Krishna Kumar's built a global agri cloud

➤ Following his engineering degree in 2006, Krishna Kumar worked for over four years at GE’s innovation centre in Bengaluru, across its appliances, industrial and energy divisions. Data analysis was a key part of his role. The teams he was part of implemented Six Sigma methodologies to enhance factory profitability through datadriven decisions.➤ This was also a time when he noticed news reports about challenges faced by agricultural communities, about farmer suicides, about yields being significantly lower than those in developed countries. And he thought it could be possible to improve agri efficiency through data insights. It was with that idea that he founded Cropin in 2010. “During my research, I observed a concerning pattern where farmers had to discard their produce, particularly tomatoes, due to prices falling to 30 paise, while urban consumers were paying upwards of Rs 10. Overproduction led to price collapses, creating a vicious cycle of debt for farmers. I wondered if we could enhance data intelligence in the sector and make it practical and user-friendly,” he says.➤ Today, Cropin is genAI-powered agri-intelligence platform, featuring a micro language model, for climate-smart agriculture. It delivers agricultural insights, precise data, and farming guidance. The system can provide, for instance, recommendations for agri inputs suitable for specific crops under different agro-climatic conditions.➤ The platform has enabled digital transformation across 30 million acres of agri land, benefiting over 7 million farmers globally. The crop knowledge database encompasses 500 crops and 10,000 varieties spanning 103 countries, serving as the foundation for Cropin Cloud. The intelligence framework incorporates data from more than 1 billion acres of cultivable land worldwide.➤ Recently, Cropin collaborated with Walmart to enhance scalable sourcing solutions across its US and South American markets. Cropin’s platform helps optimise Walmart’s fresh produce supply chain by improving yield forecasting, monitoring crop health, and predicting seasonal transitions with greater accuracy.