From farming experience
to knowledge we can test.
KishKam Robotics Lab is building farmer-focused tools while researching how local knowledge, scientific evidence and engineering can address agricultural problems.
Respect the practice.
Investigate the outcome.
A traditional practice deserves to be understood in the conditions where farmers use it. We aim to record that context, examine the supporting evidence, and investigate when a practice helps—and when it does not.
Our first product, KishKam Field, focuses on field records and farming activities. The longer-term aim is to use validated knowledge to inform crop investigation, farm economics and agricultural automation.
Kirankumar Malothu
Founder, KishKam Robotics Lab
Field records first, robots second.
Kirankumar completed his Master of Technology (M.Tech) at IIT Madras. His engineering work spans robotics, autonomous navigation and AI systems. Across these areas, one question shapes his approach: what evidence supports a decision, and can someone else trace it?
At KishKam, he brings that question to agriculture, starting with field records that farmers can revisit, before building intelligence or automation on top of them.
Why I’m building KishKam
A season leaves more lessons than memory can hold. The sowing date, the second irrigation, the leaf spot noticed on day 40: without a record, it becomes harder to compare what happened this year with what happened last year.
A chilli farmer I know in Khammam described getting a good yield one year and a poor one the next. He spoke about soil conditions, pests, and unfamiliar pests that left him unsure what to spray or when. That conversation stayed with me. Much of this knowledge stays with individual farmers and is difficult to revisit or share.
That is why KishKam starts with a diary. The diary we are developing is a first step towards recording farming practices and their context. We want to examine traditional knowledge alongside scientific evidence, and build future AI tools that show their sources and uncertainty, including when there is not enough evidence to answer. Robots come later, for tasks farmers tell us they need solved.
I’ll consider KishKam useful when farmers keep choosing it, and their own records help us understand where it made a difference.