Memoria Investigaciones en Ingeniería, núm. 29 (2025). pp. 95-108
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ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay
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Smart and Sustainable IoT-Driven Vertical Farming Solution for Agricultural
Challenges in Pakistan
Solución de agricultura vertical inteligente y sostenible basada en IoT para los
desafíos agrícolas en Pakistán
Solução de agricultura vertical inteligente e sustentável baseada em IoT para
desafios agrícolas no Paquistão
Sadiq Ur Rehman
1
(*), Muhammad Adeel Mannan
2
, Muhammad Ahsan Shaikh
3
, Muhammad Uzair
4
Recibido: 15/05/2025 Aceptado: 23/07/2025
Summary. - Agriculture in Pakistan faces critical challenges such as water scarcity, inefficient resource use, and
climate change impacts, particularly in urban and peri-urban areas. This study presents a smart, solar-powered vertical
farming system designed to address these issues by integrating capacitive soil moisture sensors, temperature and
humidity sensors (DHT22), and light sensors (BH1750), controlled via Raspberry Pi 4. The off-grid system, powered
by a 100-watt solar panel and battery, features intelligent irrigation driven by a Random Forest algorithm to optimize
water use. Over a six-week trial cultivating cherry tomatoes, the system achieved a 60–65% yield increase, 40% energy
savings, and a 28.57% reduction in water consumption compared to traditional methods. While promising, limitations
include the small trial size and lack of long-term environmental impact data. Scalability challenges such as cost,
maintenance, and local constraints must be addressed for wider adoption. Future work will focus on expanding crop
varieties, enhancing AI integration, and improving accessibility for small-scale farmers to support sustainable urban
agriculture and food security in Pakistan.
Keywords: Agriculture, Vertical Farming, Sustainable Agriculture, Water Efficiency, Solar Energy.
1
Ph.D., Associate Professor, Faculty of Engineering Science and Technology, IQRA University (Pakistan),
sadiq.rehman@iqra.edu.pk, ORCID iD: https://orcid.org/0000-0002-6308-450X
2
Ph.D., Associate Professor, Bahria School of Engineering and Applied Sciences, Bahria University (Pakistan),
madeelmannan.bukc@bahria.edu.pk, ORCID iD: https://orcid.org/0000-0002-0811-4753
3
Ph.D., Assistant Professor, Faculty of Engineering Science and Technology, Hamdard University (Pakistan)
muhammad.ahsan@hamdard.edu.pk, ORCID iD: https://orcid.org/0000-0003-2408-5689
4
Ph.D., Associate professor, Electrical Engineering Department, Faculty of Engineering, Islamic University of Madinah (Saudi Arabia)
muzair@iu.edu.sa, ORCID iD: https://orcid.org/0000-0003-1063-9476
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Memoria Investigaciones en Ingeniería, núm. 29 (2025). pp. 95-108
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Resumen. - La agricultura en Pakistán enfrenta desafíos críticos como la escasez de agua, el uso ineficiente de los
recursos y los impactos del cambio climático, particularmente en áreas urbanas y periurbanas. Este estudio presenta
un sistema de cultivo vertical inteligente alimentado con energía solar, diseñado para abordar estos problemas
mediante la integración de sensores capacitivos de humedad del suelo, sensores de temperatura y humedad (DHT22)
y sensores de luz (BH1750), controlados mediante Raspberry Pi 4. El sistema autónomo, alimentado por un panel
solar de 100 vatios y una batería, cuenta con riego inteligente impulsado por un algoritmo de Bosque Aleatorio para
optimizar el uso del agua. Durante un ensayo de seis semanas cultivando tomates cherry, el sistema logró un aumento
del 60-65% en el rendimiento, un ahorro de energía del 40% y una reducción del 28,57% en el consumo de agua en
comparación con los métodos tradicionales. Si bien es prometedor, las limitaciones incluyen el pequeño tamaño del
ensayo y la falta de datos de impacto ambiental a largo plazo. Es necesario abordar los desafíos de escalabilidad,
como el costo, el mantenimiento y las restricciones locales, para una adopción más amplia. El trabajo futuro se
centrará en ampliar las variedades de cultivos, mejorar la integración de la IA y mejorar la accesibilidad para los
pequeños agricultores para apoyar la agricultura urbana sostenible y la seguridad alimentaria en Pakistán.
