Can AI Address Real World Issues, such as Agriculture?
NEW YORK, NY / ACCESSWIRE / September 10, 2020 / To quote a classic paper titled “Machine Learning that Matters” by NASA computer scientist Kiri Wagstaff: “Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society.”
Ordinary people who are not familiar with AI and ML may consider them as fictional, but their applications are stepping out of the science community to address real life issues.
According to UN Food and Agriculture Organization (FAO), the worldwide population will increase to 10 billion by 2050. However, only 4% additional land will come under cultivation by then, let along uncertain threats from climate change and increasing sea level. Traditional methods are not enough to handle those tough problems. AI is steadily emerging as one of the innovative approaches to agriculture. AI-powered solutions should not only enable farmers to produce more with less resources, but also improve food quality and security for consumer market.
AI’s Booming in Agriculture
The global AI in agriculture market size is expected to grow at a CAGR of 24.8% from 2020 to 2030, based on an industrial report. At this rate, the market size would rise from $852.2 million in 2019 to $8,379.5 million in 2030. At present, AI in agriculture is commonly used for precision farming, crop monitoring, soil management, agricultural robots and it has more to come.
Take precision farming as an example, the comprehensive application of AI technologies, such as machine learning, computer vision, and predictive analytics tools. It comprises farm-related data collection and analysis in order to help farmers make accurate decisions and increase the productivity of farmlands.
Dr. Yiannis Ampatzidis, an Assistant Professor of precision agriculture and machine learning at University of Florida (UF), mentions ML applications are already at work in agriculture including imaging, robotics, and big data analysis.
“In precision agriculture, AI is used for detecting plant diseases and pests, plant nutrition, and water management,” Ampatzidis says. He and his team at UF have developed the AgroView cloud-based technology that uses AI algorithms to process, analyze, and visualize data being collected from aerial- and ground-based platforms.
“The amount of these data is huge, and it’s very difficult for a human brain to process and analyze them,” he says. “AI algorithms can detect patterns in these data that can help growers make ‘smart’ decisions. For example, Agroview can detect and count citrus trees, estimate tree height and measure plant nutrient levels.” Ampatzidis believes AI holds great potential in the analytics of agricultural big data. AI is the key to unlocking the power of the massive amounts of data being generated from farms and agricultural research.
Stepping Stone: Labeled Data
A weeding robot makes real-time decisions to identify crops and weeds through close cooperation of built-in cameras, computer vision, machine learning and robotics technology. As the machine drives through the field, high-resolution cameras collect images at a high frame rate. A neural network analyzes each frame and produces a pixel-accurate map of where the crops and weeds are. Once the plants are identified, each weed and crop is mapped to field locations, and the robot sprays only the weeds. The entire process occurs just in milliseconds.
It is challenging to train the neural network models as many weeds look similar to crops. Traditionally, it is agronomists and weed scientists who label millions of field images. However, data labeling is arduous and time-intensive. ML models need to be fed with labeled data in high quality and quantity to get trained or “smarter”automatically and constantly.
The high cost of gathering labeled data restrains AI’s application in agriculture. Aware of such a dilemma, some data service companies, such as ByteBridge.io, provide premium quality data collection and labeling service to empower AI applications to practical industries such as agriculture.
ByteBridge.io has made a breakthrough on its automated data collection and labeling platform where agricultural researchers can create the data annotation and collection projects by themselves. Since most of the data processing is completed on the platform, researchers can keep track of the project progress, speed, quality issues, and even cost required in real-time, thereby improving work efficiency and risk management in a transparent and dynamic way. They can upload raw data and download processed results through ByteBridge’s dashboard. Not only that, ByteBridge.io has utilized blockchain technology to make sure the labeled data service is cost-effective and productive.
Data is powerful, but labeling data makes it useful. Labeled data can be used to train machine learning models effectively. Furthermore, automated, AI-driven labeling platforms, such as ByteBridge.io can help to speed up the data labeling process and accelerate the development of AI industry which aims to address real world issues, such as agriculture.
SOURCE: TTC Foundation
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