Overview

Traditional agricultural trial data collection relied heavily on pen, paper, and manual measuring tools. This process was tedious, time-consuming, and often led to inefficient and inaccurate results. To address these challenges, our client, a leading multinational AgTech company headquartered in Switzerland sought a fast and reliable digital solution for counting corn seeds.

As a global leader in seed production, their mission is to help farmers around the world meet the challenges of the future by enabling them to grow more with fewer resources. They focus on delivering affordable, simple, and turnkey solutions that drive efficiency and innovation in farming practices.

Key Challenges

Creating a reliable and accurate seed counting application came with several technical challenges that needed to be solved:

• Building the perfect dataset for training the models
• Training and checking different machine learning models for better, decisive results.
• Training the model by changing image resolutions to achieve accuracy.

Solutions

Feathersoft partnered with the client to address key challenges using Agri AI, significantly reducing time and effort while maximizing efficiency. By leveraging cutting-edge technologies, a machine learning model was developed to accurately detect and count individual corn seeds.

• A reliable and quick iOS application was developed to detect seed trait data from the image of the corn.
• With the help of deep learning techniques, we detected every single corn seed on one side of the corn and then calculated the whole seed count in that entire corn.
• A decisive platform was developed that could collect seed trait data at any location without any prior experience.

Impacts and Results

The implementation of the AI-powered seed counting solution brought measurable improvements to the client’s operations:

Seamless Convenience: An iOS device with a stable internet connection will do the complex task of counting seed trait data thus, eliminating the need for manual, time-consuming processes.

Massive productivity gain: What once required extensive time and manual effort can now be completed in seconds, with no need for professional assistance.

Conclusion

By providing more precise trait information, the AI-powered solution plays a crucial role in improving crop yield and overall productivity. Leveraging AI allows trialists to save valuable time and resources, enabling them to focus on other critical aspects of agricultural research and development. This initiative demonstrates how AI has the potential to revolutionize AgTech—making farming smarter, faster, and more efficient for the future.