Palabras clave: Agricultura impulsada por el IoT, agricultura vertical, agricultura sostenible, eficiencia hídrica,
energía solar
Resumo. - A agricultura no Paquistão enfrenta desafios críticos, como a escassez de água, a utilização ineficiente dos
recursos e os impactos das alterações climáticas, particularmente nas áreas urbanas e periurbanas. Este estudo
apresenta um sistema de agricultura vertical inteligente, alimentado a energia solar, concebido para lidar com estas
questões, integrando sensores capacitivos de humidade do solo, sensores de temperatura e humidade (DHT22) e
sensores de luz (BH1750), controlados através do Raspberry Pi 4. O sistema off-grid, alimentado por um painel solar
de 100 watts e bateria, possui uma irrigação inteligente acionada por um algoritmo Random Forest para otimizar a
utilização da água. Ao longo de um teste de seis semanas de cultivo de tomate-cereja, o sistema obteve um aumento de
60–65% na produtividade, 40% de poupança de energia e uma redução de 28,57% no consumo de água em
comparação com os métodos tradicionais. Embora promissor, as limitações incluem o pequeno tamanho do teste e a
falta de dados de impacto ambiental a longo prazo. Os desafios de escalabilidade, como o custo, a manutenção e as
restrições locais, devem ser abordados para uma adoção mais ampla. O trabalho futuro irá focar-se na expansão das
variedades de culturas, na melhoria da integração da IA e na melhoria da acessibilidade para os pequenos agricultores
para apoiar a agricultura urbana sustentável e a segurança alimentar no Paquistão.
Palavras-chave: Agricultura orientada por IoT, agricultura vertical, agricultura sustentável, eficiência hídrica,
energia solar.
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1. Introduction. - The global population has been growing at an increasingly rapid pace, which has led to several
challenges, such as the strain on land available for agriculture and the growing need for fresh water to meet both
drinking and agricultural demands [1]. Experts predict that by 2050, the world's population could reach 9 billion, further
intensifying the pressure on land resources needed for living spaces [2]. With only 10% of the land being suitable for
farming, it is becoming increasingly difficult to grow enough crops to meet the needs of a growing population.
Furthermore, an enormous number of crops are wasted due to natural disasters such as earthquakes, droughts, heavy
rainfall, and flooding [3]. Moreover, the crops that make it to the market are often not fresh, as the farming and
agricultural lands are located far from cities and residential areas. Pakistan is one of the developing countries whose
economy heavily depends on agriculture [4]. However, rapid urbanization has made it difficult for this sector to grow
enough food to meet demand. Traditional farming [5] in Pakistan is still widely practiced but faces many challenges,
including high water use and vulnerability to environmental degradation (see Table I).
Challenge
Description
Water Scarcity
Low rainfall and over-extraction of groundwater
Energy Shortage
Frequent power outages, especially in rural áreas
Land Constraints
Urban sprawl reduces arable land availability
Climate Variability
Increasing droughts, floods, and heatwaves
Inefficient Irrigation
High water loss due to outdated techniques
Crop Loss & Decay
Delays from rural farms to markets reduce freshness
Table I. Challenges in Traditional Agriculture in Pakistan
Vertical farming [6] offers a promising solution to these challenges by maximizing agricultural space through stacked
beds that grow crops vertically in controlled environments. This method optimizes the use of arable land and allows
for more efficient water and energy use. By carefully monitoring and controlling factors like light, temperature,
humidity, and soil moisture, vertical farming can create ideal growing conditions with fewer resources. Importantly,
vertical farming isn’t just about saving space, it also addresses critical sustainability issues around water and energy.
