Development of an Energy-efficient Adaptive IoT Gateway Model for Precision Agriculture

Jain, Prachin; Sarangi, Sanat; Bhatt, Prakruti; Pappula, Srinivasu

doi:10.1109/giots.2018.8534553

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…where denotes the number of correct classifications and denotes the total number of test images [38], [39]. Other soil related sensors can also be deployed underground and connected with the proposed IOT system for monitoring the soil health which in turn can predict the plant health.…”

Section: C) the Classification Accuracymentioning

confidence: 99%

A smart agriculturing IoT system for banana plants disease detection through inbuilt compressed sensing devices

Hemalatha²,

Radha³

et al. 2023

Multimed Tools Appl

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The Internet of Things (IoT) solutions for agriculture are rapidly growing and have the potential to transform agriculture in many aspects. In particular, the plant disease detection devices play a vital role in improving the agriculture. The visual monitoring of plants for the onset of diseases is a tedious and time-consuming task for farmers and at the same time it is less accurate. Hence an automated system with environmental data and camera sensors can serve as an alternative and effective solution for manual monitoring of plants. In this paper, a novel and efficient compressed sensing inbuilt plant disease detection device is developed which uses a foreground-based segmentation method and two step feature extraction technique to detect and classify two of the major banana diseases. A database is created for banana bunchy top and sigatokaleaf spot diseases by collecting images in real time from the fields of southern parts of Tamilnadu namely Thadiyankudisai and Thandikudi of Dindigul district, KC Patti, Muthalapuram, Suruli Patti and Kambam of Theni district and ICAR NRCB, Tiruchirapalli. The suggested device's effectiveness has been assessed in terms of the proportion of infected areas, detection accuracy, percentage of feature reduction, and classification accuracy. The prototype of the proposed device is developed and validated using the Raspberry pi board. The findings demonstrate that the suggested device achieves classification accuracy of 97.33% and detection accuracy of 96.75%.

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Section: C) the Classification Accuracymentioning

confidence: 99%

A smart agriculturing IoT system for banana plants disease detection through inbuilt compressed sensing devices

Hemalatha²,

Radha³

et al. 2023

Multimed Tools Appl

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show abstract

“…In this framework, agricultural machinery, sensors for crop environmental factors, conveying equipment, farmers' mobile terminals, and databases from higher-level agricultural departments are connected as a whole [6,7]. By monitoring the planting of crops on the farm and uploading it to the server through the analysis of big data by higher-level departments and then accurately regulating planting, fertilization, irrigation, and picking, it can allocate land more scientifically and plant crops more reasonably, and ultimately achieve a large increase in production and significant improvement in quality [8]. In addition, IoT technology uses low-latency networks to drive farm machinery work.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Tea-Picking System Based on Active Computer Vision and Internet of Things

Zhang

2021

Security and Communication Networks

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Intelligent farming machines are becoming a new trend in modern agriculture. The intelligence and automation allow planting to become data-driven, leading to more timely and cost-effective production and management of farms and improving the quality and output of farm products. This paper presents a proposal for developing a type of intelligent tea picking machine based on active computer vision and Internet of Things (IoT) techniques. The intelligent tea picking machine possesses an active vision system for new tip positioning and can automatically implement tea picking operation in the natural environment. The active vision system provided with a cross-light path of projection and camera is designed according to the actual characteristics of picking surface, where new tips can be recognized by referring to the color factor and their height information is easily acquired by fringe projection profilometry. Furthermore, the machine attaches wireless communication equipment to transmit the real-time status of the tea picking process to an intermediary platform and eventually to the Internet for extensive data analysis. The data such as color factor and quantity of new tips collected through IoT can be used for different quality and production evaluations. The focus of this paper can promote the automation and intelligence of tea pickers and agricultural machinery.

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Smart Agriculture: A Survey on Challenges and Opportunities with Recent Advancements

Thangatamilan¹,

Prasad²,

Vivekanandan

2021

Advances in Automation, Signal Processing, Instrumentation, and Control

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Development of an Energy-efficient Adaptive IoT Gateway Model for Precision Agriculture

Cited by 8 publications

References 3 publications

A smart agriculturing IoT system for banana plants disease detection through inbuilt compressed sensing devices

A smart agriculturing IoT system for banana plants disease detection through inbuilt compressed sensing devices

Intelligent Tea-Picking System Based on Active Computer Vision and Internet of Things

Smart Agriculture: A Survey on Challenges and Opportunities with Recent Advancements

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