Orchard Apple Tree Health Assessment using UAV Imagery-Based Computer Vision System

Accurate and efficient orchard tree inventories play a crucial role in obtaining up-to-date information for effective treatments and crop insurance purposes. Surveying orchard trees, including counting, locating, and assessing their health status, is vital for predicting production volumes and facilitating orchard management. However, traditional manual inventories are labor-intensive, expensive, and prone to errors. Motivated by the recent advances in UAV imagery and computer vision methods, we propose a new framework for individual tree detection and health assessment. The proposed approach follows a two-stage process. First, we build a tree detection model based on a hard negative mining strategy using RGB UAV images. In the second stage, we address the health classification problem using two methods. We present a classical machine learning approach by exploring the use of multi-band imagery-derived vegetation indices. We also propose a new convolutional autoencoder-based architecture mainly designed to extract the relevant features for tree health classification. 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

Design and Rationale of the National Tunisian Registry of Heart Failure (NATURE-HF): Protocol for a Multicenter Registry Study

BackgroundThe frequency of heart failure (HF) in Tunisia is on the rise and has now become a public health concern. This is mainly due to an aging Tunisian population (Tunisia has one of the oldest populations in Africa as well as the highest life expectancy in the continent) and an increase in coronary artery disease and hypertension. However, no extensive data are available on demographic characteristics, prognosis, and quality of care of patients with HF in Tunisia (nor in North Africa). ObjectiveThe aim of this study was to analyze, follow, and evaluate patients with HF in a large nation-wide multicenter trial. MethodsA total of 1700 patients with HF diagnosed by the investigator will be included in the National Tunisian Registry of Heart Failure study (NATURE-HF). Patients must visit the cardiology clinic 1, 3, and 12 months after study inclusion. This follow-up is provided by the investigator. All data are collected via the DACIMA Clinical Suite web interface. ResultsAt the end of the study, we will note the occurrence of cardiovascular death (sudden death, coronary artery disease, refractory HF, stroke), death from any cause (cardiovascular and noncardiovascular), and the occurrence of a rehospitalization episode for an HF relapse during the follow-up period. Based on these data, we will evaluate the demographic characteristics of the study patients, the characteristics of pathological antecedents, and symptomatic and clinical features of HF. In addition, we will report the paraclinical examination findings such as the laboratory standard parameters and brain natriuretic peptides, electrocardiogram or 24-hour Holter monitoring, echocardiography, and coronarography. We will also provide a description of the therapeutic environment and therapeutic changes that occur during the 1-year follow-up of patients, adverse events following medical treatment and intervention during the 3- and 12-month follow-up, the evaluation of left ventricular ejection fraction during the 3- and 12-month follow-up, the overall rate of rehospitalization over the 1-year follow-up for an HF relapse, and the rate of rehospitalization during the first 3 months after inclusion into the study. ConclusionsThe NATURE-HF study will fill a significant gap in the dynamic landscape of HF care and research. It will provide unique and necessary data on the management and outcomes of patients with HF. This study will yield the largest contemporary longitudinal cohort of patients with HF in Tunisia. Trial RegistrationClinicalTrials.gov NCT03262675; https://clinicaltrials.gov/ct2/show/NCT03262675 International Registered Report Identifier (IRRID)DERR1-10.2196/1226