By: Pablo A. Tariman
(Published in its print edition on August 3-9, 2024)
Just a few days after their sendoff concert in Manila last June 27, the Manila Symphony Junior Orchestra (MSJO) under the baton of Jeffrey Solares was awarded the gold prize plus the Grand Prix in the 13th Bratislava International Youth Music in Slovakia July 4.
The ranking: first, Manila Symphony Junior Orchestra; second Fire Choir, United States; 3rd. St. Margaret’s Anglican Girls ‘School Choir and fourth, Afrikaans Hoër Meisieskool Pretoria.
Over 50 youth participants from 13 countries attended the 3-day festival for youth choirs and orchestras presided by the festival’s artistic director Prof Milan Kolena.
Distinguished musicians from Slovakia, USA, Australia and Italy formed the jury.
Of the 17 ensembles in the festival, only two were given the Grand Prix, one for choir- the Afrikaans Hoer Meisisskool Pretoria from South Africa, and one for orchestra, the Manila Symphony Junior Orchestra from the Philippines.
A first-timer in the Bratislava music festival, the MSJO got the Grand Prix which is given only to participants who garnered 97 points or higher. The MSJO scored 99 points.
In recent years, the Department of Science and Technology (DOST) has come up with some solutions to the problems that beset the mango industry.
For instance, the manual classification of mangoes has long been a bottleneck in the mango supply chain, characterized by time-consuming efforts and subjective judgment. The University of the Philippines Cebu (UP Cebu) has harnessed the power of artificial intelligence (AI) and brought automation to the labor-intensive task of sorting carabao mangoes for the fresh export market.
UP Cebu Professor Jonnifer Sinogaya headed a team that conducted the “Mango Automated Neural Net Generic Grade Assignor (MANGGA),” which the DOST’s Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development (PCAARRD) is funding.
The team’s systematic approach to data acquisition has led to an extensive data set of 10,440 images captured from various angles and orientations and corresponding ethylene concentrations collected from 870 individual mangoes, which served as the cornerstone for training a cutting-edge AI model for sorting Carabao mangoes.
The MANGGA project team has coded the Convolutional Neural Network (CNN) from scratch and also created an image data acquisition system. Their preliminary training of a single-input CNN model exhibited an impressive 94% accuracy in determining whether mangoes are suitable for export based on their overall visual characteristics.
Using the Philippine National Standard for quality metrics, the refinement of the CNN and Computer Vision System (CVS) promises a more efficient way to grade export-quality Carabao mangoes.
“The MANGGA project encourages the adoption of a smart postharvest system within the local mango industry,” wrote Thea Mariel Valdeavilla of the Science and Technology Information Institute. “With the premise of creating a conveyor system designed to sort mangoes based on their marketability, this initiative stands poised to revolutionize mango grading, offering efficiency and safety to the fresh export market.”
(To be continued next week)