Using smart sensors to water plants only when needed helps cut down on wasted water, while integrating solar power
reduces dependence on unreliable grid electricity. This combination makes the system not only smarter but also much
more environmentally friendly and sustainable, something particularly vital for resource-limited countries like Pakistan.
In this study, we propose a practical and fully autonomous vertical farming system that integrates IoT [7-8] sensors,
machine learning algorithms [9], and solar power. This combination not only allows precise control of environmental
conditions but also reduces reliance on conventional electricity grids by harnessing renewable energy, making it a
realistic and sustainable solution for the challenges faced by agriculture in Pakistan and similar regions. Table II
presents a comparison between vertical farming and traditional farming methods, showing measurable improvements
in space efficiency, water savings, and environmental impact. While traditional farming requires large land areas and
often wastes water through inefficient irrigation, vertical farming can reduce water use by up to 90% and increase yield
per square foot. Our approach builds on these advantages by incorporating IoT and machine learning to optimize
irrigation and lighting further, backed by solar energy to ensure sustainable power supply.
Aspect
Vertical Farming
Traditional Farming
Space Efficiency
High crops grow in stacked layers,
maximizing space.
Low; requires large land areas for planting.
Water Usage
Reduced by up to 90% due to efficient
irrigation methods (e.g., hydroponics).
High, especially in water-scarce regions with
inefficient irrigation systems.
Energy Usage
High, but can be offset with renewable
energy (e.g., solar power).
Generally low but dependent on external
power sources.
Yield
Significantly higher per square foot
compared to traditional methods.
Relatively low per unit area, especially in
urban or degraded soils.
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Environmental Impact
Lower carbon footprint due to local
production and reduced transportation.
Higher due to transportation and potential
soil degradation.
Table II. Comparison Between Vertical Farming and Traditional Farming Systems
The rest of the paper is organized as follows: Section II provides a literature review, Section III details the methodology
of the proposed system, Section IV presents the results and discussion, and finally, Section V concludes with a summary
of key findings and possible future directions.
2. Literature Review. - The world’s economic stability is increasingly threatened by challenges such as infectious
diseases and rapid population growth, which directly impact agricultural land availability and food security [10]. In
countries like Pakistan, where agriculture is a major economic driver, these pressures are intensified by urbanization
and climate change, leading to decreased arable land and freshwater scarcity. This calls for innovative farming solutions
that maximize productivity within limited resources.
The proposed system seeks to address these challenges by combining vertical farming with modern technologies such
as IoT, machine learning (ML), and solar power. The focus is on creating an easy-to-use internal farming platform with
shelving units to grow crops efficiently indoors. Before detailing the system design, it is essential to critically review
relevant technologies and their current limitations.
2.1 IoT in Agriculture. - IoT technologies have transformed agricultural monitoring by enabling real-time data
collection on environmental factors like soil moisture, temperature, and humidity [11-12]. These systems improve water
efficiency through automated irrigation and enable precise crop management [13-14]. However, many existing IoT
applications focus primarily on data gathering rather than integrating predictive analytics or full automation, limiting
their optimization potential.
Furthermore, IoT integration in vertical farming remains nascent. Vertical farms require more sophisticated control
over multiple environmental variables simultaneously, which complicates IoT deployment and data processing. Studies
rarely address system scalability or robustness in challenging field conditions, especially in developing countries where
infrastructure may be unreliable.
2.2 Vertical Farming vs. Traditional Farming. - Vertical farming, characterized by stacking plants in multi-layered
structures, offers substantial benefits over traditional farming, including higher space utilization and significant water
savings (up to 90% reduction) due to methods like hydroponics and aeroponics [15-16]. While vertical farming
promises efficiency gains, it is important to highlight its main challenges. Energy consumption is notably high due to
artificial lighting and climate control requirements, which can limit its practicality in energy-constrained regions [15].
