Olongapo City Agricultural

and Biosystems Engineering

City Government of Olongapo
Rizal Ave. Olongapo City 2200

CONCEPT PROPOSAL
1.0 Program: City SMMAART
2.0 Project: Building Automated Bio-yield lushes on Layered Outdoor Network
3.0 Proponents: Cesar U. Ramirez, Jr./City ABE

Partners/Collaborators: Francis E. Maniago/Head, CAO

Budget: 250,000

4.0 Rationale

Urbanization has drastically reduced available agricultural land, leading to

increased dependence on external food sources and heightened environmental concerns. As
cities expand, innovative solutions are required to ensure food security, enhance sustainability,
and maximize urban spaces efficiently. The B.A.B.Y.L.O.N. Project proposes an innovative
approach to urban agriculture by transforming city walls into automated, layered bio-yielding
farms, integrating technology with vertical farming to promote green infrastructure and food
resilience.

This project leverages automated hydroponic and aeroponic systems, enabling the cultivation
of fresh produce on urban walls while optimizing resource use through IoT-based monitoring
and smart irrigation networks. By repurposing building exteriors and underutilized vertical
spaces, B.A.B.Y.L.O.N. contributes to climate adaptation, carbon footprint reduction, and air
purification. The incorporation of solar-powered automation ensures energy efficiency, making
it a sustainable and scalable solution for urban environments.

Beyond environmental impact, the project fosters economic and social benefits. By engaging
local communities, businesses, and government units, B.A.B.Y.L.O.N. provides opportunities
for urban farming education, employment generation, and eco-tourism development. The
initiative aligns with global sustainable development goals (SDGs), particularly zero hunger,
sustainable cities, and climate action, making it a transformative model for urban resilience and
food security.

With Olongapo City as a pilot location, the success of the B.A.B.Y.L.O.N. Project can serve as
a blueprint for other urban centers, demonstrating the feasibility of integrating agriculture with
modern urban landscapes to create self-sustaining, eco-friendly cities of the future.

Review of Literature

This review examines existing literature on vertical farming, smart agriculture, green
infrastructure, and the economic and social impacts of urban farming. It provides a foundation
for understanding how technology-driven urban agriculture can enhance food security, optimize
space, and promote ecological balance. Furthermore, the discussion highlights successful
implementations of green walls and automated vertical farms, reinforcing the practicality and
benefits of integrating agriculture into the built environment.
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 Olongapo City Agricultural
and Biosystems Engineering
City Government of Olongapo
Rizal Ave. Olongapo City 2200

1. Vertical Farming and Urban Agriculture

Urban agriculture has gained global recognition as a sustainable solution for food security in cities.
According to Despommier (2010), vertical farming optimizes urban spaces by growing crops in
stacked layers, reducing land use and water consumption. Studies by Kalantari et al. (2017)
emphasize that vertical farming not only increases food production but also contributes to
environmental sustainability by improving air quality and mitigating the urban heat island effect.

In countries like Singapore and Japan, hydroponic and aeroponic vertical farms have been
integrated into buildings and public infrastructure (Graamans et al., 2018). These systems have
demonstrated high efficiency in food production with minimal environmental impact, proving their
feasibility in dense urban areas.

2. Smart Agriculture and Automation in Vertical Farming

The integration of Internet of Things (IoT) and automation in vertical farming has revolutionized
urban food production. Research by Shamshiri et al. (2018) highlights how sensor-based agriculture
improves crop monitoring, automates irrigation, and optimizes nutrient supply. Smart irrigation
systems have been found to reduce water usage by up to 50%, making them ideal for sustainable
urban farming (Zhang et al., 2017).

A study by Benke and Tomkins (2017) emphasizes the role of artificial intelligence (AI) in precision
agriculture, which helps predict plant growth patterns and detect diseases early. These technologies
enable data-driven decision-making, ensuring maximum yield with minimal resource input.

3. Green Infrastructure and Environmental Benefits of Vertical

Farming

The concept of green walls has been widely studied as an urban greening strategy. According to
Pérez et al. (2014), green walls enhance urban aesthetics while providing thermal insulation,
reducing noise pollution, and improving air quality. They act as natural carbon sinks, helping
mitigate the effects of climate change in cities (Wong et al., 2010).

Incorporating agriculture into building façades has also been explored in projects like One Central
Park in Sydney, Australia, where vertical gardens contribute to energy efficiency and biodiversity
conservation (Köhler, 2008). These precedents highlight the viability of integrating urban agriculture
with modern architectural designs.

4. Economic and Social Impact of Urban Farming

Beyond sustainability, vertical farming presents economic and social benefits. A study by Specht et
al. (2014) indicates that urban agriculture can create job opportunities, reduce food transportation
costs, and provide fresh produce to local communities. Additionally, community-based vertical
farms encourage citizen participation and promote urban resilience.

