Towards Precision Agriculture: IoT-Enabled Intelligent Irrigation Systems Using Deep Learning Neural Network

Kashyap, Pankaj Kumar; Kumar, Sushil; Jaiswal, Ajay; Prasad, Mukesh; Gandomi, Amir H.

doi:10.1109/jsen.2021.3069266

Cited by 146 publications

(44 citation statements)

References 30 publications

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“…The simulation results show that our proposed model ANFPB preserves the security and privacy of the users, and encourages the users to participate in the service that also helps to capture the correct evidence of any events happening on the road as compared to other state-of-art-algorithms. In the future, we would like to extend this work by advancing machine learning techniques [53][54] with blockchain. And, also includes the compression of huge data in the cloud to preserving their security.…”

Section: Discussionmentioning

confidence: 99%

Blockchain Based Intelligent Incentive Enabled Information Sharing Scheme in Future Generation IoV Networks

Alam¹,

Kumar²,

Kumar³

et al. 2021

Preprint

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With the advancement of vehicle’s traffic information processing and communication capability in Internet of Vehicle (IoV) network widely used for the vehicle-to- infrastructure transportation communication. Firstly, the protection of the user’s (vehicles) privacy at the time of information sharing and secondly, users lack the motivation to share the traffic information with roadside units (RSUs) are two major concerns in the IoV networks. In this regard, we propose a novel adaptive neuro-fuzzy based payment using blockchain (ANFPB) transportation communication scheme that not only motivates users to take participate in the information sharing problems with the payment mechanism but also allows users to anonymously share the traffic information with RSU in the IoV network. Meanwhile, a smart contract is presented to generate pseudonyms to share the traffic information anonymously in a non-trustful IoV network. Also, an algorithm ANFPB is presented for the evaluation of payment based on location, timeline, and quality of information shared by the vehicles. Finally, the extensive simulation analysis shows that the proposed ANFPB is more efficient in terms of preserving privacy and computational costs as compared to state-of-the-art schemes.

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Section: Discussionmentioning

confidence: 99%

Blockchain Based Intelligent Incentive Enabled Information Sharing Scheme in Future Generation IoV Networks

Alam¹,

Kumar²,

Kumar³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Precision agriculture has also employed deep learning neural networks with IoT for intelligent irrigation systems [6]. It is considered a feedback system that works better based on the weather of any region.…”

Section: Introductionmentioning

confidence: 99%

Efficient IoT-Based Control for a Smart Subsurface Irrigation System to Enhance Irrigation Management of Date Palm

Mohammed

Riad

Alqahtani

2021

Sensors

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Drought is the most severe problem for agricultural production, and the intensity of this problem is increasing in most cultivated areas around the world. Hence improving water productivity is the primary purpose of sustainable agriculture. This study aimed to use cloud IoT solutions to control a modern subsurface irrigation system for improving irrigation management of date palms in arid regions. To achieve this goal, we designed, constructed, and validated the performance of a fully automated controlled subsurface irrigation system (CSIS) to monitor and control the irrigation water amount remotely. The CSIS is based on an autonomous sensors network to instantly collect the climatic parameters and volumetric soil water content in the study area. Therefore, we employed the ThingSpeak cloud platform to host sensor readings, perform algorithmic analysis, instant visualize the live data, create event-based alerts to the user, and send instructions to the IoT devices. The validation of the CSIS proved that automatically irrigating date palm trees controlled by the sensor-based irrigation scheduling (S-BIS) is more efficient than the time-based irrigation scheduling (T-BIS). The S-BIS provided the date palm with the optimum irrigation water amount at the opportune time directly in the functional root zone. Generally, the S-BIS and T-BIS of CSIS reduced the applied irrigation water amount by 64.1% and 61.2%, respectively, compared with traditional surface irrigation (TSI). The total annual amount of applied irrigation water for CSIS with S-BIS method, CSIS with T-BIS method, and TSI was 21.04, 22.76, and 58.71 m3 palm−1, respectively. The water productivity at the CSIS with S-BIS (1.783 kg m−3) and T-BIS (1.44 kg m−3) methods was significantly higher compared to the TSI (0.531 kg m−3). The CSIS with the S-BIS method kept the volumetric water content in the functional root zone next to the field capacity compared to the T-BIS method. The deigned CSIS with the S-BIS method characterized by the positive impact on the irrigation water management and enhancement on fruit yield of the date palm is quite proper for date palm irrigation in the arid regions.

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“…And smart sensor networks are widely used, such as realizing the monitoring of forest factors such as temperature, humidity, and light in forest environmental protection. In terms of security, the monitoring of late arrivals is realized by deploying sensor nodes in public places [1], implementing campaign tactical reconnaissance against enemy targets in the military field to monitor the enemy's real-time dynamics, and, in terms of wildlife protection, the deployment of sensor nodes to monitor the activities of wild animal groups, and so on.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Chaotic Ant Colony Optimization for Energy Optimization in Smart Sensor Networks

2021

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Smart sensor network has the characteristics of low cost, low power consumption, real time, strong adaptability, etc., and it has a wide range of application prospects in the agricultural field. However, the smart sensor node is limited by its own energy; it also faces many bottlenecks in agricultural applications. Therefore, balancing the energy consumption of nodes and extending the life of the network are important considerations in the design of efficient routing for smart sensor networks. Aiming at the problem of energy constraints, this paper proposes an intelligent sensor network clustering algorithm based on adaptive chaotic ant colony optimization (ACACO). ACACO introduces logical chaotic mapping to interfere with the pheromone on the initial path and uses the adaptive strategy to improve the transition probability formula. After selecting the best next hop node, the advancing ants are released to update the local pheromone, and the current pheromone content is adjusted by the chaos factor. When the ants determine the path, they release subsequent ants to update the global pheromone. The simulation results show that ACACO has obvious advantages over genetic algorithm (GA) and particle swarm optimization (PSO).

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Towards Precision Agriculture: IoT-Enabled Intelligent Irrigation Systems Using Deep Learning Neural Network

Cited by 146 publications

References 30 publications

Blockchain Based Intelligent Incentive Enabled Information Sharing Scheme in Future Generation IoV Networks

Blockchain Based Intelligent Incentive Enabled Information Sharing Scheme in Future Generation IoV Networks

Efficient IoT-Based Control for a Smart Subsurface Irrigation System to Enhance Irrigation Management of Date Palm

Adaptive Chaotic Ant Colony Optimization for Energy Optimization in Smart Sensor Networks

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