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... Multispectral Camera: The multispectral camera used in the project is a Mini MCA from Tetracam 2 (Fig. 3). This specific sensor weighs 695 g and consists of six digital cameras arranged in an array. Each of the cameras is equipped with a 1.3 megapixel CMOS sensor with individual band pass filters. The spectrometer filters used in this project are 488, 550, 610, 675, 780 and 940 nm (bandwidths of 10 nm). The camera is controlled from the ...
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... In (Lu et al., 2017;Liu et al., 2020;Kumar et al., 2019;Bellocchio et al., 2020;Maimaitijiang et al., 2020), deep learning-based models have been used to estimate the different traits of crops like yield, count of flowers, fruits. Deep learning and UAVs have proven to be both time-efficient and cost-efficient in various crop related tasks such as disease detection (Garcia-Ruiz et al., 2013), weed detection (Kazmi et al., 2011) and crop monitoring (Dastgheibifard and Asnafi, 2018). While CNNs have shown great promise in tackling crop-related tasks, they are notorious for requiring huge amounts of accurately labeled training data to attain a decent test performance. ...
Regular monitoring is worthwhile to maintain a healthy crop. Historically, the manual observation was used to monitor crops, which is time-consuming and often costly. The recent boom in the development of Unmanned Aerial Vehicles (UAVs) has established a quick and easy way to monitor crops. UAVs can cover a wide area in a few minutes and obtain useful crop information with different sensors such as RGB, multispectral, hyperspectral cameras. Simultaneously, Convolutional Neural Networks (CNNs) have been effectively used for various vision-based agricultural monitoring activities, such as flower detection, fruit counting, and yield estimation. However, Convolutional Neural Network (CNN) requires a massive amount of labeled data for training, which is not always easy to obtain. Especially in agriculture, generating labeled datasets is time-consuming and exhaustive since interest objects are typically small in size and large in number. This paper proposes a novel method using k-means clustering with adaptive thresholding for detecting maize crop tassels to address these issues. The qualitative and quantitative analysis of the proposed method reveals that our method performs close to reference approaches and has an advantage over computational complexity. The proposed method detected and counted tassels with precision: 0.97438, recall: 0.88132, and F1 Score: 0.92412. In addition, using maize tassel detection from UAV images as the task in this paper, we propose a semi-automatic image annotation method to create labeled datasets of the maize crop easily. Based on the proposed method, the developed tool can be used in conjunction with a machine learning model to provide initial annotations for a given image, modified further by the user. Our tool’s performance analysis reveals promising savings in annotation time, enabling the rapid production of maize crop labeled datasets.
... In (Lu et al., 2017;Liu et al., 2020;Kumar et al., 2019;Bellocchio et al., 2020;Maimaitijiang et al., 2020), deep learning-based models have been used to estimate the different traits of crops like yield, count of flowers, fruits. Deep learning and UAVs have proven to be both time-efficient and cost-efficient in various crop related tasks such as disease detection (Garcia-Ruiz et al., 2013), weed detection (Kazmi et al., 2011) and crop monitoring (Dastgheibifard and Asnafi, 2018). While CNNs have shown great promise in tackling crop-related tasks, they are notorious for requiring huge amounts of accurately labeled training data to attain a decent test performance. ...
... Precision agriculture has literally taken off with the development of autonomous UAVs [1] (Unmanned Aerial Vehicles) also known as drones that help farmers map large areas in a fast and economical way [2]. UAVs can be utilized in precision agriculture (PA) for crop management and monitoring [3], [4], weed detection [5], irrigation scheduling [6], disease detection [7], pesticide spraying [3] and gathering data from ground sensors (moisture, soil properties, etc.,) [8]. The deployment of UAVs in PA is a cost-effective and time saving technology which can help for improving crop yields, farms productivity and profitability in farming systems. ...
In this paper we have reviewed the use of a cablebot system inside greenhouses and small plots for Precision Agriculture, that is able to inform the user for plant disease and micro-climate measurements. The proposed system moves alongside a cable above a crop and takes measurements by an array of sensors (relative humidity, temperature, air quality, spectral etc) that it carries.
... We refer reader to literary works [91,92] for the detailed reports of techniques and applications of UAVs in precision agriculture, remote sensing, search and rescue, construction and infrastructure inspection and discuss other market opportunities. UAVs can be utilized in precision agriculture (PA) for crop management and monitoring [93,94], weed detection [95], irrigation scheduling [96], agricultural pattern detection [97], pesticide spraying [93], cattle detection [98], disease detection [99,100], insect detection [101] and data collection from ground sensors (moisture, soil properties, etc.,) [102]. The deployment of UAVs in PA is a cost-effective and time saving technology which can help for improving crop yields, farms productivity and profitability in farming systems. ...
In light of growing challenges in agriculture with ever growing food demand across the world, efficient crop management techniques are necessary to increase crop yield. Precision agriculture techniques allow the stakeholders to make effective and customized crop management decisions based on data gathered from monitoring crop environments. Plant phenotyping techniques play a major role in accurate crop monitoring. Advancements in deep learning have made previously difficult phenotyping tasks possible. This survey aims to introduce the reader to the state of the art research in deep plant phenotyping.