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Mobile: 09532753272; Gmail: cramirezjr1294@gmail.com;
Fb: Olongapo City Agricultural and Biosystems Engineering
 Olongapo City Agricultural
and Biosystems Engineering
City Government of Olongapo
Rizal Ave. Olongapo City 2200

5.0 Methodology/Planned Activities to Achieve Outcome

This chapter presents the methodology for conducting an orthomosaic aerial survey of
coconut farms in Olongapo City. The study aims to establish an accurate inventory of coconut
trees and develop a monitoring system to enhance farm management and productivity. Given
the challenges posed by the hilly and mountainous terrain, this study employs drone-based
remote sensing technology to efficiently collect, process, and analyze farm data.

The methodology is structured into several key phases: (1) Data Acquisition, (2) Data
Processing, and (3) Data Analysis and Interpretation. The study will use Unmanned Aerial
Vehicles (UAVs) equipped with high-resolution RGB and multispectral cameras to capture
georeferenced images of coconut farms. These images will be processed into high-resolution
orthomosaics and digital elevation models (DEM) to analyze tree distribution, health status, and
terrain characteristics.

5.1 Data Acquisition

Drone Deployment: Use UAVs equipped with high-resolution RGB and multispectral
cameras.

Flight Planning: Utilize GIS software (e.g., Pix4D, DroneDeploy) to optimize flight paths,
ensuring adequate coverage and overlap.

Survey Parameters:

Flight altitude: 100m–150m (adjusted for elevation changes).

Image overlap: 80% front overlap, 70% side overlap for optimal stitching.
Ground sampling distance (GSD): 2–5 cm per pixel.

Schedule: Initial Survey is on the whole month of April 2025

5.2 Data Processing

Orthomosaic Generation: Use photogrammetry software to create a georeferenced map.

Tree Counting & Classification: image detection to count trees and assess health based on
canopy color and density.

5.3 Analysis and Interpretation

Identify tree density, distribution, and potential gaps for replanting.

Detect tree health conditions using multispectral analysis (NDVI for chlorophyll levels).

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and Biosystems Engineering
City Government of Olongapo
Rizal Ave. Olongapo City 2200

Assess terrain-related challenges (e.g., soil erosion risks, drainage issues).

5.4 Data Integration and Reporting

Convert processed data into GIS-compatible layers for integration with farm management
systems.
Generate detailed reports with visual maps, statistics, and recommendations.

6.0 Expected Output

A comprehensive inventory of coconut trees with geotagged data.
Enhanced farm management strategies based on real-time monitoring.
Improved sustainability and productivity of coconut farms.
Data-driven decision-making for farm strengthening and expansion.

7.0 Literature Cited

Benke, K., & Tomkins, B. (2017). Future food-production systems: Vertical farming and controlled-
environment agriculture. Sustainability Science, 12(5), 1-13. https://doi.org/10.1007/s11625-017-0457-7

Despommier, D. (2010). The vertical farm: Feeding the world in the 21st century. St. Martin’s Press.

Graamans, L., Baeza, E., van den Dobbelsteen, A., Tsafaras, I., & Stanghellini, C. (2018). Plant
factories versus greenhouses: Comparison of resource use efficiency. Agricultural Systems, 160, 31-
43. https://doi.org/10.1016/j.agsy.2017.11.003

Kalantari, F., Tahir, O. M., Joni, R. A., & Fatemi, E. (2017). Opportunities and challenges in
sustainability of vertical farming: A review. Journal of Landscape and Ecological Engineering, 13(2),
173-180. https://doi.org/10.1007/s11355-017-0349-1

Köhler, M. (2008). Green façades—a view back and some visions. Urban Ecosystems, 11(4), 423-436.
https://doi.org/10.1007/s11252-008-0063-x

Pérez, G., Rincón, L., Vila, A., González, J. M., & Cabeza, L. F. (2014). Green vertical systems for
buildings as passive systems for energy savings. Applied Energy, 88(12), 4854-4859.
https://doi.org/10.1016/j.apenergy.2011.06.032

Shamshiri, R. R., Kalantari, F., Ting, K. C., Thorp, K. R., Hameed, I. A., Weltzien, C., Ahmad, D., &
Shad, Z. M. (2018). Advances in greenhouse automation and controlled environment agriculture: A
transition to plant factories and urban agriculture. International Journal of Agricultural and Biological
Engineering, 11(1), 1-22. https://doi.org/10.25165/j.ijabe.20181101.3210

Specht, K., Siebert, R., Hartmann, I., Freisinger, U. B., Sawicka, M., Werner, A., Thomaier, S., Henckel,
D., Walk, H., & Dierich, A. (2014). Urban agriculture of the future: An overview of sustainability aspects
of food production in and on buildings. Agriculture and Human Values, 31(1), 33-51.
https://doi.org/10.1007/s10460-013-9448-4

Innovating Biosystems, Empowering Communities

Mobile: 09532753272; Gmail: cramirezjr1294@gmail.com;
Fb: Olongapo City Agricultural and Biosystems Engineering
 Olongapo City Agricultural
and Biosystems Engineering
City Government of Olongapo
Rizal Ave. Olongapo City 2200

