Computer Vision Based Grain Quality Estimation Solution for AgriTech -Overview

• Computer vision is one of the most well-liked fields at the moment. It is used to read text, understand signs, received official and objects, evaluate and divide scenes, among other fascinating tasks.
• Another field of interaction where computer vision has a lot of potential is in agriculture. Several aspects of agricultural supply chains need to be automated as they involve so many extraneous parts.
• Several of these processes, notably seed identification, crop disease detection, and drone monitoring for yield estimation, can be handled with computer vision. These are pre-harvest issues, but computer vision has an interesting application that has a great deal of potential even after the grain are harvested.

• A farmer goes to a dealer to sell the wheat they have gathered. If all goes according to plan, a technician should take a sample from the farmer’s numerous sacks and manually classify this according to regulatory criteria before deciding the total quality of the food.
• This technique of estimating quality is known as assaying. Manual analysis of a 50g sample takes more than 15 minutes. Additionally, it receives hundreds of samples all throughout busy periods of the year.
• This is rarely done since it is impossible to hand analyse every sample. As a result of not realizing the grade of their goods, farms are unable to bargain.
• While there are many other factors, such as inadequate storage for farmers’ produce and the innovation of trade cartels within and between traders, which will give farmers at least some bargaining power and give traders some assurance that they won’t pay a premium for goods, a transparent and quick quality estimation system is still necessary.

Benefits of Grain Quality test

• Testing for Better Grain Marketing.
• Avoiding Unnecessary Costs.
• Improving Industry Standards
• User-friendly Testing Technology
• Bring Grain Testing Back to the Farm

How we helped Agritech industry with our Computer Vision Based Grain Quality Estimation Solution.

Solution overview

• To assess the quality of an Agriculture yield there is a need for manual labor where a human must take a look at the overall yield to find the ratio of healthy against the total number of grains where grains could be damaged, broken, foreign matter, etc.
• With the help of Image segmentation, the model can extract individual grains from a heap and then classify each grain based on the classes provided.
• By taking out a small heap from a sack, and then capturing a picture from the mobile, the platform will get the required healthy grain ratio as well as provide details for each type of grain.

Business Value

Technology

Market size: Grain Quality

Grain Analysis Market size was valued at USD 1.95 Billion in 2019 and is projected to reach USD 2.92 Billion by 2027, growing at a CAGR of 5.6% from 2020 to 2027.