AgriTech - Assess grain quality using Vision Analytics

Business Impact

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With the growing population, the necessity of tech-oriented innovation in the agriculture industry has become crucial.

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Use of Artificial Intelligence and Machine learning models to replace manual work can solve the current labour shortage in the agriculture industry.

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

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

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

Technology Stack

Solution Overview

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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.

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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.

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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.

Key Features

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

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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.

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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

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50+

AI & ML
Engineers

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40+

AI & ML
Projects for
reputed Clients

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5 yrs

in AI & ML
Engineering

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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