Application of IoT-Based Drones in Precision Agriculture for Pest Control

Refaai, Mohamad Reda A.; Dattu, Vinjamuri S. N. C. H.; Gireesh, N.; Dixit, Ekta; Sandeep, CH.; Christopher, David

doi:10.1155/2022/1160258

Cited by 11 publications

(5 citation statements)

References 32 publications

(40 reference statements)

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“…The most common examples of cloud-based IoT applications in agriculture are (1) smart sensor-based IoT devices for monitoring crops, soil, fields, livestock, storage facility, or generally any necessary factor that influences agriculture production [141], [142]. (2) Smart IoT agriculture vehicles, drones, autonomous robots, and actuators(e.g.DroneSeed, SenseFly, SoilCares [143]). (3) smart greenhouse and hydroponics [144].…”

Section: B Category 2: Children's Iot (Smart Toys)mentioning

confidence: 99%

Agri-Info: Cloud Based Autonomic System for Delivering Agriculture as a Service

2020

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Cloud computing has emerged as an important paradigm for managing and delivering services efficiently over the Internet. Convergence of cloud computing with technologies such as wireless sensor networking and mobile computing offers new applications' of cloud services but this requires management of Quality of Service (QoS) parameters to efficiently monitor and measure the delivered services. This paper presents a QoS-aware Cloud Based Autonomic Information System for delivering agriculture related information as a service through the use of latest Cloud technologies which manage various types of agriculture related data based on different domains. Proposed system gathers information from various users through preconfigured devices and manages and provides required information to users automatically. Further, Cuckoo Optimization Algorithm has been used for efficient resource allocation at infrastructure level for effective utilization of resources. We have evaluated the performance of the proposed approach in Cloud environment and experimental results show that the proposed system performs better in terms of resource utilization, execution time, cost and computing capacity along with other QoS parameters. IntroductionCloud computing has emerged as an important paradigm for managing and delivering the new emerging applications in the field of healthcare, agriculture, education, finance, etc. efficiently over the internet. However, providing dedicated cloud services that ensure application's dynamic QoS (Quality of Service) requirements and user satisfaction is a big research challenge in cloud computing. As dynamism, heterogeneity and complexity of applications is increasing rapidly, this makes cloud systems unmanageable in service delivery. To overcome these problems, cloud systems require self-management of services. Autonomic cloud computing systems provide the environment in which applications can be managed efficiently by fulfilling QoS requirements of applications without human involvement [1] [2].In our earlier work [1] [14] [15] [16] [27], we have identified various research issues related to QoS and SLA for cloud resource scheduling and have developed a QoS based resource provisioning technique (Q-aware) to map the resources to the workloads based on used requirements described in the form of SLA. Further, resource scheduling framework (QRSF) has been proposed, in which provisioned resources have been scheduled by using different resource scheduling policies (cost, time, cost-time and bargaining based). The concept of QRSF has been further extended by proposing energy-aware autonomic resource scheduling technique (EARTH), in which IBM's autonomic computing concept has been used to schedule the resources automatically by optimizing energy consumption and resource utilization where user can easily interact with the system using available user interface. In this work, we have proposed a cloud based autonomic information system which delivers Agriculture as a Service (AaaS) through cloud infrastructure and se...

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Section: B Category 2: Children's Iot (Smart Toys)mentioning

confidence: 99%

Agri-Info: Cloud Based Autonomic System for Delivering Agriculture as a Service

2020

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“…Since drones are constrained by their individual supplies of fuel as well as pesticide, they are able to achieve the elimination of pests by asking for aid from other drones. Attempts have been made to address these aforementioned concerns by the use of certain bio-inspired techniques of enrolment (Refaai et al, (2022).…”

Section: Introductionmentioning

confidence: 99%

Identifying pests in precision agriculture using low-cost image data acquisition

Hemalatha,

Sangeetha

2024

Braz. J. Biol.

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Unmanned Aerial Vehicles (UAVs), often called drones, have gained progressive prevalence for their swift operational ability as well as their extensive applicability in diverse real-world situations. Of late, UAV usage in precision agriculture has attracted much interest from scientific community. This study will look at drone aid in precise farming. Big data has the ability to analyze enormous amounts of data. Due to this, it is one of the diverse crucial technologies of Information and Communication Technology (ICT) which had applied in precision agriculture for the abstraction of critical information as well as for assisting agricultural practitioners in the comprehension of the most feasible farming practices, and also for better decision-making. This work analyses communication protocols, as well as their application toward the challenge of commanding a drone fleet for protecting crops from infestations of parasites. For computer-vision tasks as well as data-intensive applications, the method of deep learning has shown much potential. Due to its vast potential, it can also be used in the field of agriculture. This research will employ several schemes to assess the efficacy of models includes Visual Geometry Group (VGG-16), the Convolutional Neural Network (CNN) as well as the Fully-Convolutional Network (FCN) in plant disease detection. The methods of Artificial Immune Systems (AIS) can be used in order to adapt deep neural networks to the immediate situation. Simulated outcomes demonstrate that the proposed method is providing superior performance over various other technologically-advanced methods.

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“…For instance, sensors, AI, and an IoTintegrated drone can help determine and apply the proper amount of fertilizer, water, and pesticide needed based on the condition of plants and the farming land, which signicantly reduces workload, overcomes the labor shortage, and avoids the waste of resources. [10][11][12] A smart greenhouse, which has sensors and an automated system, can optimize the growing environment of plant crops by adjusting temperature, moisture, and humidity that best suit plant growth aer acquiring data from sensor measurements. Hence, biosensors integrated with these technologies are an innovative strategy to transform traditional farming.…”

Section: Introductionmentioning

confidence: 99%