2026 Volume 19 Issue 2 Pages 71-78
Lodging is a major obstacle to autonomous harvesting. As a first step toward lodging-aware harvesting, this study presents a method based on unmanned aerial vehicles (UAVs) for detecting rice lodging directions. UAV orthomosaics are divided into tiles and classified into eight directions using pretrained convolutional neural network classifiers. On a mixed-field dataset, the method achieved an overall accuracy of approximately 0.85 and a soft accuracy (allowing for classification into adjacent classes) of over 0.97. On separate-field datasets, overall accuracy decreased to approximately 0.78–0.82 due to spatial domain differences, whereas soft accuracy remained above 0.95. Most misclassifications occurred in adjacent direction classes, indicating practical reliability. The resulting lodging direction maps can be used to plan efficient harvesting paths in lodged fields.
