Extreme learning machine for plant diseases classification: a sustainable approach for smart agriculture

Aqel, Darah; AlZu’bi, Shadi; Mughaid, Ala; Jararweh, Yaser

doi:10.1007/s10586-021-03397-y

Cited by 41 publications

(17 citation statements)

References 41 publications

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“…Pollution and depletion of the soil leads to nothing growing on the site except weeds. It is well known that organic fertilizers integrate well into the food chain of soil-dwelling microorganisms, while providing all the nutrients to plants [11]. It takes a lot of effort to hurt.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(1) Start (2) Initialize the requirements (3) Check the available requirements details (4) If (requirements� available) (5) en check if (garden parameter� available) (6) en send the details to garden manager (7) Wait for approval (8) Assign the requirements for utilization (9) Else (10) en send the details to the audit manager (11) Waiting for approval from the garden manager (12) Provide the license audit manager (13) End. ALGORITHM 1: Multimodal segmentation technique (MMST).…”

Section: Warm Bed Managementmentioning

confidence: 99%

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Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine Learning

Prasath¹,

Govindaraj

Sasisekharan³

et al. 2022

Advances in Materials Science and Engineering

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Normally, gardens lower the ambient temperature, which would improve air quality, absorb pollutants, and produce oxygen. Trees reduce soil erosion, increase fertility, and help retain soil moisture. Decomposed leaves that fall in the garden become nutrients for tree growth and help microbes to thrive. When it comes to growing trees in a garden, one should try and choose native trees that are naturally found in a particular area. These trees are well adapted to the environment and require less maintenance. Many insects and birds rely on native trees for food and shelter. Therefore, they are best for the environment. However, not all native trees are evergreen trees. Many evergreen trees can be planted in a small garden. In this paper, a microplantation-based model was developed to enhance the biological impacts for a smart garden. Based on the garden requirements, the smart system was constructed. On this basis, the seeds are planted in the soil.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Warm Bed Managementmentioning

confidence: 99%

Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine Learning

Prasath¹,

Govindaraj

Sasisekharan³

et al. 2022

Advances in Materials Science and Engineering

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“…Machine learning and modern AI techniques have been effeciently employed in several human life applications [ 3 , 4 ], many previous researchers employed machine learning in security fields such as in [ 5 – 9 ]. Computer security attacks are classified into three types: physical attacks, synthetic attacks, and semantic attacks.…”

Section: Introductionmentioning

confidence: 99%

An intelligent cyber security phishing detection system using deep learning techniques

et al. 2022

Self Cite

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Recently, phishing attacks have become one of the most prominent social engineering attacks faced by public internet users, governments, and businesses. In response to this threat, this paper proposes to give a complete vision to what Machine learning is, what phishers are using to trick gullible users with different types of phishing attacks techniques and based on our survey that phishing emails is the most effective on the targeted sectors and users which we are going to compare as well. Therefore, more effective phishing detection technology is needed to curb the threat of phishing emails that are growing at an alarming rate in recent years, thus will discuss the techniques of mitigation of phishing by Machine learning algorithms and technical solutions that have been proposed to mitigate the problem of phishing and valuable awareness knowledge users should be aware to detect and prevent from being duped by phishing scams. In this work, we proposed a detection model using machine learning techniques by splitting the dataset to train the detection model and validating the results using the test data , to capture inherent characteristics of the email text, and other features to be classified as phishing or non-phishing using three different data sets, After making a comparison between them, we obtained that the most number of features used the most accurate and efficient results achieved. the best ML algorithm accuracy were 0.88, 1.00, and 0.97 consecutively for boosted decision tree on the applied data sets.

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“…With the successful application of deep learning technology in the fields of intelligent transportation systems [17], indoor target positioning [18], and intelligent agriculture [19], researchers have also introduced deep learning technology into the field of forest fire detection to improve the accuracy of forest fire identification by extracting deep semantic features from images. Hu et al proposed the MVMNet model to improve the accuracy and effectiveness of forest fire smoke target detection [20].…”

Section: Introductionmentioning

confidence: 99%

A Forest Fire Recognition Method Using UAV Images Based on Transfer Learning

Zhang

Wang

et al. 2022

Forests

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Timely detection of forest wildfires is of great significance to the early prevention and control of large-scale forest fires. Unmanned Aerial Vehicle(UAV) with cameras has the characteristics of wide monitoring range and strong flexibility, making it very suitable for early detection of forest fire. However, the visual angle/distance of UAV in the process of image sampling and the limited sample size of UAV labeled images limit the accuracy of forest fire recognition based on UAV images. This paper proposes a FT-ResNet50 model based on transfer learning. The model migrates the ResNet network trained on an ImageNet dataset and its initialization parameters into the target dataset of forest fire identification based on UAV images. Combined with the characteristics of the target data set, Adam and Mish functions are used to fine tune the three convolution blocks of ResNet, and focal loss function and network structure parameters are added to optimize the ResNet network, to extract more effectively deep semantic information from fire images. The experimental results show that compared with baseline models, FT-ResNet50 achieved better accuracy in forest fire identification. The recognition accuracy of the FT-ResNet50 model was 79.48%; 3.87% higher than ResNet50 and 6.22% higher than VGG16.

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Extreme learning machine for plant diseases classification: a sustainable approach for smart agriculture

Cited by 41 publications

References 41 publications

Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine Learning

Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine Learning

An intelligent cyber security phishing detection system using deep learning techniques

A Forest Fire Recognition Method Using UAV Images Based on Transfer Learning

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