IoT Based Automatic Irrigation System Using Wireless Sensor Networks

Penchalaiah, N.; Emmanuel, Jaladanki Nelson; Kamal, S. Suraj; Ramana, Kadiyala

doi:10.1007/978-981-15-7961-5_116

Cited by 8 publications

(4 citation statements)

References 47 publications

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“…Stimulating the middle between the short and long time frame can motivate the people involved and shorten the development time at the same time. e basic idea of the methodology is to encourage innovation and dare to test, develop, and validate concepts [23].…”

Section: Poc Design Methodologymentioning

confidence: 99%

PoC Design: A Methodology for Proof-of-Concept (PoC) Development on Internet of Things Connected Dynamic Environments

Prasanna¹,

Ramana²,

Kautish³

et al. 2021

Security and Communication Networks

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Internet of Things (IoT) is a phenomenon involving connecting things or objects with sensors. The IoT market is growing rapidly, and there are strong incentives for companies to follow the trend of IoT growth and development. However, the percentage of IoT measures that are considered successful seems low. The complexity of carrying out an IoT project lies in the need to adjust all the pieces of the puzzle: assets, sensors, communications, technology, coverage, and geographical locations with precision of the measures and regulations. All these requirements determine the economic viability of the business and its benefit. This study, therefore, examines how the project methodology can support the development of the concept and ensure the business value of IoT initiatives. The project methodology developed in this study is called PoC Design. A case study was evaluated, in which defects in street lighting were investigated and carried out. The evaluation of the methodology highlighted the importance of defining problems and solutions based on business value, calculating the potential of an IoT initiative, determining the continuation of the project, involving stakeholders at an early stage, and creating a PoC to validate the concept with stakeholders.

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Section: Poc Design Methodologymentioning

confidence: 99%

PoC Design: A Methodology for Proof-of-Concept (PoC) Development on Internet of Things Connected Dynamic Environments

Prasanna¹,

Ramana²,

Kautish³

et al. 2021

Security and Communication Networks

Self Cite

View full text Add to dashboard Cite

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“…The purpose of this contribution is to lessen the consumption of time and human intervention for experiments on seed germination and to enhance the prediction model of germination, which has been (a) independent thresholds based on custom color and hence could be implemented to various illumination settings and seed cultivars and (b) could be utilized for exploring the vigorous germination of seed by predicting not only the ultimate percentage of germination however different indices such as uniformity and rate [44,45].…”

Section: Related Workmentioning

confidence: 99%

Prediction of Seed Germination Quality Utilizing Ensemble-Based Precision Forming

2023

IJIES

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Seed germination is a primary objective of precision agriculture. Precision agriculture, which makes extensive use of machine learning, has been the subject of recent studies on predictive analytics. These machine learning methods typically employ supervised learning models to make predictions about how successfully seeds will germinate. However, a major challenge that modern models face when attempting to make accurate predictions is the curse of dimensionality in the training corpus. The primary contribution of this manuscript is an ensemble-based method for predicting seed germination quality (EL-GQP) in precision agriculture. The accuracy of predictions can be improved using this ensemble method, which combines the positive aspects of a number of different models while minimising the negative aspects of the individual models. The proposed model is significantly superior to the current model, as demonstrated by experimental results of cross-validation on the benchmark dataset. During the simulation, work is done on the corpus dataset contains 4250 negative records and 6230 positive records.

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“…According to Penchalaiah et al [23], farmers estimated the maturity of the land and reserves to create revenue in ancient times. A farmer's life is becoming increasingly challenging due to reduced humidity levels, water levels, and specific climatic circumstances.…”

Section: Introductionmentioning

confidence: 99%

“…is one component of the irrigation system enables development in areas where water is scarce. In this method, efficient watering system planning yields the best efficiency while using the least quantity of water [23,25].…”

Section: Introductionmentioning

confidence: 99%

Development of a Wireless Sensor Network and IoT-based Smart Irrigation System

Ndunagu

Ukhurebor

Akaaza

et al. 2022

Applied and Environmental Soil Science

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This study proposes a smart irrigation system (SIS) using the drip method, which was designed and implemented using wireless sensor networks and an open-source Internet of Things (IoT) cloud computing platform (“Thingspeak.com”) for data collection, storing, data analytics, and visualization. The methodology incorporates the integration of hardware and software components to make irrigation decisions based on web resources like the weather forecast from “weather.com” and sensor values from soil samples. The data collected are then analyzed at the edge server and updated every 15 minutes. Based on the threshold value, the system starts pumping water or stops the irrigation process depending on the irrigation schedule. A web application was developed to display the result so that we could monitor and control the system using an android application edge or a web browser. Based on the data recorded and measured, the data are in comma-separated values (CSV) format and contain 143731 entries with 10 columns. The sample size used contains 5722 rows and 6 columns from the result of our machine learning algorithms using Microsoft Excel and Jupyter Notebook to process and evaluate the performance of the drip SIS, considering the soil moisture, soil temperature, sunlight, rain, and pump. The results confirm the threshold metrics classification evaluation, and some of the metrics computed from our confusion matrix are shown in the classification summary results, showing the accuracy to be 89%, the misclassification rate (error rate) is equal to 10%, the sensitivity is equal to 79%, the specificity is equal to 93%, and the precision of the model is 81%. The evaluation, when compared to K-nearest neighbours using K = 6 and K = 1, shows the prediction accuracy to be 97% and 98%. The results indicate the system is highly efficient and reliable in performing irrigation and managing water resources and can be adopted in rural areas to boost agricultural productivity.

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IoT Based Automatic Irrigation System Using Wireless Sensor Networks

Cited by 8 publications

References 47 publications

PoC Design: A Methodology for Proof-of-Concept (PoC) Development on Internet of Things Connected Dynamic Environments

PoC Design: A Methodology for Proof-of-Concept (PoC) Development on Internet of Things Connected Dynamic Environments

Prediction of Seed Germination Quality Utilizing Ensemble-Based Precision Forming

Development of a Wireless Sensor Network and IoT-based Smart Irrigation System

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