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AI Technology to Help Agriculture, the Future Can Be Expected — Part 2

“Agriculture” — One of the Most Promising AI and ML Application Scenarios

Imagine that there are at least 40 basic processes that need to be tracked, highlighted, and monitored simultaneously in these large-scale farming areas, often with hundreds of acres as the basic planning unit. There are major problems of great practical significance, expected to be solved by ML, including in-depth analysis of weather changes, seasonal sunlight differences, grasping the migration patterns of birds and insects, understanding the use of special fertilizers, choosing the right pesticides for crops, monitoring planting, and irrigation cycles. Until now, crop production is increasingly dependent on superior data collection and analysis.

Firstly, AI and machine learning-based surveillance systems are used to track real-time video recordings of each crop field to identify animal or human violations and issue immediate alerts.

AI and ML can reduce the likelihood of livestock or wildlife accidentally destroying crops or breaking into farms in remote areas. With the rapid development of algorithms in video analysis, everyone involved in agricultural production can protect their fields and facilities. The video surveillance systems can be easily scaled up to accommodate large-scale agricultural operations, covering the whole farms. As time goes by, the surveillance systems can be programmed and trained to recognize people and vehicles.

As the leader of a one-stop AI platform, Shen Yan Technology has proven that these technologies can effectively protect remote facilities, optimize crop production and identify unexpected intruders on the field.

Secondly, AI and ML — improve crop yield forecasts through real-time drone sensor data and visual analysis of data.

With real-time video streams from smart sensors and data captured by drones, agricultural experts have access to new datasets that were previously unavailable. Researchers can now combine sensor data such as moisture, fertilizer, and natural nutrient levels to analyze how each crop grows over time. Machine learning is responsible for integrating large datasets and making recommendations based on constraints to optimize crop yields. Real-time video analytics can be used to allow farmers to gain new insights into improving crop health and yield per acre. Drones have proven to be an extremely reliable platform for collecting data in relation to the impact of specific fertilizers, irrigation, and pesticide treatments on actual crop yields.

Labeled Dataset: From the General Dataset to the Unique One

If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain model’s commercialization, they must gradually move forward from the general dataset to create the unique one.

End

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source:https://www.163.com/dy/article/GTHK2DJV0552OFB6.html

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