Automated bag counting uses computer vision and deep learning to track and count bags in real-time during mill operations. By deploying cameras with AI models that detect bags crossing virtual counting lines, mills achieve 99.9% counting accuracy, reduce manual labor costs by 70%, and improve inventory management efficiency through automated, error-free tracking of bag movements.
Achieved 99.9% counting accuracy
Reduced manual labor costs by 70%
Improved inventory management efficiency
Overcoming Manual Counting Challenges in High-Volume Grain Handling. The mill faced significant challenges in accurately counting the number of bags being transferred between trucks and storage areas (godowns). Relying on manual counting methods not only increased the likelihood of human error but also slowed down the unloading process. This led to discrepancies in inventory records and potential losses. The need for an automated solution that could provide real-time, accurate counts without interrupting the workflow was evident.
While our advanced solutions like automated bag counting in industrial settings showcase the power of AI, we also help businesses solve simpler yet impactful problems using the same core technology. Here are a few examples of straightforward AI-driven use cases we can implement quickly and cost-effectively:
