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Trrimber : AI-Powered Mobile App for Efficient Wood Log Volume Measurement

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Year

2025

Industry

AI

Team

Technology

Services

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Trrimber is a mobile application designed to streamline and digitize the process of measuring wood log volumes using AI and image processing. Targeted at forestry professionals and log traders, the app serves as a Minimum Viable Product (MVP) focusing on automating log counting, measuring diameters, and calculating volume using camera input and manual data. It is cross-platform, offline-capable, and built with an intuitive interface for field use.

The problem

Accurately measuring the volume of wood logs in the field is a time-consuming and error-prone task. Manual methods for counting logs, estimating diameters, and calculating volume are inefficient and inconsistent. Moreover, field workers often lack tools for organizing and sharing this data effectively. In many scenarios, assumptions based on visual estimates or manual calculations lead to significant inaccuracies, causing losses in trade value and operational delays.

The solution

Trrimber solves this by enabling users to capture photos of wood log piles directly through the app or upload HD images for processing.

  • The app uses computer vision to identify and count logs, calculate individual and average diameters (with and without bark), and estimate volume using advanced mathematical models. 
  • Users can capture both sides of the pile for greater accuracy or proceed with one-side imagery with predefined assumptions.
  • The app allows for manual selection/deselection of logs, input of parameters like log length, species, stack number, and geolocation, and displays results in a clear format.
  • All data can be saved offline and exported as CSV for reporting or shared via WhatsApp.

Results

With Trrimber, field professionals can now generate accurate log volume reports in minutes instead of hours. Users gain control over data precision through manual adjustments and multiple photo angles. The app delivers reliable results in offline environments, making it ideal for forest use. It improves operational efficiency, reduces manual errors, and supports better decision-making through structured digital data. The exported reports include log count, average diameter, length, volume, species, location, and other metadata—ready for professional use and sharing.

 

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