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Accurate and efficient orchard tree inventories are essential for acquiring up-to-date information, which is necessary for effective treatments and crop insurance purposes. Surveying orchard trees, including tasks such as counting, locating, and assessing health status, plays a vital role in predicting production volumes and facilitating orchard management. However, traditional manual inventories are known to be labor-intensive, expensive, and prone to errors. Motivated by recent advancements in UAV imagery and computer vision methods, we propose a UAV-based computer vision framework for individual tree detection and health assessment. Our proposed approach follows a two-stage process. Firstly, we propose a tree detection model by employing a hard negative mining strategy using RGB UAV images. Subsequently, we address the health classification problem by leveraging multi-band imagery-derived vegetation indices. The proposed framework achieves an F1-score of 86.24% for tree detection and an overall accuracy of 97.52% for tree health assessment. Our study demonstrates the robustness of the proposed framework in accurately assessing orchard tree health from UAV images. Moreover, the proposed approach holds potential for application in various other plantation settings, enabling plant detection and health assessment using UAV imagery. [ABSTRACT FROM AUTHOR]

Highlights • A novel computer vision system for intensive care respiratory monitoring. • An efficient positioning for the cameras in an intensive care room is proposed. • Our system offers a very high spatial coverage of the thoraco-abdominal zone. • Our system is evaluated by comparison to a mechanical ventilator. • The experiments demonstrate a high performance in estimating breathing parameters. Abstract Assessment of respiratory activity in pediatric intensive care unit allows a comprehensive view of the patient's condition. This allows the identification of high-risk cases for prompt and appropriate medical treatment. Numerous research works on respiration monitoring have been conducted in recent years. However, most of them are unsuitable for clinical environment or require physical contact with the patient, which limits their efficiency. In this paper, we present a novel system for measuring the breathing pattern based on a computer vision method and contactless design. Our 3D imaging system is specifically designed for pediatric intensive care environment, which distinguishes it from the other imaging methods. Indeed, previous works are mostly limited to the use of conventional video acquisition devices, in addition to not considering the constraints imposed by intensive care environment. The proposed system uses depth information captured by two (Red Green Blue-Depth) RGB-D cameras at different view angles, by considering the intensive care unit constraints. Depth information is then exploited to reconstruct a 3D surface of a patient's torso with high temporal and spatial resolution and large spatial coverage. Our system captures the motion information for the top of the torso surface as well as for its both lateral sides. For each reconstruction, the volume is estimated through a recursive subdivision of the 3D space into cubic unit elements. The volume change is then calculated through a subtraction technique between successive reconstructions. We tested our system in the pediatric intensive care unit of the Sainte-Justine university hospital center, where it was compared to the gold standard method currently used in pediatric intensive care units. The performed experiments showed a very high accuracy and precision of the proposed imaging system in estimating respiratory rate and tidal volume. [ABSTRACT FROM AUTHOR]