Development of a Low-Cost Hydroelectric Generation System for Application on Water Pipelines

Sani, Mohd Aliff Afira; Azharshah, Muhammad Daniel Asyraf; Yusof, Mohd Ismail; Sama’in, Mohd Usairy Syafiq; Sani, Nor Samsiah

doi:10.1007/978-3-031-01488-8_12

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Furthermore, a real-time dataset detailing all banana leaf illnesses would be used in the proposed effort [22]. The fact that IR 4.0 technology has been utilized extensively in various domains to achieve superior outcomes compared to more traditional approaches is demonstrated by the inclusion of relevant projects [23][24][25][26].…”

Section: Related Workmentioning

confidence: 99%

Utilizing Aerial Imagery and Deep Learning Techniques for Identifying Banana Plants Diseases

Aliff,

Luqman,

Yusof

et al. 2024

ITM Web Conf.

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The primary agricultural pursuit in Malaysia centres around banana cultivation; however, this vital crop faces the daunting challenge of multiple diseases that hinder its growth. The adverse consequences of these diseases extend beyond the farms to impact the nation’s economy. To empower farmers with the tools to promptly identify and categorize these diseases, image processing techniques offer a valuable solution. This research leverages deep learning Convolutional Neural Networks (CNN) implemented through MATLAB in conjunction with a DJI drone. By harnessing this technology, the system can automatically detect and classify major banana diseases. The study meticulously fine-tuned several hyperparameters to achieve impressive training and testing accuracy levels. The results revealed that the model attained its highest training accuracy of 81.27% at epoch 8 and its lowest accuracy of 78.40% at epoch 4, demonstrating its potential to aid in early disease detection and classification in banana crops.

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Section: Related Workmentioning

confidence: 99%

Utilizing Aerial Imagery and Deep Learning Techniques for Identifying Banana Plants Diseases

Aliff,

Luqman,

Yusof

et al. 2024

ITM Web Conf.

Self Cite

View full text Add to dashboard Cite

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New Method for State Express Control of Unstable Hydrocarbon Media and Their Mixtures

et al. 2023

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All requirements for the express control of unstable hydrocarbon media and devices for their implementation to obtain reliable measurement results are determined. The features of the control of volatile hydrocarbon media and their mixtures with industrial small-sized refractometers operating on the effect of total internal reflection are considered. The new method for determining components and the ratio between concentrations of them in a mixture of volatile hydrocarbon media is developed. There are no analogs to this method in the world. This method allows determining the presence of one volatile hydrocarbon medium in another in a ratio of 1% to 99% or more. The new design of a small-sized refractometer has been developed, which makes it possible to measure the refractive index in visible light. In the refractometer’s newly developed design, the refractive index measurement range of a condensed medium is from 1.2500 to 1.7000 (measurement error 0.0004). New dependencies of change in density ρ for various grades of hydrocarbon fuel (in the Russian Federation) on temperature T have been established. After adjusting the coefficients, these dependencies can be used for fuel produced in various world countries. The results of the research on different gasoline types and their mixtures are presented.

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Development of a Low-Cost Hydroelectric Generation System for Application on Water Pipelines

Cited by 2 publications

References 12 publications

Utilizing Aerial Imagery and Deep Learning Techniques for Identifying Banana Plants Diseases

Utilizing Aerial Imagery and Deep Learning Techniques for Identifying Banana Plants Diseases

New Method for State Express Control of Unstable Hydrocarbon Media and Their Mixtures

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