ML-Enabled Robots in Agricultural Farms
ML-Enabled Robots in Agricultural Farms
Recent trends have shown that AI is being used in various fields like healthcare, retail, marketing, and now, agriculture.
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Artificial Intelligence is the ability of new age machines to mimic human ability to think and learn. Aided by Machine Learning, it is teaching robots to perform cognitive functions similar to that of a human brain. They can understand the environment and take actions that seem prudent in that environment. While Artificial Intelligence as a concept has been around for decades, Machine Learning has enabled its real-world applications only in recent years.
Recent trends have shown that Artificial Intelligence is being used in various fields like healthcare, retail, marketing, etc. Now, it is also gaining traction in the field of agriculture. It is being used in robots to work in the agricultural industry. The most prevalent robotic applications are in the field of primary surveillance by use of drones, precision weed control, crop harvesting, planting, and seeding.
Serious concerns regarding climate change, population growth, and resulting uncertainty in food security have led to scientists developing innovative solutions suited to the requirements of the agriculture sector. The aim of the application of Machine Learning robots in agriculture is to improve productivity. For example, there are robots that can identify diseases in plants. A technique called transfer learning teaches the AI-driven robots to recognize crop diseases and pest-related damage. Google’s open source library TensorFlow has been used here to build a library of 2,756 images of cassava leaves from some plants in Tanzania. There was a 98% success rate to this experiment.
Similarly, an organization called Abundant Robotics developed an apple-picking robot. Tractor manufacturers John Deere use Artificial Intelligence and Machine Learning to eliminate weed from fields and take care of the plants. Farmers are using soil samples to gather data and this data is then being stored on-farm management systems to allow for analysis. This will help deploy further Machine Learning benefits to agriculture through the use of advanced robotics.
Many technology companies are developing algorithms that can be useful for agricultural practices. AgVoice is a system that turns voice into data, thereby developing a workflow management service for agricultural professionals. It combines an industrial voice enable user interface with a cloud-based analytics platform. It enables agricultural professionals to achieve high-quality fast inspections, reporting and workflow management. This improves the agricultural supply chain logistics greatly compared to traditional means. Similarly, backed by McCain Foods, Resson Aerospace — a Canadian data processing company is using image processing algorithms to help farmers protect their crops.
AI/Machine Learning helps companies implement robots that can automate various processes. A company called Blue River Technology has developed a robot called "See & Spray" which uses a computer vision to monitor the weeds and spray them on the cotton plants with precision. Precision spraying results in prevention of herbicide resistance in plants. The company claims that their technology has helped in reducing the chemical exposure of plants up to 80%. This has resulted in reducing the herbicide expenditure by 90%. Tractor company John Deere has now acquired this company and is said to be investing $305 million in it.
A Japanese company, Shibiya Seiki, has developed a $50,000 robot that can pick strawberries. Although this machine works in a special set up that allows the robot to pick the fruit, it can spur new ideas about farm setups that are more efficient than the current ones.
Various Companies are developing autonomous robots who can handle labor-intensive farming works such as crop harvesting. This will improve the speed of work as well as scale up the volumes. Predictive analytics is also being used by companies to develop Machine Learning models to track and predict weather changes. Machine Learning has made it possible for companies to develop sustainable and scalable methods that are of value in the agriculture industry. It spells new opportunities for this sector as its implementation increases.
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