Additionally, the upfront costs and technical complexity can be prohibitive, especially for small-scale farmers in
developing countries. Hence, integrating renewable energy sources such as solar power becomes critical but is still
underdeveloped in current literature.
2.3 Solar Energy in Agriculture. - Solar energy presents a promising solution to reduce reliance on unreliable
electrical grids in agriculture, particularly in countries like Pakistan with high solar insolation [17]. Solar-powered
irrigation and greenhouse systems have demonstrated reduced operational costs and lower environmental impact [18].
Despite this, solar integration into vertical farming remains limited. Vertical farming’s high continuous energy demands
require efficient energy storage and management solutions that are often overlooked.
Additionally, the intermittent nature of solar power poses challenges for maintaining the consistent environmental
conditions vertical farms need. Most studies do not address how to mitigate this intermittency or evaluate the economic
feasibility of incorporating solar power at scale.
2.4 Machine Learning in Agriculture. - Machine learning has shown significant promise in enhancing agricultural
decision-making by analyzing sensor data to predict irrigation needs, crop health, and yield [19-20]. For example,
Random Forest algorithms have effectively optimized irrigation schedules, reducing water waste [21]. However, many
ML models are trained on limited datasets and have not been extensively validated across diverse crops or
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environments.
Moreover, there is a lack of studies integrating ML with IoT in real-time vertical farming environments to create closed-
loop systems for dynamic resource management. Bridging this gap is critical to developing intelligent farms that can
autonomously optimize water and energy use.
Study
Key Findings
Limitations
[13]
Improved water use and crop
yield via soil monitoring.
Focused on monitoring, limited
automation and predictive
control.
[14]
Demonstrated water-saving
potential with soil moisture
sensors.
Prototype scale, lack of
scalability analysis.
[15]
Efficient space use and water
savings demonstrated.
High setup costs and energy
needs.
[17],[18]
Reduced energy costs and grid
dependency shown.
Limited focus on integration with
high-demand vertical farming.
[21]
Improved irrigation scheduling
and yield forecasting.
Limited real-world validation
and integration with IoT.
Table III. Comparative Analysis of Technological Interventions in Smart and Sustainable Agriculture Systems
The literature indicates clear potential in combining vertical farming, IoT, solar power, and ML. However, a holistic
system that integrates these components efficiently, addresses energy constraints, and is tailored to developing country
contexts remains elusive. Our research addresses these gaps by proposing a smart vertical farming system that leverages
IoT and ML for automated irrigation, powered sustainably by solar energy.
3. Methodology. - The system was implemented on a practical small scale inside a 12x12-foot room, featuring a two-
tiered iron frame structure approximately 45 inches tall, enclosed by transparent acrylic sheets (see Figure. I). This
setup created a mini-greenhouse effect, optimizing vertical space by stacking growing beds, which is ideal for urban or
space-constrained environments. While cherry tomatoes were the primary crop cultivated during this trial, the system
is designed to support a diverse range of crops such as leafy greens, herbs, and peppers, which will be explored in future
extended studies to validate broader applicability.
Figure I. Structure Diagram.
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.
Figure II. System Block Diagram.
Each growing bed was equipped with sensors to continuously monitor environmental and soil conditions, enabling
precise control to optimize crop growth. Capacitive soil moisture sensors measured water content, triggering irrigation
only when moisture levels fell below 30% to conserve water efficiently. Temperature and humidity were monitored
using DHT22 sensors, maintaining the target ranges of 29°C to 34°C and 58% to 68% humidity, respectively. The
BH1750 light sensor ensured that plants received adequate light by activating energy-efficient LED grow lights
whenever ambient light dropped below 3000 lux. The system architecture is illustrated in Figure II.