Wong, N. H., Tan, A. Y. K., Chen, Y., Sekar, K., Tan, P. Y., Chan, D., Chiang, K., & Wong, N. C.
(2010). Thermal evaluation of vertical greenery systems for building walls. Building and Environment,
45(3), 663-672. https://doi.org/10.1016/j.buildenv.2009.08.005

Zhang, Y., He, S., Liu, J., & Kong, F. (2017). Smart irrigation for sustainable water use: A review. Water
Science and Technology: Water Supply, 17(4), 1059-1073. https://doi.org/10.2166/ws.2017.002

8.0 Strategies to Ensure Utilization of Expected Output

8.1 Continuous Monitoring and Periodic Updating to PCA and LGU

Regular Drone Surveys: Conduct periodic aerial surveys to update the inventory, monitor
farm progress, and assess the effectiveness of interventions.

Comparative Analysis: Evaluate trends over time by comparing newly captured data with
historical survey results to measure improvements and detect emerging issues.

GIS-Based Coconut Farm Database: Develop a centralized database where farmers and
agricultural experts can access and analyze farm data in real time.

8.2 Collaboration with other offices in the LGU and Olongapo’s Higher
Education Institutions

Partnership with Universities and Research Centers: Work with academic institutions to
refine data analysis methods and explore further applications of remote sensing in
coconut farming.

Adoption of Advanced Technologies: Integrate machine learning and AI-based analytics

for automated tree health assessments and disease detection.

9.0 Alignment with SGLG Targets

9.1. Disaster Preparedness and Environmental Management

Climate Resilience: The study supports disaster risk reduction by monitoring climate-related
threats, such as droughts and typhoons, that affect coconut farms.

Sustainable Land Use Planning: By providing digital elevation models (DEM) and other
assessments, LGUs can make better land-use decisions, ensuring soil conservation,
reforestation, and erosion control measures in hilly and mountainous regions.
9.2. Economic Development and Business-Friendliness

Support for Agricultural Productivity: The coconut industry is a major contributor to the
local economy, and improving farm monitoring will increase yields, create jobs, and enhance
farmer incomes, contributing to LGU economic development goals.

Attracting Investments: With data-driven agricultural monitoring, LGUs can attract investors
in agribusiness, coconut processing, and farm technology solutions, fostering business
growth.

Innovating Biosystems, Empowering Communities

Mobile: 09532753272; Gmail: cramirezjr1294@gmail.com;
Fb: Olongapo City Agricultural and Biosystems Engineering
 Olongapo City Agricultural
and Biosystems Engineering
City Government of Olongapo
Rizal Ave. Olongapo City 2200

9.3. Social Protection

Farmer Empowerment: By equipping farmers with modern technology, the LGU ensures
that they have access to reliable data for decision-making, improving livelihood
sustainability.
9.4. Financial Administration and Transparency

Data-Driven Budget Allocation: The survey results will help the LGU allocate resources
more efficiently, ensuring that agricultural funding is directed toward areas needing
intervention, which supports financial transparency and accountability.

Evidence-Based Policy Formulation: The LGU can use drone survey data to justify funding
requests for agriculture, disaster preparedness, and environmental programs, strengthening
compliance with SGLG financial governance criteria.

9.5. Tourism and Cultural Heritage

Eco-Tourism Potential: Coconut farms, when well-maintained, can be promoted as part of

agro-tourism initiatives, aligning with the LGU’s tourism and cultural development goals.

10.0 Estimated Budget

Training and lisence
to operate drone: 50,000 PhP

Capital Outlay: 200,000.00 PhP

Arial Mapping Drone with the following recommended settings;

Camera System
 Main Camera: 20MP Hasselblad 4/3 CMOS sensor (high-resolution imaging)
 Telephoto Camera: 12MP hybrid zoom (detailed inspections)
 Video Resolution: Up to 5.1K at 50fps
 Adjustable Aperture: f/2.8–f/11 (for different lighting conditions)

Flight Performance
 Max Flight Time: 46 minutes per battery
 Max Transmission Range: 15 km (FCC)
 Max Speed: 21 m/s (75.6 km/h)
 Obstacle Avoidance: Omnidirectional sensing for safe navigation

Mapping and Surveying Features

 Satellite Positioning: GPS + Galileo + BeiDou
 RTK (Real-Time Kinematics) Compatibility (for precise mapping)
 Automated Flight Planning & Waypoints
 Compatible with GIS & photogrammetry software

Prepared by:

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Mobile: 09532753272; Gmail: cramirezjr1294@gmail.com;
Fb: Olongapo City Agricultural and Biosystems Engineering
 Olongapo City Agricultural
and Biosystems Engineering
City Government of Olongapo
Rizal Ave. Olongapo City 2200

Engr. Cesar U. Ramirez, Jr/ City ABE Office

Innovating Biosystems, Empowering Communities

Mobile: 09532753272; Gmail: cramirezjr1294@gmail.com;
Fb: Olongapo City Agricultural and Biosystems Engineering