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All Journal International Journal of Electrical and Computer Engineering
Alsayaydeh, Jamil Abedalrahim Jamil
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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.
Handwritten text recognition system using Raspberry Pi with OpenCV TensorFlow Alsayaydeh, Jamil Abedalrahim Jamil; Jie, Tommy Lee Chuin; Bacarra, Rex; Ogunshola, Benny; Yaacob, Noorayisahbe Mohd
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2291-2303

Abstract

Handwritten text recognition (HTR) technology has brought about a revolution in the way handwritten data is converted and analyzed. This proposed work focuses on developing a HTR system using deep learning through advanced deep learning architecture and techniques. The aim is to create a model for real-time analysis and detection of handwritten texts. The proposed deep learning architecture that is convolutional neural networks (CNNs), is investigated and implemented with tools like OpenCV and TensorFlow. The model is trained on large handwritten datasets to enhance recognition accuracy. The system’s performance is evaluated based on accuracy, precision, real-time capabilities, and potential for deployment on platforms like Raspberry Pi. The actual outcome is a robust HTR system that can convert handwritten text to digital formats accurately. The developed system has achieved a high accuracy rate of 91.58% in recognizing English alphabets and digits and outperformed other models with 81.77% mAP, 78.85% precision, 79.32% recall, 79.46% F1-Score, and 82.4% receiver operating characteristic (ROC). This research contributes to the advancement of HTR technology by enhancing its precision and utility.
Co-Authors Bacarra, Rex Chornobuk, Maksym Fedorchenko, Ievgen Hamzah, Rostam Affendi Herawan, Safarudin Gazali Jie, Tommy Lee Chuin Khokhlov, Mykola Magenthiran, Mithilanandini S. Mustaffa, Izadora Ogunshola, Benny Oliinyk, Andrii Yaacob, Noorayisahbe Mohd Yusof, Mohd Faizal