Modern Agriculture Using Wireless Sensor Network (WSN)

D, Devi Kala Rathinam.; Surendran, D; Shilpa, A; A, Santhiya Grace.; Sherin, J

doi:10.1109/icaccs.2019.8728284

Cited by 55 publications

(32 citation statements)

References 14 publications

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“…2) Smart Agriculture : In any smart agriculture system, each sensor node receives data from location sensor, optical sensor, pH sensor, humidity sensor, temperature sensor, and airflow sensor [24] [25]. 3) Industrial Environments : We need sensors for temperature monitoring, vibration detection, flow rate analysis, and strain monitoring for developing any smart industry [26].…”

Section: )mentioning

confidence: 99%

RecordBOX: A Framework for a Lightweight Record Oriented Storage System For IoT Sensor Nodes

Bhavsar¹,

Patel²

2021

Preprint

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Section: )mentioning

confidence: 99%

RecordBOX: A Framework for a Lightweight Record Oriented Storage System For IoT Sensor Nodes

Bhavsar¹,

Patel²

2021

Preprint

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“…The kernel function returns the internal product in the middle of two points in an appropriate feature space. The standard Kernel Function is given by (5) (5) In the proposed paper, Linear Kernel and Polynomial Kernel are used to train and test the SVM model to obtain good results. Linear Kernel and Polynomial Kernel are defined by ( 6) and ( 7) respectively.…”

Section: Keypoints Clustering Using K-means Algorithmmentioning

confidence: 99%

“…Temperature affects all the phases in plant development (vegetative growth, flowering, fruit seting, and fruit ripening). D. Devi Kala Rathinam et al [5] conducted a study on solutions for real-time problems using Wireless Sensor Network (WSN) technology. Recently WSN established widespread applications in several fields like transport, medical, military, mobile phones, home appliances, and so on.…”

Section: Introductionmentioning

confidence: 99%

A Study of Factors Influencing Plant Growth by WSN Approach and Plant Nutrient Deficiency Classification in Tomato Using SVM

Kusanur¹,

Chakravarthi²

2021

JUSST

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Soil temperature and humidity straight away influence plant growth and the availability of plant nutrients. In this work, we carried out experiments to identify the relationship between climatic parameters and plant nutrients. When the relative humidity was very high, deficiency symptoms were shown on plant leaves and fruits. But, recognizing and managing these plant nutrients manually would become difficult. However, no much research has been done in this field. The main objective of this research was to propose a machine learning model to manage nutrient deficiencies in the plant. There were two main phases in the proposed research. In the first phase, the humidity, temperature, and soil moisture in the greenhouse environment were collected using WSN and the influence of these parameters on the growth of plants was studied. During experimentation, it was investigated that the transpiration rate decreased significantly and the macronutrient contents in the plant leave decreased when the humidity was 95%. In the second phase, a machine learning model was developed to identify and classify nutrient deficiency symptoms in a tomato plant. A total of 880 images were collected from Bingo images to form a dataset. Among all these images, 80% (704 images) of the dataset were used to train the machine learning model and 20% (176 images) of the dataset were used for testing the model performance. In this study, we selected K-means Clustering for key points detection and SVM for classification and prediction of nutrient stress in the plant. SVM using linear kernel performed better with the accuracy rates of 89.77 % as compared to SVM using a polynomial kernel.

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“…However, wireless sensor nodes often use a limited energy source like a battery (rechargeable and non-rechargeable) and thus leading to the need to save energy and prolong the life of the wireless sensor network. Therefore, one of the challenging sensor network performance issues is related to energy-saving operating mechanisms [1][2][3][4].…”

Section: Introduction *mentioning

confidence: 99%

Untitled

2021

JST: SSAD

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Wireless sensor networks (WSN) play an important role in IoT (Internet of Things) as an interconnecting infrastructure. Working with a limited energy source, the vital challenge for WSN is to prolong the network lifetime as an important performance metric. Furthermore, the limitations of regular transmission technologies create localized network areas of a multi-hop fashion form that adds more constraints to enhance the network performance. Hence, the clustering strategies initially have solved these problems and received the attention of many studies, an approach using unequal clustering strategy has yielded some positive results since consumed energy gaps are avoided in regions near base stations. However, the routing strategy among cluster heads in multi-hop wireless networks is still a big challenge because of its inefficiency in energy consumption aspects. Therefore, in this paper, we propose a novel method that combining an unequal clustering problem and a simple multi-hop routing to prolong network life. The numerical results show that the proposed solution is more effective than other models in recent studies

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Modern Agriculture Using Wireless Sensor Network (WSN)

Cited by 55 publications

References 14 publications

RecordBOX: A Framework for a Lightweight Record Oriented Storage System For IoT Sensor Nodes

RecordBOX: A Framework for a Lightweight Record Oriented Storage System For IoT Sensor Nodes

A Study of Factors Influencing Plant Growth by WSN Approach and Plant Nutrient Deficiency Classification in Tomato Using SVM

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