CIOREVIEW >> Agtech >>

AI in Agriculture: A Modern Approach toward Successful Farming

By CIOReview | Friday, November 16, 2018

Agriculture, the essential ends and means for the foundation of any economy, is slowly growing digital in its various activities like planting, maintaining, and, harvesting. For this purpose farmers require time, money, energy, labor, and resources. What if these agricultural activities become automated? Here is where Artificial Intelligence (AI) influences agriculture. In the beginning, many doubts and questions were raised from researchers and organizations about whether AI can work with agriculture or not. The farmers were also curious about the applications of AI in farming. However, all these doubts have now been cleared by the technology itself.

As data management was complex in agriculture, the OEMs (Original Equipment Manufacturers) and farm management information system groups have focused more on taking out the difficulty of making data-based decisions with the use of machine learning. The New Holland combine development team has put the first stepping stone for machine learning algorithms in agriculture at the Agritechnica Farm Show in 2017 and will become a reality in the coming year, 2019. They are initiating a new technology named the Field and Yield Prediction System, a self-monitoring tool which can predict variations in the slope and crop density.

The vehicle data is the next concern in the agriculture industry. Data recording tools with the connected devices can perform various tasks like live data transferring, analysis of machine data, and decision making for preventive maintenance and services. This will enable the OEMs and dealerships to ensure efficiency in the resource management, and it will allow them to take actions on any issue before they occur.

Organizations today develop a variety of robots for handling various agricultural tasks like the faster harvesting of crops than humans in a higher volume. AI in agriculture will gain more opportunities in predictive analytics by predicting and tracking the natural disasters or impacts on the cultivation such as climate changes. Organizations leverage machine learning algorithms to analyze the data collected by the devices for monitoring the soil and crop health.

On the whole, technological applications in the agriculture industry are as best as the other industrial applications, but sometimes it will be critical when the industry will be impacted by certain environmental risks that cannot be controlled easily like how AI does in other sectors. Above all, the adoption of AI technologies in agriculture is highly increasing.