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All Journal Teknomekanik Innovation in Engineering
Dominic Olango Cagadas
Department of Electronics Technology, University of Science and Technology of Southern Philippines, Philippines
Engineering Mechanical Engineering

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Classifying four maturity categories of coffee cherry using CNN-VGG19 Dominic Olango Cagadas; Dwi Sudarno Putra; Kristine Mae Paboreal Dunque; Meri Azmi
Teknomekanik Vol. 7 No. 2 (2024): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/teknomekanik.v7i2.31072

Abstract

The local coffee farmers employ manual inspection to identify the maturity of coffee cherries that are inefficient in labor and time. Thus, the objective of this study is to develop a CNN-VGG19 algorithm model that can accurately detect the maturity image of coffee cherry samples, and classify them into: unripe, semi-ripe, ripe, and overripe categories. The proposed solution will provide local coffee farmers with an automated and more accurate classification of the quality of coffee cherries. The visual geometry group-19 was employed to increase the object recognition model performance of the proposed algorithm while maintaining higher accuracy and quicker throughput, thus increasing revenues. The images are utilized as training and test set data. They were then processed by using the feature extraction of CNN-VGG19 deep learning model, and got four coffee cherry maturity classes. The model architecture attained a 90.00 % accuracy. Furthermore, the increase in both the validation and training accuracy graph with a corresponding decrease in both the validation and training loss graph propounds that the model performance has improved.
Smart home irrigation: A solar-powered Wi-Fi-based automated drip system with real-time soil moisture sensing Dominic Olango Cagadas; Cran Leigh Mae Adis Salamanca
Innovation in Engineering Vol. 3 No. 2 (2026): (September 2026) – In Progress
Publisher : Researcher and Lecturer Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58712/ie.v3i2.46

Abstract

Manual irrigation in home gardening often causes inconsistent watering, unnecessary labor, and inefficient water use. This study developed and evaluated a solar-powered, Wi-Fi-based automated drip irrigation system with real-time soil moisture sensing for small-scale home gardens. The prototype integrated an ESP32 microcontroller, capacitive soil moisture sensors, pH sensor, float sensors, solenoid valves, DC pumps, a 150-W solar panel, a 12.8-V 100-Ah battery, and an Android-based mobile application. Four planting plots were used to test automated irrigation, soil moisture monitoring, water pH monitoring, tank-level detection, drainage monitoring, and mobile-based control. System performance was observed through 15-day plant monitoring and 5-day data logging. Results showed that the system supplied water only when soil moisture dropped below the set threshold and avoided unnecessary pump activation. Evaluations by 20 faculty members, IT experts, agriculturists, and farmers yielded excellent ratings for functionality (4.81), performance (4.74), and usability (4.89), with an overall mean of 4.81. The study demonstrates a low-cost, renewable-energy-based, mobile-controlled irrigation solution suitable for household gardening.
Co-Authors Cran Leigh Mae Adis Salamanca Dwi Sudarno Putra Kristine Mae Paboreal Dunque Meri Azmi