????? AI for Urban Agriculture: Growing on Walls and Roofs ?????
Wall and rooftop farming transforms urban buildings into productive green spaces

????? AI for Urban Agriculture: Growing on Walls and Roofs ?????

Urban farming is the practice of growing food within cities. Growing crops on walls and roofs are innovative techniques that use vertical spaces and building tops to cultivate plants, vegetables, fruits, and herbs. These methods maximize limited urban space, produce food locally, and offer environmental benefits like improved air quality and reduced urban heat.

Urban farming, particularly when implemented on facades and rooftop gardening, can be considered a subset of outdoor vertical farming as it utilizes vertical spaces in urban environments to grow crops efficiently.


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Core factors for urban agriculture planning

  1. Day Light Integral (DLI) is a measure of the total amount of photosynthetically active radiation (PAR) received by a given area over the course of a day, expressed in moles of light per square meter per day (mol/m2/day).

Daily light integral is the amount of photosynthetic light received in 1 sq.m. of area each day. Source: Michigan State University / Floriculture & Greenhouse Crop Production /


Seasonally averaged DLI values of different cities in the northern hemisphere. Source: Jung et al., 2024


An example of hourly light intensity values, the average of those values and the calculated daily light integral (DLI). Source: Michigan State University / Floriculture & Greenhouse Crop Production /

2. Building geometry profoundly affects urban farming by determining available space, sunlight exposure, and microclimates for crops. For example, a tall south-facing building might be ideal for vertical gardens of sun-loving tomatoes on its facade, while its shadier north side could support leafy greens. The rooftop's size and load-bearing capacity could influence the choice between intensive vegetable gardens or lighter weight hydroponic systems.

3. Productivity Estimates: Annual productivity of selected crops (sweet peppers, tomatoes, cucumbers, herbs, lettuce, and microgreens) was estimated based on local conditions and growing requirements.

For example, one of the studies assessed that rooftop gardens in Bologna have the potential to produce up to 12,505 tons of vegetables annually, which would satisfy 77% of the city's vegetable requirements, and create green corridors spanning 94 km to enhance urban biodiversity and ecosystem services.

Growing systems in Bologna used in the experiments. Source: Orsini et al., 2014

4. A cost analysis for urban farming should include:

  1. Initial setup costs (infrastructure, materials, equipment)
  2. Ongoing operational expenses (water, energy, labor, maintenance)
  3. Crop yield estimates and potential revenue
  4. Comparison with traditional farming methods
  5. Return on investment projections
  6. Cost per unit of produce
  7. Potential savings from building insulation and energy efficiency
  8. Long-term sustainability and scalability costs


The weightage of each cost on the total life cycle cost for (a) the conventional roof and (b) the rooftop garden. Source: R Dhivya Barathi and R Vidjeapriya, 2022

Boosting Lettuce Yields with Rooftop Agrivoltaics: The Power of Mini-PV Patterns

Country: Spain ????, Portugal ????

Published: 15 December 2021

This study investigates the feasibility and effectiveness of using mini-photovoltaic (PV) modules arranged in different patterns to optimize lettuce production on urban rooftops, balancing solar energy generation and crop growth.

The experiment tested three shading treatments — Concentrated Shade (CS), Scattered Shade (SS), and Full Sun (FS)—on rooftop lettuce crops during spring and summer 2021 in Almería, Spain. Nine cultivation containers were used, with SS treatment using four mini-PV modules arranged in patterns, and CS treatment using a single larger PV module. Key parameters measured included fresh weight, dry matter, number of leaves, and maximum leaf length. Measurements were made with digital scales and quantum sensors for photosynthetically active radiation (PAR).

Key findings showed that the SS treatment outperformed the CS and FS treatments in both seasons.

In spring, the SS treatment increased fresh weight by 46.4% over CS and 68.8% over FS. In summer, these increases were 61.2% and 87.6%, respectively. The SS treatment also produced more leaves, longer leaves, and greater root dry matter. These results suggest that patterned mini-PV modules can enhance lettuce productivity by providing intermittent shading and reducing photoinhibition.

Urban farmers, architects, and sustainability planners can apply these findings to design rooftop agrivoltaic systems that maximize both energy and food production.

