Tree Health Assessment from UAV Images: Improving Object Detection and Classification Using Hard Negative Mining and Semi-Supervised Autoencoder

Orchard tree inventory has been an essential step to obtain up-to-date information for effective tree treatments and crop insurance purposes. Inventorying trees is often performed manually through fieldwork surveys, which are generally time-consuming, costly, and subject to errors. Motivated by the latest advances in UAV imagery and deep learning, we propose a new framework for individual tree detection and health assessment. We adopt a divide-and-conquer approach to address the problem of orchard trees' health assessment in two stages. First, we build a tree detection model based on a hard negative mining strategy to improve object detection. In the second stage, we address the health classification problem using a new convolutional autoencoder architecture mainly designed to extract relevant features. The performed experiments demonstrate the robustness of the proposed framework for orchard tree health assessment from UAV images. In particular, our framework achieves an F1-score of 86.24% for tree detection and an overall accuracy of 98.06% for tree health assessment. Moreover, our work could be generalized for a wide range of UAV applications involving a detection/classification process.