Moisture level detector for green coffee bean using capacitive method and iot/ Ralph C. Admendilla, Ma. Cristina L. Agustin, Pamela Sabelle D. Nagusara, Clive Joriz M. Oxima, and Denisse P. Quejada.--

Bachelor's thesis

College of Industrial Technology.-- Bachelor of engineering technology major in computer engineering technology: Technological University of the Philippines, 2025.

Includes bibliographic references and index.

In the Philippines, coffee is consumed by many because of its economic impact and cultural
significance. The moisture content of coffee determines its quality because less than or too
much can cause it to deteriorate. Some drying techniques are destructive, time-consuming,

and expensive so moisture measurement of green coffee beans with an accurate, cost-
effective, and rapid approach remains a dilemma. Hence, this study developed a

capacitance-based method of a drying and storage with controlled temperature and
humidity system which will adapt the Scrum Methodology. The researchers assessed the
effectiveness of the system in terms of accuracy, efficiency, and timeliness; and evaluated
it using ISO 25010:2023. Calibration and paired sample t-test was done to determine the
capacitive moisture sensor’s accuracy. Results showed that there’s no significant
difference between the moisture content readings of the system’s capacitive moisture
sensor and the industry moisture meter (p-value = 0.63 ≥ 0.05). With a strong correlation
of 0.98, a small mean difference of 0.07, and a close variance; this confirms its consistency
and reliability. Furthermore, the initial moisture content and drying time are both factors
in the 0.47 or 47% variability in the final moisture content for 20 trials done by the
researchers. The timeliness of the system also showed that ideal GCB only takes up 91
seconds to complete, while under-dried GCB takes 141 seconds, and highly under-dried
takes the longest time for 156 seconds. The evaluation results also indicated that the system
is “Very Good” with an average evaluation grade of x̄= 4.42. Finally, the topic pertains to
UN-SDG 9, 2, and 3, Agriculture, Aquatic and Natural Resources (HNRDA-AANR) and
Climate Change Adaptation and Mitigation and Disaster Risk Reduction under Section 3
of the DOST Agenda, and the TUP R&D agenda “Transforming the future through
emerging technologies and engineering for IoT” and “Universal solutions for food security
and health”