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(Left): A quadrocopter with the directions of the turning propellers displayed. (Right): The six DOFs of our drone.
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In this paper, we present the development of a visual navigation capability for a small drone enabling it to autonomously approach flowers. This is a very important step towards the development of a fully autonomous flower pollinating nanodrone. The drone we developed is totally autonomous and relies for its navigation on a small on-board color cam...
Citations
... Keep in mind the success of the walnut pollination model (Figures 1-6) and based on the ab paragraphs, we illustrate the following flowchart (6 steps, state of the art flowcha which provides a general guideline for conducting a state-of-the-art analysis for a po nation robot to prevent or reduce the risk of walnut blight disease. The developmen autonomous visual navigation for a flower pollination drone has been presented in a per published in the journal Machines [73]. This technology could enable drones to aut omously approach flowers and perform pollination tasks. ...
... Keeping in mind the success of the walnut pollination model (Figures 1-6) and based on the above paragraphs, we illustrate the following flowchart (6 steps, state of the art flowchart), which provides a general guideline for conducting a state-of-the-art analysis for a pollination robot to prevent or reduce the risk of walnut blight disease. The development of autonomous visual navigation for a flower pollination drone has been presented in a paper published in the journal Machines [73]. This technology could enable drones to autonomously approach flowers and perform pollination tasks. ...
Walnut (Juglans regia L.) is a monoecious species and although it exhibits self-compatibility, it presents incomplete overlap of pollen shed and female receptivity. Thus, cross-pollination is prerequisite for optimal fruit production. Cross-pollination can occur naturally by wind, insects, artificially, or by hand. Pollen has been recognized as one possible pathway for Xanthomonas arboricola pv. juglandis infection, a pathogenic bacterium responsible for walnut blight disease. Other than the well-known cultural and chemical control practices, artificial pollination technologies with the use of drones could be a successful tool for walnut blight disease management in orchards. Drones may carry pollen and release it over crops or mimic the actions of bees and other pollinators. Although this new pollination technology could be regarded as a promising tool, pollen germination and knowledge of pollen as a potential pathway for the dissemination of bacterial diseases remain crucial information for the development and production of aerial pollinator robots for walnut trees. Thus, our purpose was to describe a pollination model with fundamental components, including the identification of the “core” pollen microbiota, the use of drones for artificial pollination as a successful tool for managing walnut blight disease, specifying an appropriate flower pollination algorithm, design of an autonomous precision pollination robot, and minimizing the average errors of flower pollination algorithm parameters through machine learning and meta-heuristic algorithms.
... These IPM methods can be applied both as precautionary and reactionary measures, and are much friendlier to urban farms, due to the zero harvest intervals and absence of harmful residues. (Dingley et al., 2022;Hulens et al., 2022). ...
... These IPM methods can be applied both as precautionary and reactionary measures, and are much friendlier to urban farms, due to the zero harvest intervals and absence of harmful residues. (Dingley et al., 2022;Hulens et al., 2022). ...
Societal Impact Statement
Cultivation of strawberry plants in urban production systems, whether in green open‐air spaces or under some form of protected horticulture such as vertical farming, has demonstrated to be challenging to new farmers and businesses. Commercial strawberry producers have an advanced understanding of strawberry plant physiology, enabling them to grow the crop successfully and profitably. Lack of knowledge exchange between commercial growers and new urban farmers seems to result in the abandonment of strawberries as crop of choice in urban systems. This review will confront the specific plant science challenges urban growers need to address to incorporate this nutritional crop into their revolutionary urban growing systems, whilst achieving good quality produce with high yields.
Summary
To ensure a sustainable future of farming, urban horticulture (UH) will need to be a key part of our everyday life. There are increasing demands for higher productivity and more locally produced food, even close to densely populated urban areas, to address environmental pressures and accelerate the resilience of modern food systems. UH is a broad term and can include numerous cultivation methods; rooftop gardens, public spaces, vertical walls, indoor vertical farms, as well as an array of crops including, salads, soft fruits and trees. Crops such as strawberries are expected to soon make a significant contribution to UH. Urban strawberry production promises all‐year round fruit availability, reduced reliance on imports, increased self‐sufficiency, lower food miles, a supply of high‐quality fresh fruits from hyper‐local spaces, increased employment opportunities, welfare benefits and an opportunity to promote a sense of community. Strawberry is a complex perennial crop with agronomical challenges, which requires specialist knowledge that is not always available to new urban farmers. Achieving an urban version of a strawberry field will require knowledge exchange between the commercial rural strawberry producers and the newly entered urban growers. Plant physiology, management of plant pathogens, choice of propagation material, fertigation, pollination and environmental requirements are the most common challenges for urban strawberry production. This review aims to consolidate the common bottleneck challenges of UH for new urban strawberry facilities.