The core processing was handled by a Raspberry Pi 4, which collected sensor data every 5 minutes to maintain real-
time control of irrigation, lighting, and environmental conditions. The system was powered by a 100-watt solar panel
connected to a battery bank via a charge controller, ensuring energy autonomy even during night hours or cloudy
conditions. A 12V DC water pump delivered irrigation for approximately 30 seconds per activation, based on sensor
readings.
Remote monitoring and manual control were enabled through the Red-Note software dashboard, which presented real-
time environmental data and system status to users, facilitating easy intervention if necessary.
To enhance irrigation precision and reduce water waste, a Random Forest regression model was implemented to predict
optimal irrigation durations. The model was trained using a dataset comprising 3,200 records collected from preliminary
trials. Features included temperature, humidity, soil moisture, and light intensity, while the target variable was irrigation
time in seconds. The dataset was split into 80% training and 20% testing partitions. The model achieved an R² score of
0.91, indicating strong predictive capability. Predictions were generated every 15 minutes and cross-checked against
real-time conditions. When the forecasted irrigation time deviated significantly from the baseline, the machine learning
output overrode the rule-based logic, ensuring that water delivery was adapted to actual environmental needs.
Parameter
Value
Dataset Size
3,200 points
Training/Test Split
80% / 20%
Features
Temp, Humidity, Moisture, Light
Target
Irrigation Time (sec)
Evaluation Metric
RMSE, MAE, R²
Model Accuracy
R² = 0.91
Table IV. Random Forest Model Configuration
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To manage energy efficiently, the charge controller prevented battery overcharging, and the system reduced power
consumption by limiting non-essential functions during periods of low sunlight, prioritizing critical operations like data
logging and irrigation. This adaptive energy management strategy ensured uninterrupted operation while maximizing
the use of solar power.
Component
Description
Essential Parameters
Soil Moisture Sensor
(FC-28)
Measures the moisture content of the
soil to optimize irrigation.
Moisture Range: 0% - 100%
Threshold: 30% for irrigation trigger
Accuracy: ±3% to ±10%
Temperature and
Humidity Sensor
(DHT22)
Monitors the ambient temperature and
humidity in the growing environment.
Temperature Range: -40°C to 80°C
Humidity Range: 0% - 100%
Accuracy: ±0.5°C for temp
Accuracy: ±2% for humidity
Light Intensity
Sensor (BH1750)
Measures the ambient light intensity to
control LED grow lights.
Light Intensity Range: 0 to 65535 lux
Threshold: 3000 lux for LED activation
Raspberry Pi 4
(Central Controller)
Acts as the main controller for the
system, processing sensor data and
making decisions.
Processor: Quad-core ARM Cortex-A72
RAM: 2GB/4GB/8GB
Connectivity: Wi-Fi, Ethernet
Solar Panel (100W)
Powers the entire system through solar
energy, reducing grid dependence.
Power Output: 100W
Efficiency: Variable based on sunlight intensity
Voltage: 12V DC
Battery and Charge
Controller
Stores energy for night-time use and
manages charging of the battery.
Battery Capacity: 12V, 12Ah
Charge Controller: Protects from overcharging,
efficient energy management
DC Water Pump
(12V)
Delivers water to the plants based on
the moisture level.
Voltage: 12V DC
Flow Rate: 4-6 liters/min
Power Consumption: 3- 5W
LED Grow Lights
Provides artificial light to support plant
photosynthesis when natural light is
insufficient.
Power: 20W-30W per panel
Color Temperature: 6000-6500K (Daylight)
Cloud-based
Dashboard
A web-based interface for remote
monitoring and management of the
system.
Real-time Monitoring: Temperature, humidity,
light, and moisture levels
User Control: Manual override available
Table V. Components used in the system along with their essential parameters
4. Results and Discussion. - This section discusses the results obtained from the IoT-powered vertical farming system,
tested with cherry tomato crops over a 6-week trial period. The key parameters analyzed include water usage efficiency,
energy consumption, crop yield, temperature and humidity control, and light intensity. While the system shows
promising improvements compared to traditional farming, the scope of the experiment is limited, and statistical analysis
has been added to strengthen validity.
