This article introduces a comprehensive dataset designed to facilitate smart harvesting applications for Deglet Nour date palms, focusing on two primary computer vision tasks: branch detection and optimal cutting point localization. Collected from various oases within the Kebili governorate of southern Tunisia during the peak harvesting season of 2023, the dataset consists of 5530 images annotated for object detection and 387 images precisely annotated with keypoints. Captured using a combination of DSLR and smartphone cameras, the dataset captures real- world agricultural complexities, including varied lighting conditions and occlusions, which were covered and uncorvered date bunches. Annotations, performed manually using Roboflow, are provided in widely used YOLO and COCO formats to ensure compatibility and facilitate widespread adoption. By offering structured, high quality annotated images, this dataset supports the robust development, training, and evaluation of advanced computer vision and robotic harvesting systems for precision agriculture.

The present paper employs fuzzy logic to control the speed of a mobile robot to ensure its safe and efficient navigation in complex and dynamic environments. We discussed the main components of the fuzzy control system, including fuzzification, inference rules, and defuzzification concerning the dynamic, kinematic, and topography models of the robot. The simulation results show the effectiveness of the proposed fuzzy controller in ensuring adaptive and collision-free robot navigation, contributing then to the advancement of intelligent robots and their use in diverse applications.

Most studies on power generation systems have focused on individual components, leaving researchers without a comprehensive understanding of integrated systems. This study addresses this gap by developing a fully self-sufficient photovoltaic (PV) system for a 12 V autonomous mobile robot. We calculated the power and energy requirements of the robot to appropriately size the PV panel and designed a DC–DC buck converter. For energy storage, we used a Li-ion battery and a bidirectional current converter to manage the charging and discharging processes. To optimize performance, we implemented Maximum Power Point Tracking (MPPT) using a fuzzy logic-based control method. Simulation results demonstrate that the proposed fuzzy MPPT controller outperforms the classical P&O algorithm by achieving up to 98% MPPT efficiency, reducing power ripple by over 50% , and improving convergence speed by approximately 30% . The system maintains the output voltage regulated within ±1% of the 12 V target across all tested irradiance and load conditions, confirming both high tracking accuracy and robust stability. During optimal irradiance, the system can simultaneously power the robot and charge the battery, while under low-irradiance conditions, the battery ensures uninterrupted operation. These findings confirm that the proposed hybrid PV system enables mobile robots to operate autonomously and reliably regardless of weather conditions, providing a practical solution for continuous power supply in robotic applications.

Ultraviolet (UV) disinfection is widely used in water treatment; however, its effectiveness strongly depends on water optical quality (e.g., turbidity, total dissolved solids, and UV transmittance, UVT). This study investigates an integrated RO–UV scheme in which reverse osmosis (RO) pretreatment improves UVT and thereby increases the effective UV dose available for microbial inactivation. First, UV-only reactor performance is characterized using literature data to fit an intensity-specific dose response relationship. The RO contribution is then incorporated at the process level through a UVT based coupling and evaluated using deterministic low/central/high scenarios (p05/p50/p95) constructed from assumed input ranges. Finally, a multi-objective optimization solved with the Grey Wolf Optimizer (GWO) is used to identify operating conditions that maximize predicted bacterial log-inactivation while limiting a UV-equivalent energy proxy based on nominal UV dose. Across the investigated flow-rate and intensity ranges, RO pretreatment yields a systematic increase in effective dose (median gain $\approx 6.8\%$) and a corresponding improvement in predicted inactivation, with the marginal benefit depending on the dose response regime.