... In recent years, drone technology has emerged as a promising solution to address these challenges and revolutionize the agricultural sector (Puri et al.,2017). UAVs have revolutionized farming practices, enabling farmers to monitor crops, map fields, spray pesticides, pollinate flowers, and perform a range of other tasks with increased efficiency, precision, and sustainability (Tsouros et al., 2019, Budiharto et al., 2021, Hulens et al., 2022. Drones have grown in popularity among farmers and agronomists in recent years due to their capacity to collect real-time data, monitor crop health, and optimise resource management (Yinka-Banjo and Ajayi, 2019). ...
This chapter examines how drones are revolutionizing conventional farming methods and extending the boundary of what is practical in contemporary agriculture. Unmanned aerial vehicles (UAVs), also known as drones, have become effective instruments for precision farming, allowing farmers to increase production, sustainability, and efficiency. In this chapter, the history and significance of drone technology in agriculture, its main subsystem required for proper functioning, how drones can be used in agriculture for a variety of tasks, such as crop monitoring and mapping, crop spraying and pest management, crop pollination etc. is being discussed. It also explores the drawbacks and potential use of drone technology in agriculture.
... The RoboMaster TT shown in Fig. 1 is a compact quadcopter with a Vision Positioning System and an onboard camera [14], [15]. This aircraft can remain stationary mid-air and navigate indoors thanks to its advanced flight controller and Vision Positioning System. ...
... The system uses a camera mounted on a UAV to capture images of the environment and then uses image processing and machine learning algorithms to detect and locate flowers. The system also uses information from the camera to control the UAV's movement and maintain a constant pause over the flowers as it pollinates [24]; • A simulation model was used to simulate the movement of multiple UAVs in a facility agriculture environment and their interactions with plants. The authors used the simulation model to test different compensatory pollination strategies and evaluate their effectiveness. ...
Currently, Unmanned Aerial Vehicles (UAVs) are considered in the development of various applications in agriculture, which has led to the expansion of the agricultural UAV market. However, Nano Aerial Vehicles (NAVs) are still underutilised in agriculture. NAVs are characterised by a maximum wing length of 15 centimetres and a weight of fewer than 50 g. Due to their physical characteristics, NAVs have the advantage of being able to approach and perform tasks with more precision than conventional UAVs, making them suitable for precision agriculture. This work aims to contribute to an open-source solution known as Nano Aerial Bee (NAB) to enable further research and development on the use of NAVs in an agricultural context. The purpose of NAB is to mimic and assist bees in the context of pollination. We designed this open-source solution by taking into account the existing state-of-the-art solution and the requirements of pollination activities. This paper presents the relevant background and work carried out in this area by analysing papers on the topic of NAVs. The development of this prototype is rather complex given the interactions between the different hardware components and the need to achieve autonomous flight capable of pollination. We adequately describe and discuss these challenges in this work. Besides the open-source NAB solution, we train three different versions of YOLO (YOLOv5, YOLOv7, and YOLOR) on an original dataset (Flower Detection Dataset) containing 206 images of a group of eight flowers and a public dataset (TensorFlow Flower Dataset), which must be annotated (TensorFlow Flower Detection Dataset). The results of the models trained on the Flower Detection Dataset are shown to be satisfactory, with YOLOv7 and YOLOR achieving the best performance, with 98% precision, 99% recall, and 98% F1 score. The performance of these models is evaluated using the TensorFlow Flower Detection Dataset to test their robustness. The three YOLO models are also trained on the TensorFlow Flower Detection Dataset to better understand the results. In this case, YOLOR is shown to obtain the most promising results, with 84% precision, 80% recall, and 82% F1 score. The results obtained using the Flower Detection Dataset are used for NAB guidance for the detection of the relative position in an image, which defines the NAB execute command.
This book chapter delves into the realm of Unmanned Aerial Vehicles (UAVs) and their potential impact on apple flower pollination, offering a captivating exploration of this subject matter. Utilizing contemporary research and practical implementations, we explore the advancements of Unmanned Aerial Vehicle (UAV) technology and its potential to augment orchard productivity. As we explore the core of the subject, we reveal the significant significance of adequate pollination for apple trees. This study investigates the consequences of inadequate pollination, encompassing diminished fruit production and yield, irregular patterns of fruit-bearing, and compromised overall tree vitality. This establishes the foundation for the novel application of Unmanned Aerial Vehicles (UAVs) as airborne agents for pollination.
Expanding our investigation, we proceed to examine the different methods of pollination and the conventional methodologies and technologies utilized in pollination. This book chapter then discusses the underlying mechanisms of unmanned aerial vehicle (UAV)-based pollination, encompassing a range of topics including autonomous flight patterns and accurate pollen delivery. By conducting a comparative analysis between unmanned aerial vehicles (UAVs) and conventional pollinators, significant insights can be obtained regarding the potential advantages and obstacles associated with the integration of this technological innovation.
In short, this chapter functions as a symbol of excitement regarding the prospective development of apple orchards.
