Design and Architecture of BenguetFresh: An IoT-Enabled Data-Driven System for Sustainable Agriculture
DOI: https://doi.org/10.52783/jes.2095
Abstract
Agriculture plays a vital role in the Philippines, contributing significantly to its economy and providing livelihoods to a substantial portion of the population. Benguet province, known as the "Salad Bowl of the Philippines" for its favorable climate and fertile soil, exemplifies thriving agriculture-based livelihoods. However, challenges persist in the sector, with the low compliance rate of Philippine Good Agricultural Practices (PhilGAP) certification highlighting the need for innovative solutions to enhance sustainable practices, market access, and overall agricultural quality for Benguet farmers.
This study presents the design and architecture of BenguetFresh a system designed to integrate data-driven monitoring and management practices with sustainable agriculture, while adhering to PhilGAP standards. Through stakeholder engagement, interviews, and systematic review, specific requirements and challenges of farmers in Benguet were identified and addressed.
BenguetFresh employs cutting-edge technology, Arduino-based smart agriculture monitoring systems, enabling real-time data collection on environmental factors. Data analytics tools process this data to provide valuable insights, facilitating informed decision-making and resource optimization.
The scalable architecture ensures efficient data flow and real-time processing, while user-friendly interfaces empower farmers with real-time and historical data visualization and data-driven recommendations. Furthermore, the integration of e-commerce functionality fosters direct connections between farmers and consumers, expanding market reach and improving economic outcomes.
BenguetFresh's design and architecture serve as a blueprint for implementing data-driven, sustainable agriculture. The potential benefits extend to both farmers and consumers, ensuring high-quality, responsibly produced agricultural products. Embracing cutting-edge technology and industry standards, BenguetFresh paves the way for efficient, sustainable, and prosperous agriculture.
CLIMATE-RESILIENT AGRICULTURE: ML INTEGRATION FOR WEATHER AND PREDICTIVE ANALYTICS IN BENGUETFRESH
DOI: (accepted but waiting for publication at Scopus Indexed Journal)
Abstract
The escalating impacts of climate change offer significant difficulties to farmers worldwide, affecting agricultural practices and food security. In Benguet province, these effects are particularly pronounced, with farmers dealing with heightened unpredictability in weather patterns, increased frequency of extreme temperatures, and natural disasters. Such climatic shifts disrupt traditional farming calendars, jeopardize crop yields, and the prevalence of pests and diseases. This research focuses on Climate-Resilient Agriculture, specifically the integration of machine learning (ML) for weather data and predictive analytics within the BenguetFresh farm monitoring system. In response to climate change challenges, the study empowers farmers with real-time weather insights and predictive analytics. The methodology encompasses diverse sources for collecting and integrating real-time weather data, rigorous processing to ensure accuracy, and the utilization of varied ML algorithms. Aligned with PhilGAP standards, BenguetFresh offers data-driven recommendations emphasizing eco-friendly farming methods, and integrated pest management. The study investigates the platform's capacity to enhance early detection and prevention of pests and diseases through data analytics. BenguetFresh's user-centric approach, facilitated by intuitive dashboards, enables informed decision-making aligned with sustainable farming practices.
This research signifies a significant step toward intelligent agricultural systems that enhance resilience and responsiveness to the evolving challenges provided by climate variability.
Text Classification for Suicidal Ideation Detection on Online Social Networks
URL: https://www.sajst.org/online/index.php/sajst/article/view/225
Abstract
Online social networks have become a common medium of communications. Studies have shown that it is more likely for a user to share their opinions and ideation. In spite of the fact that this could be beneficial, there are some developing concerns with respect to its negative effect on the users, such as, the spread of self-destructive ideation. According to the World Health Organization (WHO), more than 800,000 people die by suicide each year, a number that translates to one death every 40 seconds. Thus, this study aims to detect suicidal ideation from tweets based from pronouns and absolutist words weights using TF-IDF (Term Frequency – Inverse Document Frequency). Furthermore it will evaluate the performance of two machine classifiers in identifying suicide-related text from Twitter (tweets) using Rapid Miner.
Stimulating the Economic Growth and Social Welfare of Benguet using Geotagging: an Intelligent Application
URL:
Abstract
Poverty is a major cause of social tension which can divide a nation. The government’s assistance programs
often fail to reach those in need, necessitating a focus on directing aid to areas where poverty is prevalent.
This study aims to introduce an innovative approach for defning target communities based on geographical
location to ensure equal and fair distribution of basic resources, services, and programs provided by
government agencies. Additionally, the research will pinpoint the specifc needs of the community based on
their geographic location, while also examining the disparity in poverty rates between genders. Lastly, the
study aims to identify and classify the prevalent reasons behind obtaining Barangay Indigent Certifcates
and Business Closure Certifcates.
The study incorporates smart technologies that involve employing Natural Language Processing to process
and analyze data collected from the Community Information System and other sources. The results are then
presented through a Data Analytics Tool for review.
The fndings suggest that geotagging result can aid the government in equitably allocating fundamental
resources, services, and programs. In addition, it can facilitate the identifcation of new programs for women
that will reduce unemployment rates. Additionally, implementing economic programs will be made easier
with the use of Machine Learning to categorize common causes of business closures. Overall, the fndings
of this study will foster economic development in Benguet province.