Enhancing seeding efficiency using a computer vision system to monitor furrow quality in real-time
This work addresses a lack of quantitative assessment methods for row cleaner performance in precision agriculture, which is an incremental improvement for farmers and agricultural engineers.
The researchers tackled the problem of assessing row cleaner performance in precision agriculture by developing a computer vision system to monitor furrow quality in real-time, resulting in an objective quantification method that demonstrated potential for improving row cleaner selection and seeding efficiency.
Effective seed sowing in precision agriculture is hindered by challenges such as residue accumulation, low soil temperatures, and hair pinning (crop residue pushed in the trench by furrow opener), which obstruct optimal trench formation. Row cleaners are employed to mitigate these issues, but there is a lack of quantitative methods to assess trench cleanliness. In this study, a novel computer vision-based method was developed to evaluate row cleaner performance. Multiple air seeders were equipped with a video acquisition system to capture trench conditions after row cleaner operation, enabling an effective comparison of the performance of each row cleaner. The captured data were used to develop a segmentation model that analyzed key elements such as soil, straw, and machinery. Using the results from the segmentation model, an objective method was developed to quantify row cleaner performance. The results demonstrated the potential of this method to improve row cleaner selection and enhance seeding efficiency in precision agriculture.