DOST-PCAARRD-funded project seeks ways to utilize mussel shell waste

Butch S. Pagcaliwagan, DOST-PCAARRD S&T Media Services 25 September 2025

A project of the University of the Philippines Visayas (UPV) is turning the mussel shell, a discarded waste, into a valuable resource.

The project, “A Valorization of Agri-fishery Materials using Opportune Science (AVAMOS): Nanomaterials from mussel shells for agri-aquaculture applications” implemented by UPV is funded by the Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development of the Department of Science and Technology (DOST-PCAARRD).

Mussels, locally known as “tahong,” consist of 20–30% meat with 70–80% shell. Owing to its widespread consumption as an affordable source of protein among Filipinos, mussels produce substantial shell waste at 6,000 metric tons in Western Visayas alone, which can cause health and environmental problems.

At the recently concluded 2025 Regional Science, Technology and Innovation Week (RSTW) in Caraga held at the Robinsons Mall in Butuan City, products of the AVAMOS project’s innovative valorization of mussel shell waste were showcased.

Musorb, a sustainable and cost-effective solution for industrial wastewater treatment using upcycled mussel shells as an adsorbent, was exhibited. It was designed to remove ammonia, phosphate, and carbon dioxide from wastewater. It is ideal for industrial effluent treatment and aquaculture water management.

The other featured mussel shell waste-derived product was the humic-acid-functionalized nano-hydroxyapatite, a slow-release fertilizer. It can be used for both aquaculture and agriculture purposes either as a soil nutrient replenishment or as a bioavailable phosphate fertilizer, or for remediation of polluted water bodies.

Traditionally, mussel processing in the country focused only on the meat. The AVAMOS project, on the other hand, recognized the potentials of mussel shells for nanomaterial production and thus generated mussel shell waste processing innovations anchored on green technology and circular economy for use in aquaculture and agriculture.

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PEST D-Tech Deploys Drones to Control Pest and Disease Infestations in Corn and Onion

An initiative from Central Luzon State University (CLSU) is set to mitigate pest infestations and outbreaks in corn and onion through PEST D-Tech, a crop monitoring system enabled by drones.  The project will later establish data integration and cooperation between UGV of Taiwan and UAV or drone of the Philippines.

Led by Dr. Gella Patria L. Abella and Dr. Elaida R. Fiegalan, the project integrates drone technology with agriculture through normalized difference vegetation index (NDVI) analysis using unmanned aerial vehicles (UAV) imagery to enable precise monitoring of crop health of corn and onion. 

The project is supported by the Department of Science and Technology through the Manila Economic and Cultural Office – Taipei Economic and Cultural Office (MECO-TECO) Joint Research Program and monitored by the Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development of the Department of Science and Technology (DOST-PCAARRD).

To capture high-resolution images of corn and onion fields and to gather multispectral data of vegetation vigor and stress, CLSU deployed UAV equipped with powerful multispectral cameras and sensors in selected municipalities of Tarlac and Nueva Ecija. 

The mapping of NDVI results are being done through the use of the Quantum Geographic Information System (QGIS) which supports the viewing, editing, and analysis of geospatial data. These maps serve as crucial tools for targeted intervention strategies, allowing for efficient allocation of resources and proactive management efforts in vulnerable zones. 

With two comprehensive pest and disease infestation maps already developed by the project team, farmers and agricultural officers are provided spatially explicit maps that provide a detailed visual representation of the distribution and intensity of key pests and diseases affecting their agricultural areas.

Unmanned ground vehicles (UGV) are also going to be explored and deployed by the project in its third implementation year. Through this, farmers and other stakeholders can quickly and efficiently assess large fields, detect early signs of diseases or nutrient deficiency, and make data-driven decisions for targeted irrigation, fertilization, and pest control.

Set to be completed by March 2026, the project will be creating tailored strategies for managing crop pests and diseases for corn and onion. The development of this initiative is expected to benefit municipal agricultural officers, cooperatives, and non-government stakeholders as it can proactively prevent widespread infestations and outbreaks through the timely release of pest and disease advisories for farmers.

Thea Mariel N. Valdeavilla and Maria Teresa L. De Guzman, DOST-PCAARRD S&T Media Services 07 October 2025

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UP Cebu’s MANGGA system automates mango grading for domestic and export markets

Thea Mariel N. Valdeavilla, Kathleen Faith Jay O. Villarma, DOST-PCAARRD S&T Media Services 16 October 2025

A locally-developed system by the University of the Philippines Cebu (UP Cebu) integrates computer vision and deep learning techniques to grade mangoes for domestic and export markets.

The Mango Automated Neuralnet Generic Grade Assignor (MANGGA) system features a fully integrated mango grading and classification system that is capable of processing up to 800 mangoes per hour. With the increasing demand for Philippine mangoes, the system’s ability to classify mangoes efficiently and accurately streamlines the process of fruit evaluation in compliance with the Philippine National Standards. 

The initial field deployment of the MANGGA system in Guba, Cebu City demonstrates its strong potential as a reliable alternative to manual sorting.

UP Cebu’s project leader, Dr. Jonnifer Sinogaya, reported that the MANGGA system can sort mangoes into three groups: small (200–249 grams [g]), medium (250–299g), and large (300–349g). The system includes three evaluation modules: 1) a size estimation model using image processing techniques that has achieved 98.05% accuracy; 2) a multi-input convolutional neural network (CNN) grading model based on general appearance that recorded a 95.4% F1-score; and   3) a CNN-based maturity detection model utilizing stem-end image that reached an accuracy of 90.62%.

By standardizing processing across batches, the system dramatically enhances postharvest mango operations, boosting efficiency, minimizing variability, and ensuring a more consistent, higher-quality product for both local and international markets. This is particularly evident insize estimation, where the MANGGA system boasts 92% accuracy compared to the manual sorter’s 45%.

The system was developed by UP Cebu Center for Environmental Informatics, in partnership with the Department of Agriculture – Region 7, Technological Institute of the Philippines, and Bureau of Plant Industry – National Mango Research and Development Center in Guimaras, through the funding support of the Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development of the Department of Science and Technology (DOST-PCAARRD).