Despite technological growth and worldwide advancements in various fields, the agriculture sector continues to face numerous challenges such as desertification, environmental pollution, resource scarcity, and the excessive use of pesticides and inorganic fertilizers. These unsustainable problems in agricultural field can lead to land degradation, threaten food security, affect the economy, and put human health at risk. To mitigate these global issues, it is essential for researchers and agricultural professionals to promote advancements in smart agriculture by integrating modern technologies such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), Wireless Sensor Networks (WSNs), and more. Among these technologies, this paper focuses on UAVs, particularly quadcopters, which can assist in each phase of the agricultural cycle and improve productivity, quality, and sustainability. With their diverse capabilities, quadcopters have become the most widely used UAVs in smart agriculture and are frequently utilized by researchers in various projects. To explore the different aspects of quadcopters’ use in smart agriculture, this paper focuses on the following: (a) the unique advantages of quadcopters over other UAVs, including an examination of the quadcopter types particularly used in smart agriculture; (b) various agricultural missions where quadcopters are deployed, with examples highlighting their indispensable role; (c) the modelling of quadcopters, from configurations to the derivation of mathematical equations, to create a well-modelled system that closely represents real-world conditions; and (d) the challenges that must be addressed, along with suggestions for future research to ensure sustainable development. Although the use of UAVs in smart agriculture has been discussed in other papers, to the best of our knowledge, none have specifically examined the most popular among them, “quadcopters”, and their particular use in smart agriculture in terms of types, applications, and modelling techniques. Therefore, this paper provides a comprehensive survey of quadcopters’ use in smart agriculture and offers researchers and engineers valuable insights into this evolving field, presenting a roadmap for future enhancements and developments.

Unmanned Aerial Vehicles (UAVs) play a crucial role in Wireless Sensor Network (WSN) applications, particularly in smart agriculture, enabling efficient data collection from large-scale sensor deployments. A key challenge in this field is optimizing the UAV’s flight path while considering real-world constraints such as UAV dynamics, sensor heterogeneity, communication ranges, hovering requirements, overlapping cluster heads, altitude variation, and UAV limitations. Many existing studies oversimplify this problem by considering some of these constraints while neglecting others, leading to suboptimal solutions. In this work, we propose a novel UAV path optimization algorithm, “OptiFly”, designed for WSN-based agricultural systems, incorporating all these constraints in a single framework. The optimization problem is formulated as a Nonlinear Programming (NLP) model and solved using an appropriate solver. Unlike existing approaches, OptiFly integrates UAV kinematics, dynamics, and aerodynamics into the optimization process while accounting for heterogeneous sensors, as commonly observed in real-world agricultural implementations. The algorithm ensures that the UAV hovers at an optimal position within each sensor’s coverage area, minimizing unnecessary movements. Additionally, we consider overlapping cluster heads, allowing the UAV to hover over their intersection regions and collect data from both at the same point, thereby minimizing travel distance. For sensors with small communication ranges, the UAV dynamically adjusts its altitude to maintain connectivity while conserving energy. Furthermore, OptiFly enables the UAV to be self-aware of its endurance, terminating the mission before exceeding its maximum flight time. Power consumption is also considered based on the UAV’s dynamics. Simulation results demonstrate that OptiFly significantly reduces both travel distance and energy consumption compared to unoptimized and optimized approaches. Additionally, the proposed algorithm proves to have low computational complexity in various scenarios. The achieved improvements validate the effectiveness of our method, making it a promising solution for UAV-WSN applications in smart agriculture and beyond.

On 3 March 2020, the Lebanese government declared a state of health emergency and general mobilization, imposing a total lockdown to combat the spread of the COVID-19 pandemic with the discovery of the first few cases in Lebanon. This was unchartered territory for everyone in the current modern era. Faced with the sudden disruption, Lebanese higher-education institutions, like their counterparts around the world, faced the challenge of relying on full online teaching overnight-urgently and without preparation in Lebanon, for sure, and many other nations worldwide.