AI, News, Technologies

Computer Vision and Deep Learning Technologies in Agriculture

Technologies in Agriculture, Computer Vision, Deep Learning, Crop and Soil Monitoring, stress levels in plants

Seemingly unrelated to digitalization, present-day agriculture more and more relies on AI technology. It becomes efficient and precise. High-tech agriculture employs robots and drones to monitor soil and growing conditions, control pests, plant irrigation, and harvest. What technologies does AI provide for farmers?

First off, it’s Computer Vision. It combines high-resolution image capturing with high-speed computing. So, it breaks down images and video that helps specialists make informed decisions about their farms. After that, annotated data in the format of images is used to create objects recognizable to machines through Machine learning algorithms for similar predictions.

If you are interested in food cultivation of the future, let’s see in more detail how and for what purposes farmers are using artificial intelligence.

Crop and Soil Monitoring

AI and machine learning allow farmers to recognize highly valuable patterns in multispectral imagery of agricultural land.

From drones, AI-enabled cameras can capture the entire farm’s images and analyze the pictures in near-real-time to identify problem areas and potential improvements.

According to PWC, the total addressable market for drone-based solutions worldwide for agriculture reaches $32.4 billion.

Drones can cover much more land in much less time than humans do. Thus, flying devices quickly monitor large farms – a solar-powered autonomous drone can cover up to 200 acres at a single fly.

Farmers are leveraging these technologies to process data captured by drones and software-based technology to monitor the state of crop and soil.

Besides, artificial intelligence applications help optimize resources by creating a field map and identifying areas where crops require water, fertilizer, or pesticides.

Predictive Analysis

Farmers also use AI to create seasonal forecasting models to improve agricultural accuracy and increase productivity. Machine learning models can predict upcoming weather patterns and environmental impacts on crop yield in advance to assist farmers’ decisions. Weather forecasts are customized based on each client’s needs.

Apart from that, Machine learning models can be used to recognize stress levels in plants. The entire approach can be classified into four stages of identification, classification, quantification, and prediction to make better decisions.

For instance, Descartes Labs is employing Machine learning to analyze satellite imagery to forecast soy and corn yields. The New Mexico startup collects 5 terabytes of data every day from multiple satellite constellations, including NASA and the European Space Agency. Linked with weather readings and other real-time inputs, Descartes Labs reports it can predict cornfield yields with high accuracy. Its AI platform can even assess crop health from infrared readings.

Boosted Yields

The world will need more and more food – the Earth population is growing. Its number is predicted to grow from almost 8 billion to 10 billion by 2050. In such a prospect, Artificial intelligence appears to be a key ingredient in improving crop yields.

Markets and Markets researchers state that in January 2020, IBM and Yara International invited farmer associations, key industry players in the agriculture industry to join a movement to develop an open data exchange that facilitates collaboration around farm and field data, to improve the efficiency, transparency, and sustainability of global food production.

The scope of manual input in agriculture will considerably reduce. For example, AI-backed satellites can simplify the entire process of identifying the crop water requirement and various farm plants’ diseases. The IoT app, a new sensation in the agricultural landscape, can show the satellites’ collected data to their phones.

Apart from that, the smart irrigation feature has reduced farmers’ need to be present in the field. This automated work process will empower them to make informed decisions in much less time. With this technology’s help, the agriculturalists can take more preventive steps to improve the crop production cycle.

Companies are developing and programming autonomous robots to handle essential agricultural tasks such as harvesting crops at a higher volume and faster pace than human laborers. For example, Harvest CROO Robotics has developed a robot to help strawberry farmers pick and pack their crops. These robots can replace 30 human laborers.

Conclusion

Though Artificial Intelligence offers vast opportunities for application in agriculture, there is still a lack of familiarity with high-tech Machine learning solutions in farms across most parts of the world.

Now artificial intelligence technologies are becoming more affordable – various enterprises incorporate agriculture-friendly algorithms. We hope that the maximum number of farmers will come forward to accept and apply these technologies in their farmlands in the upcoming future.


Andrew Mikhailov

 

Author’s Bio: From 2017 as a CTO at Zfort Group, Andrew Mikhailov concentrates on growing the company into the areas of modern technologies like Artificial Intelligence, BigData, and IoT. Being a CTO, Andrew doesn’t give up programming himself because it is critical for some of the projects Andrew curates as a CTO.

 


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