Honor:2024 Agricultural Industry Science and Technology Project
Scenario:
At the Amano shrimp shipping stage, traditional manual visual estimation of shrimp quantities is time-consuming and often subjective, lacking objective scientific tools for accurate quantification. This makes it challenging to provide precise shipping measurement evidence.
In this project, Gordonsmart utilizes the latest generation of AI recognition model and a comprehensive collection of Amano shrimp photos to develop an Amano Shrimp AI counting model. This model assists on-site farming staff conduct daily inventory and shipping counts more efficiently, reducing errors in shipping quantities and controlling the volume of incremental shipments. It focuses on effectively managing potential damage during transportation, thereby enhancing cost-effectiveness and profitability.
The application of this technology not only improves operational efficiency during the farming process but also enhances the accuracy and reliability of shipping data, helping aquaculture industry maintain a competitive advantage in the market.
