We developed an intelligent weed detection system to perform a selective application of herbicides at any stage of the soybean crop growth. It allows savings up to 90% in agrochemicals depending to the amount of weeds present in the lot.
It can be installed in any sprayer, to open and close the applicator nozzles automatically in real time. Independent modules with a working width of 2 meters are sold, which consist of a camera, a computer and an individual electrovalves for each nozzle. Depending on the amount of weeds present in the lot, the herbicide savings can vary between 70% and 90%.
Business flyer Buy SprAIDeepAgro's potential lies in the possibility of continuing developing artificial intelligence solutions for the farmer. From generation of weed maps, crop health, etc., to intelligently combat pests, where agrochemicals are more toxic to humans.
It is possible to differentiate green from green and detect weeds at any growth stage of the soybean and corn crops.
A key difference against competitors is that our system has been developed to work in no till production systems, where recognition conditions are tougher.
Allow high technology CPP (Crop Protection Product) at lower cost than low technology CPP in broadcasting application, reduce phytotoxicity on crops and improve yields.
Our system is competitively fast, providing high precision up to 18 km/h spraying speed, and it works day and night by means of LED lighting.
We have one of the most competitive prices in the market and payback in 1 year for producers of 2000 hectares or more.
We have developed an after-sales network in the main areas of the country to be able to serve all producers and our devices are easy to replace, allowing the producer to be always operational.
Contact our distributor or sprayer manufacturer partners to learn about our financing options.
Write to us to join a demonstration event, where you will be able to see our technology at work and get all your doubts out of the way.
At DeepAgro we have developed our own weed recognition algorithms based on Deep Learning that are robust to agricultural conditions in different countries and situations.
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