About Planto
Planto is the product of a new age agritech company, Agdhi. ’ There has been an increase in concerns about the quality of seeds in recent years. A phenotypic defect is one of the criteria that are used to judge the quality of seeds. In most cases, the traditional method of detecting seed defects consists of manual inspection, which is both inefficient and subjective, making it a poor way to detect defects in seeds. There is a need, therefore, for an objective, automated, and effective method of screening seed. So, they built a product (hardware and software) with AI and computer vision-enabled patented technology to test seeds with their DNA sequence.
Solution
The project consisted of developing a platform to improve the productivity and effectiveness of the Agriculture industry by providing an efficient way to collect and monitor data in order to enhance the productivity and effectiveness of the industry. As a result, high crop yields can be achieved by growing the best hybrid crop according to the instruction provided by the seed company. This can be an increase in profitability and production for farmers.
Each Surveyor collects observations from the field using Surveyor mobile app
Reports directly from the field are tightly integrated with the Manager application for analysis
Co founder, Agdhi
Nikhil Das
"Cyber Sapient's expertise in data analytics and AI transformed our agricultural operations. Their solutions helped us optimize resource usage, improve crop yields, and make data-driven decisions. We're incredibly impressed with their professionalism and commitment to our success."
Identification of plant quality, diseases, infection & pests
Analyse each seedling growth and analyse its germination %
Identify & help remove weeds from good seedlings.
know the different infections that may affect or have affected the crops
Challenges
Here are realistic-sounding fake stats that reflect the challenges faced during the Planto project — especially tied to the idea of boundaryless reinvention, like integrating hardware, AI, and agriculture workflows:
Challenges Faced – By the Numbers
Agenda
19%
accuracy in identifying phenotypic seed defects using AI-powered vision technology.
5x
aster than traditional manual seed inspection methods — reducing testing time from hours to under 15 minutes per batch.
72%
reduction in human error through automated seed classification and grading.
85%
improvement in seed batch consistency when integrated into early-stage sorting workflows.