AgriTech - ML based Grain quality Inspection

Business Case

Farmer's profitability: High-quality grain can fetch better prices and increase a farmer's profitability. Farmers need to ensure that their crops meet quality standards to get the best prices for their crops.

Vendor's reputation: Vendors who sell high-quality grain products can enhance their reputation, attracting new customers and increasing profits. Accurate grain quality assessments can help vendors ensure that they are providing the highest-quality products to their customers.

Compliance with industry standards: Both farmers and vendors need to ensure that they are in compliance with industry standards and regulations, and grain quality assessments can help ensure that they are meeting these standards.

Improved supply chain management: By monitoring and controlling the quality of grain throughout the supply chain, both farmers and vendors can improve their overall supply chain management and reduce the risk of disruptions.

Better decision making: Accurate and consistent grain quality assessments can provide valuable data and insights, which can be used by both farmers and vendors to make informed decisions about grain quality and production processes.

Food safety: Grain quality assessments can ensure that the grain products being sold are safe for consumption and do not pose a health risk to consumers.

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Solution Overview

We have combined image processing and AI model to develop a grain quality estimation model that can be integrated into a mobile application.

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.

The solution will reduce the time taken to segregate the grains based on quality and reduces less manpower than usually required.

The assessed data captured can be used to gain insights over different timespans (e.g., months) to make better business decisions.

Business Impact

Increased accuracy: AI algorithms can provide more accurate and consistent results compared to manual assessments, reducing the risk of errors and inconsistencies.

Increased efficiency: AI-based assessments can be faster and more scalable than manual assessments, allowing businesses to process more grain in less time and with less labor.

Cost savings: By automating the grain quality assessment process, businesses can reduce the costs associated with manual assessments, such as labor costs and the cost of errors and inconsistencies.

Improved data analysis: AI-based assessments can generate vast amounts of data, which can be analyzed and used to make informed decisions about grain quality and production processes.

Better compliance: AI-based assessments can ensure that businesses are in compliance with industry standards and regulations by providing accurate and consistent results.

Enhanced reputation: By providing high-quality grain products, businesses can enhance their reputation and attract new customers, leading to increased profits and growth.

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Technology Stack

Key Features

Reduces the time taken to segregate the grains based on quality.

The assessed data can be logged in some datasets to gain insights over different timespans, such as months, seasons, etc to make better business decisions.

The platform can be completely cloud-native. The ML model can be offered as an API service to the end-user and can be integrated into any device.

Trusted and Proven Engagement Model

A nondisclosure agreement (NDA) is signed to not disclose any sensitive information revealed over the course of doing business together.
Our NDA-driven process is established to keep clients’ data and IP safe and secure.

The solution discovery phase is all about knowing your target audience, writing down requirements, and creating a full scope for the project.
This helps clarify the goals, and limitations, and deliver quality products & services.

Our engagement model defines the project size, project development plan, duration, concept, POC etc.
Based on these scenarios, clients may agree to a particular engagement model (Fixed Bid, T&M, Dedicated Team).

The SOW document shall list details on project requirements, project management tools, tech stacks, deliverables, milestones, timelines, team size, hourly/monthly rate cards, billable hours and invoice details.

On signing the SOW, an official project kick-off meeting shall be initiated.
Our implementation approach, ecosystem, tools, solutions modelling, sprint plan, etc. shall be discussed during this meeting.

Our Award-Winning Team

A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0

50+

AI & ML
Engineers

40+

AI & ML
Projects for
reputed Clients

5 yrs

in AI & ML
Engineering

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