Harnessing science & technology to improve farming productivity

Agriculture provides a livelihood to approximately 40 percent of Indonesia’s population, however challenges persist in ensuring agriculture productivity and quality towards higher-value-added commodities. In 2017, we built a machine learning model for a farmer co-op to improve farming productivity using the latest technology.

The Challenge

Identifying the crop diseases is the key to prevent the loss in the yield and quantity of the agricultural product. However, conducting manual monitoring requires a tremendous amount of work, expertise in the plant diseases, and also requires excessive processing time. Additionally, most farmers don’t understand specific medication for each plant disease they encounter.

The Solution

We built a machine learning-powered disease detection using image recognition, classification, and segmentation technique.

The Outcome:

  • 95% of Paddy Crop diseases are predicted.
  • Distributed in pilot project to more than 500 rice fields.
  • Featured as a public showcase and reviewed by the President of Indonesia – Joko Widodo.