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Figure III. Assembled hardware of the proposed system model.
4.1 Water Usage Efficiency. - Water efficiency is critical for sustainable farming, especially for crops like cherry
tomatoes that require precise irrigation. Using soil moisture sensors integrated with a Random Forest model, the system
automated irrigation to maintain soil moisture between 40%-45%, compared to 33%-38% in traditional farming (see
Figure V). This resulted in average water savings of 28.6% (±2.3% standard deviation), as shown in Table VI.
Parameter
Traditional
Farming
Vertical Farming with IoT
Improvement (%)
Average Soil Moisture (%)
35.5 ± 2.1
42.5 ± 1.8
-
Water Usage (liters)
10.5 ± 0.7
7.5 ± 0.5
28.6 ± 2.3
Table VI. Water Usage Efficiency and Soil Moisture Comparison
Figure IV. Reading of FC-28 on the user interface
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Figure V. Soil Moisture Readings.
4.2 Energy Consumption. - Energy consumption was monitored weekly, comparing the solar-powered vertical
farming system with traditional farming relying on grid electricity. Vertical farming consumed between 2.9 to 3.3 kWh
per week, significantly less than traditional methods (4.8 to 5.2 kWh). Solar panel efficiency ranged from 35.3% to
40% (see Table VII). Energy savings averaged 39.1% (±1.5%). Figure 6 includes error bars showing weekly
consumption variance.
Week
Traditional Energy
(kWh)
Vertical Farming Energy
(kWh)
Energy Efficiency
(%)
Std. Dev
(Efficiency)
1
5.0 ± 0.1
3.0 ± 0.1
40
±1.2
2
4.8 ± 0.2
2.9 ± 0.1
39.6
±1.3
3
5.2 ± 0.1
3.1 ± 0.2
40.4
±1.5
4
5.0 ± 0.1
3.2 ± 0.1
36
±1.1
5
4.9 ± 0.2
3.0 ± 0.1
38
±1.4
6
5.1 ± 0.1
3.3 ± 0.2
35.3
±1.6
Table VII. Weekly Energy Consumption and Solar Panel Efficiency
4.3 Crop Yield. - The vertical farming system yielded 60-65% more cherry tomatoes per plant compared to traditional
farming (Table VIII). Specifically, plants produced an average of 9 (±1.2) fruits versus 5.5 (±1.0) in the traditional
setup. While promising, the limited crop variety and short 6-week trial restrict broader applicability.
Parameter
Traditional Farming
(4 Plants)
Vertical Farming (4
Plants)
Yield Increase
(%)
Average Fruits per Plant
5.5 ± 1.0
9.0 ± 1.2
63.6 ± 5.4
Total Fruits (6 Weeks)
22 ± 4
36 ± 5
-
Table VIII. Comparison of Crop Yield between Traditional and IoT-Driven Vertical Farming
4.4 Temperature and Humidity Control. - Stable environmental conditions are vital for crop health. The vertical
farming system-maintained temperatures between 29°C and 34°C (±1.2°C) and humidity between 58% and 68% (±3%)
as can be seen in Figure VI, whereas traditional farming saw wider fluctuations (31°C-36°C, ±2°C; 52%-60%, ±4%).
This controlled environment improved plant health and fruit quality.
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Figure VI. Temperature and Humidity level of a system
Week
Temp Traditional
(°C)
Temp Vertical
(°C)
Humidity Traditional (%)
Humidity Vertical (%)
Avg ± SD
33.0 ± 1.5
31.5 ± 1.2
57 ± 4
63 ± 3
Table IX. Average Temperature and Humidity Comparison with Variability
4.5 Light Intensity and Growth Conditions. - Light intensity was consistently higher and more stable in the vertical
system, ranging from 1150 to 1300 lux, compared to 950 to 1050 lux outdoors. This represents a 22.5% ± 2.1% increase
in light availability, promoting better photosynthesis and growth (Table X).
