An IoT Based Plant Health Monitoring System Implementing Image Processing

Pavel, Monirul Islam; Kamruzzaman, Syed; Hasan, Sadman Sakib; Sabuj, Saifur Rahman

doi:10.1109/ccoms.2019.8821782

Cited by 37 publications

(11 citation statements)

References 16 publications

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“…Quadcopter that autonomously traverse and take aerial shots of a specified field for NDVI analysis [112] ---AI-based systems to detect and identify crop disease [47,[74][75][76][77][113][114][115][116][117] Weed mapping and AI-based weed detection [48,71,72] --Pest recognition using AI-based methods [73,118,119] --…”

Section: Weather and Ghgsmentioning

confidence: 99%

Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities

et al. 2021

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Investment in technological research is imperative to stimulate the development of sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and sensor networks, robotics, artificial intelligence, big data, cloud computing, etc. foster the transition towards the Agriculture 4.0 era. This fourth revolution is currently seen as a possible solution for improving agricultural growth, ensuring the future needs of the global population in a fair, resilient and sustainable way. In this context, this article aims at characterising the current Agriculture 4.0 landscape. Emerging trends were compiled using a semi-automated process by analysing relevant scientific publications published in the past ten years. Subsequently, a literature review focusing these trends was conducted, with a particular emphasis on their applications in real environments. From the results of the study, some challenges are discussed, as well as opportunities for future research. Finally, a high-level cloud-based IoT architecture is presented, serving as foundation for designing future smart agricultural systems. It is expected that this work will positively impact the research around Agriculture 4.0 systems, providing a clear characterisation of the concept along with guidelines to assist the actors in a successful transition towards the digitalisation of the sector.

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Section: Weather and Ghgsmentioning

confidence: 99%

Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities

et al. 2021

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“…In this thesis, CNN [8,9,10] is applied for text classification where main dataset represents the mentioned two statistical characters relying on the fact that neighboring words in a sentence present dependency, however, their processing is not straight forward. In image classification, pixels are some integer values with specific threshold value, but in the case of sentence or words, we need to encode first before fed to the networks [11]. To do so, we applied NLTK library [12]of python for using vocabulary which is structured as an list containing words which are shown in the set of comment's texts.…”

Section: Cnn For Text Classificationmentioning

confidence: 99%

Classification of Vulgar Comments Usingconvolutional Neural Networkwith Gluon Natural Language Processing

Jahan¹,

Shakir²,

Imran³

et al. 2020

IJAR

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Withthe advancement of technology, the virtual platform and social media have become an important part of people's daily life. The social media allows users to communicate, to express their feelings and to discuss on various topics. But cyber bullying and vulgar or toxic comments become an alarming problem in social media. In this paper, a system has been proposed to detect and classify vulgar comments using convolutional neural networking with gluon natural language processing. The system can classify toxic, hate speech, insult, obscene, threat and normal text by training from a huge dataset obtained from Kaggle, extracting comments from YouTube, facebook through extension of browser. The system shows 95.4% accuracy to detect and classify the vulgar comments.

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“…Kim et al [19] used the IoT technique and a machine learning algorithm to classify plant diseases at an early stage. An IoT-based monitoring system for precision agricultural applications such as epidemic disease control was developed by Pavel et al [20]; an expert system was also developed to make decisions regarding the diseases. A survey on the current techniques and prediction models based on image processing and the role of the IoT being applied for identification, detection as well as quantification of tomato plant diseases was shown by Khattab et al, Verma et al, and Diyan et al [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Design of a Novel Remote Monitoring System for Smart Greenhouses Using the Internet of Things and Deep Convolutional Neural Networks

et al. 2021

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To support farmers and improve the quality of crops production, designing of smart greenhouses is becoming indispensable. In this paper, a novel prototype for remote monitoring of a greenhouse is designed. The prototype allows creating an adequate artificial environment inside the greenhouse (e.g., water irrigation, ventilation, light intensity, and CO2 concentration). Thanks to the Internet of things technique, the parameters controlled (air temperature, relative humidity, capacitive soil moisture, light intensity, and CO2 concentration) were measured and uploaded to a designed webpage using appropriate sensors with a low-cost Wi-Fi module (NodeMCU V3). An Android mobile application was also developed using an A6 GSM module for notifying farmers (e.g., sending a warning message in case of any anomaly) regarding the state of the plants. A low-cost camera was used to collect and send images of the plants via the webpage for possible diseases identification and classification. In this context, a deep learning convolutional neural network was developed and implemented into a Raspberry Pi 4. To supply the prototype, a small-scale photovoltaic system was built. The experimental results showed the feasibility and demonstrated the ability of the prototype to monitor and control the greenhouse remotely, as well as to identify the state of the plants. The designed smart prototype can offer real-time remote measuring and sensing services to farmers.

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An IoT Based Plant Health Monitoring System Implementing Image Processing

Cited by 37 publications

References 16 publications

Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities

Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities

Classification of Vulgar Comments Usingconvolutional Neural Networkwith Gluon Natural Language Processing

Design of a Novel Remote Monitoring System for Smart Greenhouses Using the Internet of Things and Deep Convolutional Neural Networks

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