... We refer reader to literary works [91,92] for the detailed reports of techniques and applications of UAVs in precision agriculture, remote sensing, search and rescue, construction and infrastructure inspection and discuss other market opportunities. UAVs can be utilized in precision agriculture (PA) for crop management and monitoring [93,94], weed detection [95], irrigation scheduling [96], agricultural pattern detection [97], pesticide spraying [93], cattle detection [98], disease detection [99,100], insect detection [101] and data collection from ground sensors (moisture, soil properties, etc.,) [102]. The deployment of UAVs in PA is a cost-effective and time saving technology which can help for improving crop yields, farms productivity and profitability in farming systems. ...
In light of growing challenges in agriculture with ever growing food demand across the world, efficient crop management techniques are necessary to increase crop yield. Precision agriculture techniques allow the stakeholders to make effective and customized crop management decisions based on data gathered from monitoring crop environments. Plant phenotyping techniques play a major role in accurate crop monitoring. Advancements in deep learning have made previously difficult phenotyping tasks possible. This survey aims to introduce the reader to the state of the art research in deep plant phenotyping.
... UAVs can be utilized in precision agriculture (PA) for crop management and monitoring [105], [106], weed detection [107], irrigation scheduling [108], disease detection [109], pesticide spraying [105] and gathering data from ground sensors (moisture, soil properties, etc.,) [110]. The deployment of UAVs in PA is a cost-effective and time saving technology which can help for improving crop yields, farms productivity and profitability in farming systems. ...
The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate the more that $45 Billion market value of UAV usage. In this survey, we present UAV civil applications and their challenges. We also discuss current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including: charging challenges, collision avoidance and swarming challenges, and networking and security related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.
... UAVs can be utilized in precision agriculture (PA) for crop management and monitoring [105], [106], weed detection [107], irrigation scheduling [108], disease detection [109], pesticide spraying [105] and gathering data from ground sensors (moisture, soil properties, etc.,) [110]. The deployment of UAVs in PA is a cost-effective and time saving technology which can help for improving crop yields, farms productivity and profitability in farming systems. ...
The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related
challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.
... Teams of cooperative robots hold unique advantages over single actors across many task domains. Examples include mapping [1], [2], search [3], [4], precision agriculture [5], [6], and logistics and delivery [7]. There are, however, fundamental challenges to designing effective multi-robot systems. ...
Effective communication is required for teams of robots to solve sophisticated collaborative tasks. In practice it is typical for both the encoding and semantics of communication to be manually defined by an expert; this is true regardless of whether the behaviors themselves are bespoke, optimization based, or learned. We present an agent architecture and training methodology using neural networks to learn task-oriented communication semantics based on the example of a communication-unaware expert policy. A perimeter defense game illustrates the system's ability to handle dynamically changing numbers of agents and its graceful degradation in performance as communication constraints are tightened or the expert's observability assumptions are broken.
... A Mission Control Center (MCC) is set up with wireless communication, from which waypoints and commands are sent to each UGV controller. Each UGV will respond wirelessly with the data requested and status on the drive [36]. The system setup is illustrated in Fig. 8. Fig. 6. ...
The development of a new generation of agricultural robots that can easily and safely cooperate to accomplish agricultural tasks become necessary. Heavy tractors and machinery used today compacts the soil, which over time severely deteriorates the fertility of the soil. This is a significant threat to soil in Europe. Compacted soils require more than a decade of expensive treatment to recover its fertility. The problem can be solved by replacing heavy tractors with a number of smaller vehicles that can treat crop fields just as well and without compacting the soil. However, that scenario requires a human supervisor/operator for each vehicle that is very expensive. In this paper, the technology required to enable a single farmer to supervise and operate a team of these automated vehicles is proposed. This will include the development of a Mission Control Center and Intelligent Coverage Path Planning algorithms to enable team members to communicate and cooperate, and solve a range of agricultural tasks in a safe and efficient way.
... During the last decade, multi-robot systems have gone from isolated and anecdotal laboratory systems to robustly deployed across a number of domains, such as warehousing [1]- [3], precision agriculture [4], [5], search-and-rescue [6], [7], and environmental monitoring and exploration [8]- [10]. The fundamental reason why more robots are preferable in these types of domains is that there is strength in numbers. ...
Recently, significant gains have been made in our understanding of multi-robot systems, and such systems have been deployed in domains as diverse as precision agriculture, flexible manufacturing, environmental monitoring, search-and-rescue operations, and even swarming robotic toys. What has enabled these developments is a combination of technological advances in performance, price, and scale of the platforms themselves, and a new understanding of how the robots should be organized algorithmically. In this paper, we focus on the latter of these advances, with particular emphasis on decentralized control and coordination strategies as they pertain to multi-robot systems. The paper discusses a class of problems related to the assembly of preferable geometric shapes in a decentralized manner through the formulation of descent-based algorithms defined with respect to team-level performance costs.
