Ten ways AI has the potential to improve agriculture in 2021

AI, machine learning (ML), and IoT sensors can provide rich real-time data for algorithms to improve agricultural production efficiency, increase crop yields, and reduce food production costs. According to the UN‘s forecast data on population and hunger, by 2050, the global population will further increase by 2 billion, and agricultural productivity needs to increase by 60% to provide adequate food. According to data released by the Bureau of Economic Research of the U.S. Department of Agriculture, in the United States alone, the total market for planting, processing and food distribution businesses is as high as $1.7 trillion. By 2050, artificial intelligence and machine learning are likely to become the core of new technologies, helping us to calmly cope with the expected food demand brought by the 2 billion new population.

"Agriculture"-one of the most promising artificial intelligence and machine learning application scenarios

Imagine that in these large farming areas, which are usually hundreds of acres as the basic planning unit, there are at least 40 basic processes that need to be tracked, highlighted, and monitored simultaneously. In-depth analysis of weather changes, seasonal sun differences, grasping the migration patterns of birds and insects, understanding the needs of special fertilizers, choosing suitable pesticides for crops, monitoring the planting cycle and irrigation cycle, etc., are all for machine learning It is a major problem that is expected to be solved and of great practical significance. Today, crop production is increasingly dependent on excellent data collection and analysis capabilities. Because of this, farmers, cooperatives, and agricultural development companies have decided to further adopt a data-centric approach and continue to introduce AI and machine learning elements to improve agricultural yields and crop quality. Looking at 2021, the following ten methods are expected to promote further development of agriculture:

1. Use a monitoring system based on AI and machine learning to track the real-time video source of each crop field, thereby identifying violations of animals or humans and issuing an alarm immediately.

AI and machine learning can reduce the possibility of accidentally destroying crops by domestic or wild animals or breaking into farms in remote areas. With the rapid development of AI and machine learning algorithms in the field of video analysis, every participant in agricultural production can use this to protect their fields and agricultural facilities. AI and machine learning video surveillance systems can be easily expanded to adapt to large-scale agricultural operations, covering the entire farm. Over time, we can program or train a monitoring system based on machine learning to teach it to recognize people and vehicles. As a leader in the field of AI and machine learning monitoring systems, Twenty20 Solutions has proven that these technologies can effectively protect remote facilities, optimize crop production, and identify accidental intruders on the field through machine learning.