Research

As the national university, we champion and support innovative research that addresses the country’s most pressing challenges.

15 Apr 2025

Survivors of the 1974 Battle of Jolo wish to forget the tragedy but their memories expose the recent struggles of Muslim Mindanao

Moving beyond the disinformation surrounding Filipino memory construction of martial law this study delves into the experiences of civilians during...

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11 Apr 2025

This study of the largest fast food group in the Philippines acquiring its competition adds to the limited, but growing, inventory of business case studies

This case is about the acquisition by the largest quick service restaurant (QSR) group in the Philippines, Jollibee Foods Corporation...

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25 Mar 2025

Tracing Darna’s roots in Filipino folk culture challenges the typically male-centered Western superhero genre

Power is one of the most distinguishing features of the superhero genre. In this regard, one of the most recognizable...

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18 Mar 2025

UP Manila

The clustering of tuberculosis cases could be linked to the El Niño phenomenon

This study focused on tuberculosis (TB) cases in Region III of the Philippines for the years 2019, 2020, and 2021,...

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17 Mar 2025

Perceived factors to teenage pregnancy such as technological influence influence the manner of delivering services

Teenage pregnancy remains to be a major concern in the health of Filipino adolescents despite the nationwide adoption of sexual...

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07 Mar 2025

UP Open University

Research uncovers new information on the voice of the Philippine flat-headed frog found only in Palawan

This study describes the call of Barbourula busuangensis, a peculiar and elusive fully aquatic frog endemic to the Palawan archipelago of...

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07 Mar 2025

UP Manila

High levels of body association lowers the risk of depression, anxiety and stress among Filipino emerging adults

Among Filipino emerging adults, prevalence rates of severe and extreme levels of depression, anxiety, and stress are relatively high. High...

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07 Mar 2025

Research uncovers new information on the voice of the Philippine flat-headed frog found only in Palawan

This study describes the call of Barbourula busuangensis, a peculiar and elusive fully aquatic frog endemic to the Palawan archipelago...

Read More

06 Mar 2025

Lemongrass has the potential to control the growth of fungi from skin mycoses, foodborne illnesses and opportunistic infections

Lemongrass is an abundant medicinal plant that is used as an herbal remedy for fungal infections. Despite being widespread, to...

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04 Mar 2025

UP Diliman

A hybrid Internet of Things-based smart system performs better than a purely wireless sensor in monitoring indoor hydroponics farms

Internet of Things (IoT) is a one of the technologies that are being applied to various applications and scenarios. This...

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28 Feb 2025

UP Diliman

Transforming higher education requires a more diverse and inclusive landscape especially for women and minority groups in STEAM

This research dives into how higher education can be transformed through leadership focused on Equity, Diversity, and Inclusion (EDI). In...

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26 Feb 2025

UP Diliman

The proposed Input-Process-Output-Outcome/Result Model evaluates the success of the Philippine Olympic Committee Athletes’ Commission

The paper discusses the various institutions, laws, policies and programs of Olympic governance in the Philippines from the American era...

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Research

As with many Asian countries, rice is a principal food in the Philippines providing nearly half of the daily caloric needs of Filipinos. However, rice plants are also susceptible to many diseases whose spread is induced by weather conditions such as high humidity and rainfall producing detrimental effects on crop’s yield and thus affecting the country’s food security. Hence, a rapid, early, and correct detection of rice plant disease is crucial to prevent spread of the disease mitigating its detrimental effects through an early institution of preventive measures.

In this study, researchers applied a deep learning approach using convolutional neural networks in the assessment of rice plant disease. Results showed superior diagnostic performance with these models. As such, these deep learning models can be useful complementary tools which can be deployed as quick and non-invasive diagnostic support instruments assisting farmers in the evaluation of rice diseases especially in communities where agricultural experts are limited. Farmers would gain more valid outcomes with these new technological diagnostic approaches, enabling them to institute cost-effective measures. Thus, effective management of rice plant diseases, optimized and efficient use of available resources leading to improved rice crop productivity can be achieved. A working partnership of agriculturists and machine learning enthusiasts is crucial to achieve the desired goal of early identification of rice plant diseases for prompt intervention efforts to be instituted.

The purpose of the study is to ascertain the distinguishing capability of convolutional neural networks in the recognition of rice plant disease. Deep learning models (base convolutional neural and pre-trained networks) were applied to the Philippine Rice Disease Dataset to diagnose rice plant diseases. VGG16 obtained the best performance with a 96% accuracy, 99% sensitivity, 97% precision, 98% F1-score, and a 0.834 normalized Matthews Correlation Coefficient. InceptionV3 also generated superior performance while the base model had a lower diagnostic capability. These models can be useful complementary tools which may be deployed as quick and non-invasive diagnostic support instruments assisting farmers in the evaluation of rice diseases especially in communities where agricultural experts are limited. Farmers would gain more valid outcomes with these technological approaches, enabling them to institute cost-effective measures. Thus, effective management of rice plant diseases, optimized and efficient use of available resources leading to improved rice crop productivity can be achieved.

Authors: Vincent Peter C. Magboo and Ma. Sheila A. Magboo (Dept. of Physical Sciences and Mathematics, University of the Philippines Manila)

Read the full paper: https://ieeexplore.ieee.org/document/102867499