Challenge:
Diseases and infestation in orchards often spread through "mummies" — dried, infected fruit that remain on trees. Manual detection is labor-intensive, inconsistent, and difficult in dense canopies, especially across large commercial orchards.
Solution:
We developed a robust object detection system that processes high-resolution 4K images captured in real-world outdoor conditions (variable lighting, weather, foliage density). The AI model accurately identifies mummies despite challenges like occlusion and environmental variation, providing reliable bounding boxes for downstream robotic intervention.
Technical Highlights:
- Real-time analysis of large-scale orchard imagery
- Trained on diverse outdoor datasets for robustness
- Integration-ready output for robotic removal systems
- Developed and supported by our U.S.-based team in Indiana
Results:
- Early, consistent detection of mummies
- Reduced manual labor and scouting time
- Targeted removal to break infection cycles
- Supports sustainable orchard management and higher yields
Ready to Protect Crops with Practical AI?
