Agriculture is seeing rapid adoption of artificial intelligence and machine learning, both in terms of agricultural products and in-field farming techniques. Agriculture is both a major industry and a foundation of the economy.
Factors such as climate change, population growth, and food security concerns have propelled the industry into seeking more innovative approaches to protecting and improving crop yield.
As a result, AI is steadily emerging as part of the industry’s technological evolution. In this blog, the applications of AI to provide business leaders with an understanding of current and emerging trends.
AI in the Agricultural Industry
Based on research, the most popular applications of AI in agriculture appear to fall into three major categories:
- Agricultural Robots– Companies are developing and programming autonomous robots to handle essential agricultural tasks such as harvesting crops at a higher volume and faster pace than human laborers.
- Crop and Soil Monitoring– Companies are leveraging computer vision and deep-learning algorithms to process data captured by drones and or software-based technology to monitor crop and soil health.
- Predictive Analytics– Machine learning models are being developed to track and predict various environmental impacts on crop yield such as weather changes.
- Blue River Technology- weed control
The ability to control weeds is a top priority for framers and ongoing challenges as herbicide resistance become more commonplace.
Companies are using automation and robotics to help farmers find more efficient ways to protect crops from weeds.
Blue River Technology has developed a robot called See and Spray which reportedly leverages computer vision to monitor and precisely spray weeds on cotton plants.
- Harvest CROO Robotics- Crop Harvesting
Automation is also emerging to help address challenges in the labor force. Harvest CROO Robotics claims that its robot can harvest 8 acres in a single day and replace 30 human laborers.
It has developed a robot to help strawberry farmers pick and pack their crops. Lack of laborers has reportedly led to millions of dollars revenue losses in key farming regions.
- PEAT- Machine Vision for pests diagnosing
Deforestation and degradation of soil quality remain significant threats to food security and harm the economy. The image recognition and identifies possible defects through images captured by the user’s smartphone cameras.
- Trace Genomics- ML for diagnosing soil defects
Same as other apps it also provides soil analysis services to farmers. The unique quality is that it provides soil’s strengths and weaknesses with machine learning algorithms.
According to the company’s website, after submitting a sample of their soil Trace Genomics, users reportedly receive an in-depth summary of their soil’s contents.
Services are provided in packages which include a pathogen screening focused on bacteria and fungi as well as a comprehensive microbial evaluation.
- Sky Squirrel Technologies- Drone and Computer Vision for Crop Analysis
The presence of drones in agriculture seemed advantageous for farmers. Today, companies are leveraging AI and aerial technology to monitor crop health.
Sky Squirrel is one of the companies bringing drone technology to a vineyard. The company aims to help users improve their crop yield and to reduce costs.
Once the drone completes its route, users can transfer a USB drive from the drone to a computer and upload the captured data to a cloud drive.
Summing it Up!
Extensive testing and validation of emerging Artificial Intelligence in agriculture sector will be not that critical as it is impacted by environmental factors and industries. One anticipates that the agriculture industry will continue to see the steady adoption of AI and will continue to monitor these trends.