AI is a helpful tool in many fields, despite the attention being focused on flashy new artificial intelligence tools like ChatGPT, the difficulties in regulating AI, and doomsday scenarios involving superintelligent machines. It actually has a great deal of promise to advance humankind.
Farmers are employing AI-powered tools more frequently in agriculture to address issues that endanger food security, the environment, and public health. By 2032, researchers predict that the market for these tools will grow to $12 billion USD.
Three exciting advancements in agricultural AI are federated learning, pest and disease detection, and price forecasting.
Combining data without disclosing it
Agriculture is using robotics, sensors, and information technology more and more. By using these tools, farmers can increase productivity and use fewer chemicals. Furthermore, software that employs machine learning to enhance management systems and decision-making can make use of the data gathered by these tools. However, data sharing amongst stakeholders is usually required for these applications.
According to a survey conducted among American farmers, over 50% of participants stated they don’t trust private or federal organizations with their data. This mistrust is related to worries that private data may be compromised or exploited to influence laws and markets. These worries might be lessened with machine learning.
Federated learning is a method that uses data from several sources to train a machine learning algorithm without requiring the sources to disclose their data to one another. Instead of sharing the data on a central server, federated learning allows a farmer to put data on a local computer that the algorithm can access. This technique lowers the chance of compromise while increasing privacy.
Farmers can contribute to a cooperative system that supports them in meeting their sustainability objectives and improving decision-making if they can be convinced to share their data in this manner. If farmers pooled their data regarding the conditions of their chickpea crops, for instance, a model trained on all of their data could forecast chickpea yields more accurately for each farmer than models trained on their own data alone.
Identifying diseases and pests
Plant diseases and pests are putting the livelihoods of farmers and the world’s food security at greater and greater risk. According to estimates from the Food and Agriculture Organization, disease and pests cause $290 billion in losses annually globally, impacting 40% of crop production.
Usually, farmers use chemical sprays on crops to prevent outbreaks. On the other hand, excessive use of these chemicals has been connected to negative consequences for biodiversity, soil and water quality, and human health. It’s concerning that many infections are growing resistant to current therapies, and creating new ones is proving to be challenging. Thus, minimizing the usage of chemicals is crucial, and artificial intelligence might help.
There are Apps which can help farmers more accurately determine how much chemical to use and more precisely target their spraying when used in conjunction with farm management tools. In the end, these efficiencies might lower the need for pesticides, lower the chance of resistance developing, and stop spills that could harm the environment and people.
Crystal ball for prices
Decisions about what to grow and how to invest are influenced by price fluctuations and market volatility. Furthermore, farmers may be discouraged from taking chances with novel innovations due to this uncertainty.
Price forecasting using AI can help to lessen this uncertainty. Artificial Intelligence-driven farm decision support, for instance, is offered by Agtools, Agremo, and GeoPard. These instruments facilitate instantaneous examination of pricing points and market data, while also providing farmers with information on enduring patterns that may aid in maximizing production.
Thanks to this data, farmers can better strategize their planning and respond to changes in prices. The possibility that farmers will be able to invest in new opportunities and technologies that benefit farms and the larger food system rises when their economic resilience does.
AI for good
There have always been winners and losers in human innovation. It’s clear that artificial intelligence poses risks, such as biased algorithms, invasions of privacy, and behaviour manipulation of people. It is a technology that can, nevertheless, also be used to address a variety of issues.
Farmers find these applications of AI in agriculture to be a source of optimism. By advocating for the practical applications of these innovations within the agriculture sector and establishing robust and rational frameworks to mitigate any negative consequences, artificial intelligence (AI) has the potential to mitigate the adverse effects of contemporary agriculture on the environment and human health, as well as contribute to the enhancement of worldwide food security in the twenty-first century.