Week
Light Intensity Traditional
(lux)
Light Intensity Vertical
(lux)
Increase
(%)
Avg ± SD
1005 ± 40
1225 ± 55
22.5 ± 2.1
Table X. Light Intensity Comparison
4.6 Baseline Comparison with Other Smart Farming Systems. - To provide broader context for our system’s
performance, Table XI compares key metrics such as water savings, yield improvement, and energy use against other
smart farming solutions reported in the literature. While some field-based IoT irrigation systems demonstrate higher
water savings due to starting from less efficient traditional methods, our vertical farming system achieves competitive
water efficiency alongside a notably higher yield increase, thanks to its controlled environment. Additionally, the
integration of solar power enhances sustainability by reducing reliance on grid electricity an aspect often unreported in
other studies. This comparison highlights the practical benefits and unique strengths of our approach within the
landscape of smart agriculture technologies.
System Type
Water Savings vs
Traditional
Yield Increase
Energy Use Notes
Our Vertical Farm (IoT +
Solar)
28.6 ± 2.3 %
63.6 ± 5.4 %
~3.0 kWh/week (solar
powered)
[22]
~50 %
~35 %
Not reported
[23]
47.8 %
~34.9 %
Not reported
[24]
~35 %
~24–30 %
—
Table XI. Benchmark Comparison with Other Smart Farming Systems
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5. Conclusion and Future Work. - This study successfully developed a smart, solar-powered vertical farming system
tailored to the unique agricultural challenges of Pakistan. By integrating real-time environmental monitoring,
automated irrigation, and machine learning (Random Forest), the system significantly improved resource efficiency
and crop productivity compared to traditional farming. Over a six-week trial, it reduced water use by 28.57%, cut
energy consumption by 40%, and increased cherry tomato yield by 60–65%. Its off-grid, solar-powered design ensured
reliable operation while maintaining ideal growing conditions, something hard to achieve in open-field farming.
However, this study has some limitations. The experimental scope was limited to a single crop and a short trial period,
which restricts the generalizability of the findings. Additionally, long-term environmental impacts and system
durability were not assessed, indicating the need for extended studies. Scalability challenges also remain, such as the
initial cost of setup, ongoing maintenance requirements, and adapting the system to diverse local conditions and crop
types. Addressing these issues will be critical to ensuring wider adoption.
Looking forward, future work should focus on scaling the system for commercial use, expanding to a broader variety
of crops, and integrating more advanced AI models for further optimization. Efforts to reduce costs and simplify
maintenance will enhance accessibility for small-scale farmers. Moreover, long-term field studies evaluating
environmental and economic impacts are essential. With supportive policies and investments, this innovation could
play a vital role in driving sustainable agriculture and improving food security in Pakistan.
S. Ur Rehman, M. Adeel Mannan, M. Ahsan Shaikh, M. Uzair
Memoria Investigaciones en Ingeniería, núm. 29 (2025). pp. 95-108
https://doi.org/10.36561/ING.29.7
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Memoria Investigaciones en Ingeniería, núm. 29 (2025). pp. 95-108
https://doi.org/10.36561/ING.29.7
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay
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Author contribution:
1. Conception and design of the study
2. Data acquisition
3. Data analysis
4. Discussion of the results
5. Writing of the manuscript
6. Approval of the last version of the manuscript
SUR has contributed to: 1, 2, 3 and 4.
MAM has contributed to: 4, 5 and 6.
MAS has contributed to: 6.
MU has contributed to: 6.
Acceptance Note: This article was approved by the journal editors Dr. Rafael Sotelo and Mag. Ing. Fernando A.
Hernández Gobertti.