Main tools/technologies:

  • Mini-photovoltaic (PV) modules
  • Quantum sensors for PAR measurement
  • Digital scales for weight measurement
  • Controlled cultivation containers

Experimental design. SS (Scattered shade), CS (Concentrated shade), FS (Full Sun), R (treatment replication), BPN (Bird proof net), PAR (quantum PAR sensor, within a discontinuous line indicated below the panel), TM (Thermometer). Source: Carre?o-Ortega et al., 2021
Aerial photograph of the experimental design. Source: Carre?o-Ortega et al., 2021
Maximum and minimum temperatures during May–June–July (°C). Source: Carre?o-Ortega et al., 2021
UVI (Ultra Violet Index) and daily irradiance during May–June–July (MJ m?2). Source: Carre?o-Ortega et al., 2021
Average daily values of photosynthetically active radiation (PAR) and errors, during May–June–July (μmol m?2 s?1). Different letters indicate significant differences, with ANOVA significance level α = 0.05. (CS) Concentrated shadow treatment. (SS) Scattered shadow treatment. (FS) Full sun treatment. Source: Carre?o-Ortega et al., 2021
Monthly daily average evolution of photosynthetically active radiation (PAR) during May-June-July (μmol m?2 s?1). (


Average lettuce fresh weight (g) and errors, during spring and summer season. Different letters indicate significance differences, with ANOVA significance level α = 0.05. (CS) Concentrated shadow treatment. (SS) Scattered shadow treatment. (FS) Full sun treatment. Source: Carre?o-Ortega et al., 2021
Average lettuce number of leaves (
Comparison between levels of radiation input (PPFD) PAR and resulting lettuce fresh weight for each treatment, during spring and summer seasons. (CS) Concentrated shadow treatment. (SS) Scattered shadow treatment. (FS) Full sun treatment. Source: Carre?o-Ortega et al., 2021
Comparison between levels of radiation input (PPFD) PAR and resulting height of the lettuces for each treatment, during spring and summer seasons. (CS) Concentrated shadow treatment. (SS) Scattered shadow treatment. (FS) Full sun treatment. Source: Carre?o-Ortega et al., 2021



AGRI|gen: Parametric Modular System for Vertical Urban Agriculture

Country: Palestine ????, Germany ????

Published: 16 March 2023

This research focuses on developing and implementing a parametric modular system for vertical urban agriculture to enhance food production in densely populated areas.

The study employed two parametric tools: AGRI|gen\Analysis and AGRI|gen\Design. AGRI|gen\Analysis was used to evaluate the farming potential of facades and roofs of 22 multi-floor residential buildings in Nablus, Palestine, identifying suitable areas for daylight-based farming and potential crop yields. AGRI|gen\Design was used to create a modular adaptive facade system, incorporating elements such as LED, PV, sensor, and fan units. The study determined that about 28,500 m2 of farming area was available, with half of the facades and all roofs being suitable for farming.

Key findings include:

  1. Tomatoes can be farmed on 25% of the facades, fulfilling 350% of the local consumption.
  2. Cucumbers can be farmed on 33% of the facades, fulfilling 237% of the local consumption.
  3. Roof farming was more suitable for high DLI-requiring plants like sweet peppers, producing 315 times the local consumption.
  4. The modular system required 40,824 units, with 73.3% LED, 10.1% PV, 8.7% sensor, and 8% fan units, costing $55.2/m2 on average and totaling $1.7 million.

Urban planners, architects, and agricultural engineers can practically apply these findings to design efficient vertical urban farming systems.

Main tools/technologies

  • Parametric analysis tools (AGRI|gen\Analysis and AGRI|gen\Design)
  • Modular adaptive facade systems
  • LED lighting
  • Photovoltaic units
  • Sensors
  • Fans

Different views of the building’s neighbourhood. Source: Ghazal et al., 2023.
General algorithm of the AGRI|gen\Analysis tool. Source: Ghazal et al., 2023.
The three possible scenarios for farming in the neighbourhood. Source: Ghazal et al., 2023.
Buildings modelling output; 3d model for the entire neighbourhood. Source: Ghazal et al., 2023.


