yield, the need and time for taking medicine, feeding, etc.
As mentioned earlier, predictive analytics work in the domain of agricultural finance, as well. Fintech startups are using AI to evaluate the creditworthiness of farm borrowers when they apply for a loan. Combining predictive analytics with precision agriculture, these types of solutions convert field data taken from satellites into valuable insights for lenders. With machine learning algorithms in place, these tools provide an accurate credit scoring mechanism for risk officers. This approach to scoring permits banks to get a detailed financial portrait of a farmer and thus minimize default risk when considering a loan.
And it’s not solely about credit data. These tools can also work for another party, as well: Platforms can give farmers access to vegetation process monitoring via satellites, variable rates for nitrogen, potassium, phosphorus, soil moisture and temperature measurement, etc. In regard to the growing global population, agricultural lending serves as a strategically important means to develop the sector. It’s especially true for developing countries where lenders tend to decline loan requests from farmers. Hopefully, such solutions may change the credit landscape for good.
AI is growing in many industries, and quickly. A Deloitte survey found that in 2019, 53% of businesses adopting AI spent over $20 million on technology and talent acquisition in the past year.
It’s fair to say the use of technological advances in farming is nothing fresh. From the invention of grain elevators and artificial fertilizers to the use of satellites, agriculture technology has come a long way. However, it is too early to talk about a complete digital transformation in this industry.
First, the industry needs to build an infrastructure that effectively collects data for further processing. It’s also important to note here that with the widespread digitalization in modern agriculture, the amount of data will grow exponentially. The creation of an effective agricultural data management system demands the procession of huge amounts of information. That’s the biggest challenge to overcome.
Secondly, without comprehensive automated solutions, data will be barely useful. It is important to include independent solutions of the whole agro-industrial complex into a global system. This way, the data from one project will be available to any party involved, making the connected systems interact with full capacity.
Challenges exist, but one thing is sure: AI will significantly increase the efficiency of the farming industry. Undoubtedly, the need for quality AI solutions in agriculture will only grow. But we need to make sure there is cooperation between governments, science and businesses in terms of proper investment and research.