Extending ONTAgri with Service-Oriented Architecture towards Precision Farming Application

Fahad, Muhammad; Javid, Tariq; Beenish, Hira; Siddiqui, Adnan Ahmed; Ahmed, Ghufran

doi:10.3390/su13179801

Cited by 11 publications

(4 citation statements)

References 21 publications

(36 reference statements)

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“…Images of pests prevalent in the Malatya, Bingöl, and Elaz districts of Turkey were utilized to assess the suggested technique's effectiveness [22]. A drone was used to capture these photographs.…”

Section: Data Collectionmentioning

confidence: 99%

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

Refaai

Dattu

Gireesh³

et al. 2022

Advances in Materials Science and Engineering

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Unmanned aerial vehicles (UAVs), commonly known as drones, have been progressively prevalent due to their capability to operate quickly and their vast range of applications in a variety of real-world circumstances. The utilization of UAVs in precision farming has lately gained a lot of attention from the scientific community. This study addresses with the assistance of drones in the precision agricultural area. This paper makes significant contributions by analyzing communication protocols and applying them to the challenge of commanding a fleet of drones to protect crops from parasite infestations. In this research, the effectiveness of nine powerful deep neural network models is measured for the detection of plant diseases using diverse methodologies. These deep neural networks are adapted to the immediate situation using transfer learning and deep extraction of features approaches. The presented study takes into account the used pretrained deep learning model for extracting features and fine-tuning. The deep feature extraction characteristics are subsequently categorized using support vector machines (SVMs) and extreme learning machines (ELMs). For measuring performance, the precision, sensitivities, specific, and F1-score are all evaluated. Deep feature extraction and SVM/ELM classification generated better outcomes than transfer learning, according to the analysis result. Furthermore, the analysis of the various methodologies tries to assess their effectiveness and costs. The different approaches, for example, confront difficulties such as investigating the region in the shortest possible time feasible, while eliminating the same region being searched by more drones, detecting parasites, and stopping their spread by applying the appropriate number of pesticides. Simulation models are a significant aid to researchers in conducting to evaluate these technologies and creating specific tactics and coordinating procedures capable of effectively supporting farms and achieving the aim. The main objective of this paper is to compare the search techniques of two distinct methods of parasitic to identify performance.

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Section: Data Collectionmentioning

confidence: 99%

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

Refaai

Dattu

Gireesh³

et al. 2022

Advances in Materials Science and Engineering

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“…Many authors [14][15][16][17] speak of revolutions in the era of industrial agriculture. The introduction of tractors brought about the first revolution, combining harvesters and mechanisation.…”

Section: Definition Of Precision Agriculturementioning

confidence: 99%

Precision Agriculture Technologies for Crop and Livestock Production in the Czech Republic

2022

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Modern technologies are penetrating all fields of human activity, including agriculture, where they significantly affect the quantity and quality of agricultural production. Precision agriculture can be characterised as an effort to improve the results of practical farming, achieving higher profits by exploiting the existing spatial unevenness of soil properties. We aim to evaluate precision agriculture technologies’ practical use in agricultural enterprises in the Czech Republic. The research was based on a questionnaire survey in which 131 farms participated. We validated the hypothesis through a Chi-squared test on the frequency of occurrence of end-use technology. The results showed that precision farming technologies are used more in crop than livestock production. In particular, 58.02% of enterprises use intelligent weather stations, 89.31% use uncrewed vehicles, and 61.83% use navigation and optimisation systems for optimising journeys. These technologies are the most used and closely related to autonomous driving and robotics in agriculture. The results indicate how willing are agricultural enterprises to adopt new technologies. For policy makers, these findings show which precision farming technologies are already implemented. This can make it easier to direct funding towards grants and projects.

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“…Agricultural illnesses and infestations surveillance should be regulated and computerized whenever drones are employed to detect the presence of bugs and pests. However, a drone on a distant vast field must deal with problems including a limited travel time and frequent charge changes because of its limited transporting capacity and memory [8]. It is becoming increasingly popular to utilize training techniques to improve information retrieval from sensory information.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-based Topology-Aware Routing Protocol with Priority Scheduling for Internet of Drones in Agriculture Application

2023

IJIES

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Internet of drones (IoD) are commonly constructed with unmanned vehicles, have been progressively prevalent due to their capability to operate quickly and their vast range of applications in a variety of real-world circumstances. These IoDs are interact with zone service providers (ZSPs) to achieve the goal of assisting drones in accessing controlled agriculture services. The utilization of drones in precision farming has lately gained a lot of attention from the scientific community. This study addresses with the assistance of drones in the precision agricultural area by analysing communication protocols and applying them to the challenge of commanding a fleet of drones to protect crops from parasite infestations. objectively and equitably assigns a weight to multiple service scheduling parameters based on maldistributed decision making theory, calculates the serving priority of each service request group, and then serves the service request groups based on the calculated serving priori-ty accordingly Hence, this paper proposes reinforcement learning-based topology-aware routing protocol with priority scheduling (RLTARP) to provide reliable combinations between the source and destination. It also improves the routing decision by considering two-hop neighbour nodes, extending the local view of the network topology. The priority scheduling method adopts maldistributed decision making theory, to find the group of priorities based on service request. The proposed RLTARP is compared with three existing methods such as DroneCOCoNet, Markov decision process (MDP)and deep deterministic policy gradient (DDPG) and hence it produces 46.34% of packet delivery ratio, 67.49% of end to end delay, 16.45% of routing overhead, 13% of energy consumption and 97.6% of network lifetime.

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Extending ONTAgri with Service-Oriented Architecture towards Precision Farming Application

Cited by 11 publications

References 21 publications

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

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

Precision Agriculture Technologies for Crop and Livestock Production in the Czech Republic

Reinforcement Learning-based Topology-Aware Routing Protocol with Priority Scheduling for Internet of Drones in Agriculture Application

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