Individual Plant Detection on Post-Harvest Forest Floor Using Aerial Imagery

Effectively identifying and locating plants on the forest floor is essential for successful reforestation efforts and forest health assessment. This task is challenging due to the diverse range of plants, the high variance in appearance, the limited availability of accurately labeled data, and the complexity of data collection in post-harvest forest areas. These challenges are addressed by integrating field-surveyed plant data with images captured by Unmanned Aerial Vehicles (UAVs) to produce labeled training data. A custom Faster-RCNN model is trained using the aforementioned data to detect individual plants and clustered plant groups. Two different annotation approaches (unified class and distinct classes) are explored to address plant groupings and compare their performance using Mean Average Precision at 50% overlap (mAP50) and F1 score. The model trained using the unified class approach shows promise in detecting plants and plant groups larger than 0.15 square meters (F1 score: 0.44) but struggles with those smaller than 0.15 square meters. The inclusion of field-labeled data makes detection more challenging but ensures reliability. Meanwhile, the distinct class approach shows limited success. This work underscores the value of high-resolution imagery and comprehensive temporal analysis in enhancing detection accuracy and reliability.