Landslides are among the most relevant and potentially damaging natural risks, causing material and human losses. The department of Aube in France is well known for several major landslide occurrences. This study focuses on the assessment of Landslide Susceptibility (LS) using the Frequency Ratio (FR) as a statistical method, the Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) method, and Random Forest (RF) and k-Nearest Neighbor (kNN) as machine learning methods in the Aube department, northeast of France. Subsequently, the thematic layers of eight landslide causative factors, including distance to hydrography, density of quarries, elevation, slope, lithology, distance to roads, distance to faults, and rainfall, were generated in the geographic information system (GIS) environment. The thematic layers were integrated and processed to map landslide susceptibility in the study area. On the other hand, an inventory of landslides was carried out based on the database created by the French Geological Survey (BRGM), where 157 landslide occurrences were selected, and then RF and kNN models were trained to generate landslide maps (LSMs) of the study area. The generated maps were assessed by using the Area Under the Receiver Operating Characteristic Curve (ROC AUC). Subsequently, the accuracy assessment of the FR model revealed more accurate results (AUC = 66.0%) than AHP, outperforming the latter by 6%, while machine learning models results showed that RF gave better results than kNN (<7.3%) with AUC = 95%. Following the analysis of LS mapping results, lithology, distance to the hydrographic network, distance to roads, and elevation were the four main factors controlling landslide susceptibility in the study area. Future mitigation and protection activities within the Aube department can benefit from the present study mapping results, implicating an optimized land management for decision-makers.

Accurate citrus fruit yield and estimation is of utmost importance for precise agricultural management. Unmanned aerial vehicle (UAV) remote-sensing systems present a compelling solution to this problem. These systems capture remote-sensing imagery with both high temporal and spatial resolution, thus empowering farmers with valuable insights for better decisionmaking. This research assessed the potential application of UAV imagery combined with the YOLOv7 object detection model for the precise estimation of citrus yield. Images of citrus trees were captured in their natural field setting using a quadcopter-mounted UAV camera. Data augmentation techniques were applied to enhance the dataset diversity; the original YOLOv7 architecture and training parameters were modified to improve the model’s accuracy in detecting citrus fruits. The test results demonstrated commendable performance, with a precision of 96%, a recall of 100%, and an F1-score of 97.95%. The correlation between the fruit numbers recognized by the algorithm and the actual fruit numbers from 20 sample trees provided the coefficient R2 of 0.98. The strong positive correlation confirmed both the accuracy of the algorithm and the validity of the approach in identifying and quantifying citrus fruits on sample trees.