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Philippines is one of the countries in the world known for exporting good quality crops. Mangoes in the Philippines are very popular for its good sweet taste and considerably one of the best. Hence, ensuring the quality of the crop to be exported is essential. The study focused on utilizing convolutional neural network in determining the quality of carabao mango (Mangifera Indica). To make sure that all sides of the mango is going to be considered for the quality assessment, a mechanical system that uses conveyor belt, rollers, and camera was used to gather videos for training and validation of the model. The videos were extracted into frames and gone through image processing to remove the background and retain the mango only. The dataset is composed of different mangoes having both good and bad qualities. The implemented model used a total of 5550 training samples with 94.99% accuracy and a total of 2320 samples used for validation with an accuracy of 97.21%.

The variability in grain production makes it necessary to have strategic grain storage plans in order to ensure adequate supplies at all times. However, insects in stored grain products cause infestation and contamination which reduce grain quality and quantity. In order to prevent these problems, early detection and constant monitoring need to be implemented. Acoustic methods have been established in numerous studies as a viable approach for insect detection and monitoring with various sound parameterization and classification techniques. The aim of this study is to further demonstrate the efficacy of acoustic methods in pest management mainly through feature extraction using Mel-frequency cepstral coefficients (MFCCs) and classification using artificial neural network. The study used sounds from the Sitophilus oryzae (L.) or commonly known as rice weevil in larval stage recorded using five different acoustic sensors with the purpose of proving the capability of artificial neural network to recognize insect sounds regardless of the acoustic sensors used. Network models with varying number of nodes for the hidden layer were experimented in search for the highest accuracy that may be obtained. Results show that the network with 25 nodes for the hidden layer provides the best over-all network performance with 94.70% accuracy and the training, validation, and testing are accurate at 95.10%, 94.00%, and 93.60% respectively. Although, difference in accuracy values across all simulations never exceeded 1%. These show that the proposed method is capable of recognizing insect sounds regardless of the acoustic sensors used provided that proper acoustic signal preprocessing, feature extraction, and implementation of the network are performed.

In Philippines, there is a big problem in parking because of the highly increasing number of vehicles which has a big impact in the commuters, this lead the creation of Real-Time Viewing Automated Parking System that help the parking congestion especially in mall parking and other establishment. The system uses proximity sensors for the prototype to detect the vehicles. The driver can use mobile phone to access the web application and reserve the slot for parking while the system administrator assist the transaction. This parking system can lessen the struggling on finding parking availability and tool for transportation trends.