According to UN estimates, the global population will reach 9.7 billion people in 30 years. To add to this concern, global warming has an impact on crop growth, further diminishing available food resources. Just like centuries ago, the agrarian sector is going to face a new transformation in the decades to come.
With this goal in mind, governments, organizations and researchers in agricultural area are seeking new ways to revolutionize farming. The power of AI to make farming more efficient and productive could be the answer to combating these growing threats. As of today, AI applications in agriculture have expanded into accurate and controlled farming through providing proper guidance to farmers about precision farming, water management, crop rotation, timely harvesting, nutrient management and pest attacks.
AI in agriculture sector applies advanced technologies such as machine learning, data analytics and availability of sensors and cameras, etc. By analyzing the data sources such as temperature, soil, weather, and crop performances, AI in agriculture sector will be able to provide better predictive insights and help farmers improve productivity.
Common cases of AI in agriculture can include but not limited to the following:
- Autonomous tractors/robots. Equipped with sensor navigation, the tractors are programmed to independently detect ploughing position; decide driving speed; avoid obstacles like irrigation objects, humans and animals while performing various tasks. Robotics machines are trained to distinguish weeds, check the quality of crops, and harvest the crops at a much faster pace with higher volume compare to manpower.
- Drones. Ground-based and aerial-based drones can play significant roles in crop monitoring, irrigation, soil assessment and field inspection. Drones gather real-time and accurate data through a series of sensors that are used for imaging, mapping, and surveying on farmland. With strategy and planning based on data collection and processing, drone technology will give a high-tech makeover to the agriculture industry.
At present, we cannot deny that it is still too early to talk about a complete AI transformation in agriculture. Drones and robots need to go through the machine learning training first before they are able to work automatically. Developers input a large amount of high-quality labeled training data into the machine learning algorithms which can gradually train themselves to identity images and other related data.
However, a mass of labeled data in agriculture becomes an obstacle of machine learning development. AI runs on data but data needs to be labeled first. It is labor-intensive and time-consuming to label millions of data on field.
ByteBridge.io has realized such an urgent demand for high quality labeled data in machine learning and agricultural industry. Based in Beijing, the tech startup has launched its automated data training platform this year to facilitate machine learning researchers to get high quality labeled data in a cost-effective and efficient way. The datasets that Bytebridge.io collects are provided with the best quality and accuracy related to agriculture and other industries from reliable sources. The strong and effective data labeling platform makes machine learning process as smooth as possible.
From artificial fertilizers to weeding robots, agriculture technology has come a long way. The cutting-edging AI enables farmers to produce more and utilize resources more sustainably.
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