IOT Based Smart Agriculture System

Sushanth, G.; Sujatha, S.

doi:10.1109/wispnet.2018.8538702

Cited by 179 publications

(39 citation statements)

References 2 publications

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“…Using MATLAB Programming it displays threshold value and previous data in an excel sheet. G. Sushanth [11] presented an idea on an agriculture system based on IoT, along with a sensing system that senses an invasion of animals, and notification to the farmer about the invasion.…”

Section: II Conventional Methodsmentioning

confidence: 99%

Low Cost Design of Automated Drip Irrigation System with GSM

Venkatesh V*,

DEVI B

2019

IJRTE

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This paper is about an automatic irrigation control system which is cost effective and can be used for irrigation by a farmer. Today’s industrial automation and controlling of machine is high in cost and not suitable for a farming field. So, here we design a smart drip irrigation technology with effective control system in low cost. The voltage monitoring unit informs the farmer about the power supply conditions on the field. The aim of this study, is to control the motor automatically, and decide the direction of the water flow through valves, based on the inputs from the farmer and also with the collective inputs from the sensors, which finally notify instantly about the happenings and conditions of the field. It operates under low hardware cost by distributing irrigation to crops by elevation change and gravity. The soil moisture and amount of flow of water in each sector are major consideration to design a fail-safe system for a variety of crops planted at a time.

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Section: II Conventional Methodsmentioning

confidence: 99%

Low Cost Design of Automated Drip Irrigation System with GSM

Venkatesh V*,

DEVI B

2019

IJRTE

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“…This is one of the crucial issues in a number of systems, including smart agriculture, smart cities, and public safety, among others. Using agriculture as an example, there have been a number of research efforts devoted on applying IoT techniques to the agriculture domain [11], [40]- [44]. For example, the concepts and models for smart farming using IoT have been introduced by existing research [11], [43], [44].…”

Section: Related Workmentioning

confidence: 99%

Internet of Things Data Collection Using Unmanned Aerial Vehicles in Infrastructure Free Environments

et al. 2020

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With the immensity of distributed Internet of Things (IoT) devices and the exponential increase in data generated from a variety of IoT-driven smart-world applications, how to effectively provide data driven service supported by IoT has become a critical issue. While the state-of-the-art technologies have been developed and network infrastructures with high capabilities have been designed to deal with the data collection problem, there are still application scenarios, in which network infrastructure is not available or appropriate in large target areas (e.g., farmlands deployed with IoT sensors in operation, providing precise agriculture; emergency responder with IoT sensors, providing public safety service). To address the issue of efficiently collecting data from IoT devices deployed in large areas without predeployed network infrastructure, we formalize the problem space in a three-dimensional model that considers task, resource, and methodology. Based on the designed problem space, we propose a novel solution that deploys an unmanned aerial vehicle (UAV), as a critical next generation mobile network, to achieve intermittent IoT device connections and enable data collection based on delay tolerant network (DTN) protocol. The UAV flight path is determined using a Hilbert Curve-based path planning algorithm. Through a series of quantitative experiments, we validate the effectiveness of our approach in a network emulation environment, and confirm its advantages in comparison with several baseline approaches. The results of our research shows the capability of quality and cost control in IoT applications such as smart agriculture, public safety disaster recovery and rescue. INDEX TERMS Internet of Things, delay tolerant networks, next generation mobile networks, unmanned aerial vehicles, path planning, data collection.

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“…If the environmental condition is warm, dry, sunny, windy, then plants require a large quantity of water, and if these variables are like a cold, wet, cloudy, low wind, then the crop needs less water. The previous research model abstracted a scheme consisting of six components that are monitoring, managing, planning, distributing information, supporting the decision, and tracking action [22].…”

Section: Parallel Researchmentioning

confidence: 99%

The Automatic Agricultural Crop Maintenance System using Runway Scheduling Algorithm: Fuzzyc-LR for IoT Networks

Balakrishna¹,

Moparthi²

2020

IJACSA

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In this framework, the crop diseases have been identified using three types of methods, fuzzy-c as a clustering algorithm, runway scheduling trains like classification algorithm, and logistic regression as prediction algorithm. These techniques are meaningful solutions for losses in yields and the quantity of agriculture production. In this work, crop disease and corresponding fertilizers are predicted based on pattern scalability by the above algorithms. It proposes a Sensor Calibration and Feed Back Method (SCFM) with RWSA for better agriculture crop maintenance with automation and Fuzzyc, Logistic regressions are helpful in studying the datasets of the crops for classifying the disease. This research tries to identify the leaf color, leaf size, disease of plant, and fertilizer for the illness of crops. In this context, RWSA-Agriculture gives the solution for current problems and improves the F1-Score. The data collected from local sensors and remote station is estimated with the dataset, these sensor based L.R., and Fuzzy-c controls disease prediction system in SCFM and RWSA. This technique accurately regulates the dispensing of water as well as chemicals; fertilizers for crop monitor and prevent the diseases of crops. This investigation gives performance metrics values i.e PSNR=44.18dB, SSIM = 0.9943, BPP =1.46, Tp=0.945 and CR = 5.25.

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IOT Based Smart Agriculture System

Cited by 179 publications

References 2 publications

Low Cost Design of Automated Drip Irrigation System with GSM

Low Cost Design of Automated Drip Irrigation System with GSM

Internet of Things Data Collection Using Unmanned Aerial Vehicles in Infrastructure Free Environments

The Automatic Agricultural Crop Maintenance System using Runway Scheduling Algorithm: Fuzzyc-LR for IoT Networks

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