Fields are the atomic units of agriculture. Generating agricultural insights at an individual field level is critical to a meaningful change in the agri ecosystem.
Diversity of landscape and crops lead to very different requirements between two fields in proximity to each other
Currently, insights are available at an aggregate level, but the intervention and advisory is needed at an individual or farm level
Leveraging high resolution satellite imagery, Google Maps corpus and bespoke ML models
Agriculture Landscape Understanding
(Preview)
Landscape understanding leverages satellite imagery and machine learning to draw agricultural boundaries of fields, the basic unit of agriculture and essential in creating meaningful insights. With field segments established, the model can determine the acreage of farm fields. Similarly other landscape elements like water bodies and vegetation can be identified, which can help with drought contingency planning.
Read our Research paper
Agricultural Monitoring and Event Detection
For the identified agricultural fields, via AMED API we provide historical and current in-season crop monitoring. AMED builds on ALU, and can be queried the same way ALU is. Predicted data is organized at field level in chronologically ordered crop seasons containing the predicted crop labels.
Read our Research paper
Our Partners
Partnerships with state governments, academic institutions and agritech enterprises
Rama Devi Lanka
Director-Emerging Technologies ITE&C Department | Government of Telangana
Sibi Prabhakaran
CEO’s Office @ Ninjacart
Krishna Chaithanya
Founder & CEO at Team-Up!
Yogesh Patil
CEO at Skymet
Milind Sohoni
Teacher at Indian Institute of Technology, Bombay