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All Journal International Journal of Electrical and Computer Engineering
Yusof, Mohd Faizal
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Computer Science & IT Electrical & Electronics Engineering

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Empowering crop cultivation: harnessing internet of things for smart agriculture monitoring Alsayaydeh, Jamil Abedalrahim Jamil; Yusof, Mohd Faizal; Magenthiran, Mithilanandini S.; Hamzah, Rostam Affendi; Mustaffa, Izadora; Herawan, Safarudin Gazali
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp6023-6035

Abstract

Agriculture, the foundation of human civilization, has relied on manual practices in the face of unpredictable weather for millennia. The contemporary era, however, witnesses the transformative potential of the Internet of things (IoT) in agriculture. This paper introduces an innovative IoT-driven smart agriculture system empowered by Arduino technology, making a significant contribution to the field. It integrates key components: a temperature sensor, a soil moisture sensor, a light-dependent resistor, a water pump, and a Wi-Fi module. The system vigilantly monitors vital environmental parameters: temperature, light intensity, and soil moisture levels. Upon surpassing 30°C, an automatic cooling fan alleviates heat stress, while sub-300CD light levels trigger light-emitting diode lighting for optimal growth. Real-time soil moisture data is relayed to the “Blynk” mobile app. Temperature thresholds align with specific crops, and users can manage the water pump via Blynk when manual intervention is required. This work advances agricultural practices, optimizing water management by crop type. Through precise coordination of soil moisture, temperature, and light intensity, the system enhances productivity while conserving water resources and maintaining fertilizer balance.
Detection of fungal diseases of plants from leaf images based on neural network technologies Fedorchenko, Ievgen; Yusof, Mohd Faizal; Oliinyk, Andrii; Chornobuk, Maksym; Khokhlov, Mykola; Alsayaydeh, Jamil Abedalrahim Jamil
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5866-5873

Abstract

The paper addresses the issue of automating the detection of fungal diseases in plants using digital images of their leaves. The spread of diseases among agricultural and horticultural crops causes significant economic losses worldwide, making the development of an effective and affordable solution to this problem highly valuable. Literature analysis suggests the viability of employing a convolutional neural network (CNN) to tackle this issue. The 'Fungus recognition' model was developed based on a custom CNN architecture using the TensorFlow library. The model underwent training and testing on a publicly available dataset. Test results show that 'Fungus recognition' achieves a classification accuracy level of 90%, surpassing similar models considered. The developed model can be adapted for deployment on mobile computing devices, paving the way for its practical implementation in agriculture and horticulture.
An autopilot-based method for unmanned aerial vehicles trajectories control and adjustment Mochurad, Lesia; Alsayaydeh, Jamil; Yusof, Mohd Faizal
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4154-4166

Abstract

In today's world, the rapid development of aviation technologies, particularly unmanned aerial vehicles (UAVs), presents new challenges and opportunities. UAVs are utilized across various industries, including scientific research, military, robotics, surveying, logistics, and postal delivery. However, to ensure efficient and safe operation, UAVs require a reliable autopilot system that delivers precise navigation control and flight stability. This paper introduces a method for controlling and adjusting UAV trajectories, which enhances accuracy in environments and tasks corresponding to the first or second level of autonomy. It outperforms the linear-quadratic method and the unmodified predictive control method by 43% and 74%, respectively. The findings of this study can be applied to the development and modernization of new UAV, as well as the advancement of new UAV motion control systems, thereby enhancing their quality and efficiency.
Co-Authors Alsayaydeh, Jamil Alsayaydeh, Jamil Abedalrahim Jamil Chornobuk, Maksym Fedorchenko, Ievgen Hamzah, Rostam Affendi Herawan, Safarudin Gazali Khokhlov, Mykola Magenthiran, Mithilanandini S. Mochurad, Lesia Mustaffa, Izadora Oliinyk, Andrii