Weather Analytica team is developing technique known as transfer learning to teach the AI to recognize crop disease and pest damage. Our company want to work with central/state government/farmers on pilot project on getting leaf sampling for all types of crop (cotton, maize, wheat etc). We are using Google’s open source library (TensorFlow) to build a library of AI 2500 images of cotton leafs. Recent studies show that the success was that the AI was able to identify a disease with 98% accuracy.

Our team consist of atmospheric, hydrological scientist, and data scientists to provide AI solution for Agriculture applications. The main objectives of our company to reach out every single farmer for improving crop yield production, more crop production means farmers will become economically stronger, and reduce hunger problems in the world. The AI can be very useful in agriculture practices and here are some important areas where the use of AI can benefit agriculture

  1. Automation techniques enabling farmers to sowing seed, irrigation and harvesting at right stage of the crop. And this is possible with Automation algorithm connecting between soil condition, temperature, humidity, rain parameters and what types of crop can be grown in various ambient environment of nature so that we can maximize crop yield.
  2. With the few leaf samples, Machine learning automation images (Image processing techniques) can detect infestation at early stage and can be subject for immediate pesticide spray with drone technology.

Our team is developing a cloud-based solution that aggregates all existing data that farmers have like soil/water sensors, aerial images and so on. It then combines it with an in-field device that makes sense of it all. The inputs from the various souse are used to find a correlation between different data labels and make predictions.