Research

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

13 Jun 2025

UP Los Baños

Large language models offer many opportunities but also pose considerable challenges

With the advancement of artificial intelligence (AI) technologies, the availability of data, and the advancement of hardware technologies, applications such...

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10 Jun 2025

UP

A virtual health and physical education program during COVID-19 struggled with low student engagement and ineffective online assessment

The study explored a virtual health and physical education program implemented during COVID-19 school closures. It centered on practices, teacher...

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09 Jun 2025

UP Diliman

There is a weak relationship between 3D mandibular shape and diet in extant primates

This is a study that uses 3D shape analyses to investigate if there is a relationship between mandible shape in...

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09 Jun 2025

UP Diliman

The presumed invisibility and “mute” voices of Pinay lesbian writers parallel the similar invisibility of nature in Philippine anthologies

In the introduction to Tingle: Anthology of Pinay Lesbian Writing (2021), the anthology’s editor, Jhoanna Lynn B. Cruz, points to...

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03 Jun 2025

UP Diliman

AI can enhance conceptual understanding and boost self-confidence in mathematics

AI is increasingly integrated into educational settings, offering personalized learning experiences that adapt to individual needs. While AI has the...

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28 May 2025

UP Diliman

SPECIAL FEATURE: How did Filipino athletes cope with the Covid-19 pandemic?

The COVID-19 pandemic deeply affected social and economic life around the world. To protect public health, many countries enforced lockdowns...

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21 May 2025

UP Diliman

Machine learning holds great promise for advancing hadron physics and deepening our understanding of the subatomic world

We investigated a peak signal around MeV in the invariant mass spectrum, which has intrigued physicists as a potential candidate...

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20 May 2025

UP Diliman

Cigarette demand varies with changes in price

This study looks into how recent tax reforms on cigarettes and fermented liquor in the Philippines have affected people’s consumption...

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08 May 2025

UP Diliman

There is a weak positive correlation between land surface temperature and concentrations of carbon monoxide and nitrogen dioxide

This study looks at how land surface temperature (LST) from satellite data, elevation, and air quality parameters are related in...

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07 May 2025

Proper water management and soil sanitation can help prevent crop losses caused by the Paramyrothecium fungus

Paramyrothecium is a fungus that can cause plant diseases like coffee leaf spots, muskmelon crown rot, and eggplant crater rot....

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02 May 2025

Simulations show that rosette nanotube is a potential vehicle for anticancer drugs such as paclitaxel

Anticancer drugs such as paclitaxel (PTX) affect the healthy cells apart from the cancer cells, causing severe side effects. Targeted...

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30 Apr 2025

Leni Robredo’s presidential campaign offered supporters spaces that reflected their hopes by highlighting gender and sexuality issues

This paper examines how Leni Robredo’s presidential campaign during the 2022 Philippines elections used language and visuals to create a...

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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