Problem/Objective:

In agriculture, currently, quality assaying is done manually, which makes the results subjective, time-consuming, and open to malpractices. There is a need for an easy-to-use, scalable, and reliable quality assaying system that can be used at all national mandis. Labs and machines are expensive and have limited availability, which is why it is imperative to get quality results in a digital format which can then be uploaded on the eNAM platform. 

Solution/Approach:

Intello Labs is an agri-tech start-up that uses AI tools, including computer vision and deep learning, to build a platform for grading and quality monitoring of agricultural commodities. Intello Labs provides an image-based solution delivered through a smartphone app, which helps in bringing transparency and standardisation to quality assessment and reducing value risk and wastage in agriculture supply chains. 

Intello Labs has developed an application that tests, grades, and analyses visual quality parameters of agricultural products. Currently, they offer services for testing and grading of wheat, tomatoes, potatoes, onions, and cardamom. They are in the process of adding six to seven more commodities to their portfolio, including coffee, tea, and grapes. 

Impact/Implementation:

The solution has reduced the quality testing time from 15 minutes to 2 minutes and provides quality results with more than 95% accuracy. It allows real-time data sharing across multiple locations and screens, standardises quality assessment, and removes subjectivity. 

Source: NASSCOM COE-DSAI AI for Good report