Precision Agriculture

Traditional agriculture practices entail common tasks such as soil preparation, planting, water and nutrient management, weeding, harvesting, and sorting. Today with the advent of modern technologies, most processes can be successfully automated. From a technological point of view, computer vision and machine learning play a central role. For example, smart systems can sort yield, visually examining each single fruit as a human would do, but with incredible speed. Intelligent weeding systems recognize weeds by their shape and allow their elimination chemically or mechanically. Farm vehicles equipped with a computer vision navigation system can work in the field autonomously—in other words, with no driver.  In the end, all these systems enable farmers to significantly reduce costs and increase productivity at the same time.

What We Do

Automatic Fruits / Flowers Sorting

Our computer vision system helps to sort yield by a number of visual features, such as shape, size, color, texture, surface quality, maturity, and symmetry.

Plant disease detection

Computer vision and machine-learning solutions offer great opportunities for the automatic recognition of sick plants by visual inspection of damaged leaves.

Crop yield prediction

Modern remote sensing techniques together with random sampling provide enough data for a pre-trained computer model to predict crop yield at global as ...

Weed recognition

The automated identification of weed species is performed by machine-learning methods that allow species to be distinguished by their shape parameters.

Our Vision

Precision agriculture is the main concept of modern farming management. The wide view shows diverse technologies being applied to the food industry, starting from satellite field surveys and tractor automation and ending with DNA modification and synthesis of purely artificial fibers, replacing meat. 

As computer vision scientists, we work on solutions that allow automation of most farming routines, optimizing processes and reducing costs. The methods rely mainly upon a combination of cameras, sensors, satellite-imaging and machine-learning algorithms, and general computer science approaches. We are glad to see that agriculture is becoming highly driven by data and technologies, and believe that in the near future, the whole industry will be fully automated, like today’s large-scale manufacturing industry.

The new age of farming not only promises to save costs and increase food quality, but also reduce environmental impact by using less water, energy, fertilizers, and pesticides. With the world’s population increasing, the issues of maximizing efficiency of food production and reducing the load on the environment are becoming absolutely critical.

Why BitRefine

We’re inspired by the idea that our experience and technological innovation will have a profound effect on the lives of millions of people.

  • Successful solutions in precise agriculture are always found at the intersection of several disciplines. BitRefine group relies on knowledge of specialists from various fields, including computer science, life science, agronomists, farmers, and plant scientists.
  • We are continuously learning, updating our knowledge foundation, including into our solutions results of the latest research in the rapidly developing area of computer science.
  • Although our group is totally focused on a very specific area of machine learning and computer vision, we offer complex solutions, including development of interfaces, databases, and integration into existing platforms.

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