Agrograde, a physical AI startup building technologies to make fresh produce packhouses autonomous, has introduced India’s first AI-powered optical sorting system developed specifically for arecanut.
The first commercial deployment of the Agrograde system has been installed at Vikas Traders in Mangalore, Karnataka, targeting quality consistency and labour challenges in the arecanut supply chain.
The Agrograde system can process up to approximately one tonne of arecanut per hour, enabling the Mangalore facility to process close to eight tonnes a day.
The technology uses computer vision and AI to inspect individual nuts, identify defects and automatically separate rejected material from accepted produce.
The system is designed to address a persistent challenge in the arecanut supply chain: maintaining consistent quality while sorting and processing large volumes of nuts.
Unlike conventional colour-sorting machines that primarily identify differences in colour, Agrograde uses computer vision and AI to inspect individual arecanuts for external defects that may not be distinguishable through colour alone.
“Our machines are designed after studying multiple characteristics of a crop. We study how a product rolls, glides, the residue it leaves, the amount of dust that comes with the product, the amount of force that is needed to gently sort the product, and the variations across different varieties. We also understand the current process to understand the nuances in quality assessment.
We have a dedicated team that conducts this research which enables us to develop one of the best AI powered optical sorters rivaling even imported technologies. It took us almost 5 months and 3 product iterations to build India’s first autonomous optical sorter for Arecanuts.
This AI sorter has made warehouse operations almost completely autonomous. We are also grateful to the team at Vikas traders for trusting us and helping us customize our technology for the Arecanut supply chain.,” said Rakeshkumar Barai, Co-Founder, Agrograde.
Agrograde’s Vector Architecture for Arecanut Sorting
The machine’s design is based on Agrograde’s Vector architecture, which is designed to ensure high surface coverage when an object moves through the vision system. The architecture also handles significant variations in the size and shape of produce.
Aniket Ghadage, Head of Product Engineering, said that Agrograde’s Vector architecture follows a design philosophy intended to make sophisticated machines easier to operate and maintain.
The system is also designed to operate in challenging environmental conditions, including heat, dust and humidity.
The technology was developed over several months in collaboration with Vikas Traders, a Mangalore-based agri-business with decades of experience in the arecanut trade.
The collaboration focused on adapting the technology to the crop’s specific characteristics and the operating conditions of an actual arecanut facility, including dust, foreign matter and high humidity.
When initially commissioned, the Agrograde arecanut sorting system recorded approximately 75% sorting accuracy. With more commercial data becoming available from operations, the AI models have since been updated, taking accuracy to approximately 89%, with further improvements expected as the system continues to learn from additional data.
Agrograde Automation Replaces Manual Sorting Process
Before automation, Vikas Traders relied heavily on manual inspection and sorting. According to the company, an individual worker could manually inspect and sort roughly two bags a day, while a team of around 15 people could process approximately 40–45 bags a day.
The availability of skilled manpower also influenced how quickly consignments could be inspected, packed and dispatched. The Agrograde automated system now provides a more predictable processing capacity for the facility.
The system also generates a quality summary after every lot. The report includes information on size distribution, identified defects, defect intensity and the number of nuts packed, providing the business with additional data to monitor quality.
Raj Padaliya from Vikas Traders said, “I was curious to see if AI and robotics can be deployed in the arecanut industry. I was looking for technologies which could make our entire warehouse autonomous and fast. We were excited to know that there is an Indian startup working on this, and we worked with them closely to try out multiple versions.
Finally, we had success and now our sorting process is almost completely autonomous. We are the first private company in the Arecanut industry to adopt such sophisticated and high-level A.I. technology. I’m amazed at the level of hardware development that Indian startups are doing.”
How Agrograde AI Optical Sorter Works
The Agrograde system uses the company’s patent-pending Vector technology to capture 10–12 images of each nut. Its proprietary AI models analyse the images within milliseconds and identify visible defects before the sorting mechanism separates rejected material from the accepted lot.
The AI models have been trained using lakhs of crop images and run on high-performance computing infrastructure powered by NVIDIA.
According to Agrograde, its computer vision and AI technology enables the system to inspect individual arecanuts rather than relying primarily on colour differences. This allows the system to identify external defects that may not be distinguishable through colour alone.
Agrograde Expands AI and Physical Automation Across Agriculture
Agrograde has been developing AI, computer vision and physical automation solutions for Indian post-harvest operations across crops including onion, potato and apples.
The company recently deployed its 150th machine across 15 Indian states. The arecanut deployment marks Agrograde’s expansion into a crop where quality assessment has traditionally depended heavily on manual inspection.
About Agrograde
Agrograde is a physical AI startup building technologies that make fresh produce packhouses autonomous. Its technology combines computer vision, AI and robotics to automate processes such as grading, sorting, weighing and packing across agricultural supply chains.
The company works with packhouses, traders, exporters, cold-storage operators and food processors to improve processing efficiency and quality consistency.
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