Solar radiation calculation results on the 3D model. Source: Ghazal et al., 2023.
DLI calculation algorithm. Source: Ghazal et al., 2023.
Comparison between DLI values for the surface facade. Source: Ghazal et al., 2023.
Crops distribution on the facades. Source: Ghazal et al., 2023.
Modular hexagonal unit joinery system was designed, units were fabricated, controlled by Arduino, and a modular adaptive greenhouse was implemented on a building facade and a roof. Source: Ghazal et al., 2023.
?Unites types and dimensions. Source: Ghazal et al., 2023.
AGRI|gen\Design tool general algorithm. Source: Ghazal et al., 2023.
Shell modelling algorithm and results. Source: Ghazal et al., 2023.
Facade tessellation Shell algorithm and results. Source: Ghazal et al., 2023.
Units orientation results in the entire neighbourhood. Source: Ghazal et al., 2023.
Roof Design algorithm and results. Source: Ghazal et al., 2023.
Different distribution patterns on facades with different DLI values. Source: Ghazal et al., 2023.
Exterior perspective for single building from the neighbourhood
Medium DLI facade pattern. Source: Ghazal et al., 2023.
Roof Exterior perspective for a single building from the neighbourhood. Source: Ghazal et al., 2023.
An interior perspective showing how the system might look from a normal room. Source: Ghazal et al., 2023.
General image showing the system installation over the entire neighbourhood. Source: Ghazal et al., 2023.

Enhancing Vertical Garden Maintenance with Quadrotor Aerial Inspection

Country: Germany ????

Published: 10 June 2015

This study explores the use of unmanned aerial vehicles (UAVs) for the inspection and maintenance of vertical gardens, focusing on automated insecticide spraying and trajectory tracking using advanced algorithms.

The methodology involved employing a quadrotor equipped with a high-definition camera to visually inspect vertical gardens and perform targeted insecticide application. Key techniques included:

  1. Implementation of the HSV algorithm for green area detection.
  2. Development of fuzzy logic position controllers for precise navigation.
  3. Utilization of visual odometry for real-time position adjustments.
  4. Integration of GPS data for geo-referencing and trajectory planning.

The outcomes demonstrated the system's effectiveness in identifying green patches and applying insecticide with high precision, achieving significant improvements in maintenance efficiency.

Real-time flight tests demonstrated that the quadrotor's position adjustments in response to visual odometry inputs resulted in smooth, overshoot-free trajectories, which enhanced energy efficiency and precision in maintenance tasks.

The results showed that the quadrotor maintained stability with minimal deviation in position and orientation control, ensuring accurate spraying within a tolerance interval of ±5%.

The fuzzy logic controllers maintained quadrotor stability with deviations in sensor readings as follows: roll reading at 0.045°/s, yaw reading at 0.125°/s, and pitch reading at 0.15°/s, indicating reliable control of the UAV's movements.

Urban planners, horticulturists, and environmental engineers can practically apply these findings to enhance green infrastructure maintenance.

Main tools/technologies

  • Quadrotor UAVs
  • HSV color detection algorithm
  • Fuzzy logic controllers
  • Visual odometry
  • GPS geo-referencing

Vertical garden concept. Source: Yemelyanov et al., 2015
Resulting Graph de(t)= f(e(t)). Source: Yemelyanov et al., 2015

????? What's Next in Urban Farming?

We will check the best practices for aquaponics and have already discovered some interesting case studies about the "plants + fish" soilless formula.

?? Which topics are you interested in?

?? Let us know in the comments below

?? Thank you for your time today

?? Stay in touch and... grow ??!

Wishes of great harvests from roofs and walls,

Maryna Kuzmenko , Chief Urban Farming Officer at Petiole Pro

Photo credit for the cover of this edition:

  1. Orsini, F., Gasperi, D., Marchetti, L. et al. Exploring the production capacity of rooftop gardens (RTGs) in urban agriculture: the potential impact on food and nutrition security, biodiversity and other ecosystem services in the city of Bologna. Food Sec. 6, 781–792 (2014). https://doi.org/10.1007/s12571-014-0389-6
  2. Carre?o-Ortega, A.; do Pa?o, T.A.; Díaz-Pérez, M.; Gómez-Galán, M. Lettuce Production under Mini-PV Modules Arranged in Patterned Designs. Agronomy 2021, 11, 2554. https://doi.org/10.3390/agronomy11122554


References

Petiole Pro is an AI-powered platform for Plant Phenotyping and Quality Assurance

What is "Vertical Farming: A Guide for Growing Minds"? It's like my newsletter but 100x better :). Pre-order the book NOW if you believe in vertical farming as much as I do :)


Abdul Manan

Engineer || AgTech || Precision Crop Protection Researcher || UAV's

8 个月

Looks great!

Max Pavan

Business Development / Agtech / Sustainable Agriculture Irrigation /Vertical Farming / Plant Breeding / Sales / Networker

8 个月

Always insightful. Thank you.

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The link to Amazon pre-order of book "Vertical Farming: A Guide for Growing Minds": https://www.amazon.co.uk/dp/B0D9TPZ1MB

Fascinating insights on urban agriculture—looking forward to learning more about the integration of aquaponics in cityscapes!

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