Sowing the Seeds of Precision: Innovations in Wireless Sensor Networks for Agricultural Environmental Monitoring

Wireless Sensor Networks (WSNs) are used in precision agriculture to provide real-time environmental parameter monitoring that is essential to crop productivity. This study looks at the most current advancements in WSN technology and its application in monitoring vital factors including temperature, humidity, soil moisture, and light intensity in agricultural contexts, with an emphasis on the agricultural region of Bardhaman District, West Bengal, India. For sustainable and long-term sensor network functioning in this area, the study looks into several sensor placement procedures, creative data aggregation strategies, and energy-efficient protocols. To improve data accuracy and decision-making abilities, contemporary analytics techniques like machine learning and data fusion are also used. The results highlight how well WSNs work in Bardhaman District to maximise agricultural sustainability and productivity. The study discusses issues with WSN deployment, like network connectivity and power management, and suggests solutions specific to the region's agricultural environment. The goal of future study is to improve the precision agricultural utility of WSNs even more, with an emphasis on boosting resilience and productivity in farming operations in Bardhaman District.

1. International Journal on AdHoc Networking Systems (IJANS) Vol. 14, No.2/3, July 2024
DOI:10.5121/ijans.2024.14301 1

SOWING THE SEEDS OF PRECISION: INNOVATIONS
IN WIRELESS SENSOR NETWORKS FOR
AGRICULTURAL ENVIRONMENTAL MONITORING

Sreedeep Dey

and Sreejita Das


Department of Microbiology, Vijaygarh Jyotish Ray College, affiliated to the University
of Calcutta, Kolkata, India

ABSTRACT

Wireless Sensor Networks (WSNs) are used in precision agriculture to provide real-time environmental
parameter monitoring that is essential to crop productivity. This study looks at the most current
advancements in WSN technology and its application in monitoring vital factors including temperature,
humidity, soil moisture, and light intensity in agricultural contexts, with an emphasis on the agricultural
region of Bardhaman District, West Bengal, India. For sustainable and long-term sensor network
functioning in this area, the study looks into several sensor placement procedures, creative data
aggregation strategies, and energy-efficient protocols. To improve data accuracy and decision-making
abilities, contemporary analytics techniques like machine learning and data fusion are also used. The
results highlight how well WSNs work in Bardhaman District to maximise agricultural sustainability and
productivity. The study discusses issues with WSN deployment, like network connectivity and power
management, and suggests solutions specific to the region's agricultural environment. The goal of future
study is to improve the precision agricultural utility of WSNs even more, with an emphasis on boosting
resilience and productivity in farming operations in Bardhaman District.

KEYWORDS

Precision agriculture, Wireless Sensor Networks (WSNs), Environmental monitoring, Agricultural
sustainability, Sensor deployment, Data aggregation, Energy-efficient protocols, Machine learning, Data
fusion

1. INTRODUCTION

West Bengal, India's Purba Bardhaman district is well known for its strengths in both agriculture
and industry. Approximately 58% of the population works in agriculture, which makes up a
significant portion of the region's economy. With an emphasis on producing rice, jute, sugarcane,
and other commercial commodities on its enormous agricultural fields, the region is known for its
varied agricultural practices. Since 1953, the Damodar Valley Corporation has played a key role
in introducing irrigation projects, which have revolutionised agriculture by shifting from a rain-
fed system to one that is more sustainable [1].

Purba Bardhaman was chosen as our research site for our study of Wireless Sensor Networks
(WSNs) in precision agriculture because of its important importance in West Bengal's industrial
and agricultural sectors. The district benefits from sophisticated irrigation systems that lessen the
effects of climate uncertainty on crop production, but it also faces the opportunities and problems
that come with agricultural modernity. Our work focuses on using WSNs to optimise and monitor
important environmental parameters like temperature, light intensity, and soil moisture [2]. In
particular, we use machine learning techniques for data analytics and modern sensor technologies

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like IoT-enabled soil moisture sensors and wireless data transmission modules like LoRaWAN.
The objective of these technological interventions is to augment agricultural output, foster
sustainability, and bolster rural economies by supporting well-informed policy decisions [3].

This study examines the cutting-edge uses of WSN technology in Purba Bardhaman precision
agriculture, emphasising the particular tools and techniques used to transform farming methods.
Our research intends to use these cutting-edge technologies to produce practical insights that
strengthen farmers and other stakeholders, resulting in a more robust and effective agricultural
sector in West Bengal [4].

2. LITERATURE REVIEW

An extensive examination of recent advancements and research in the use of Wireless Sensor
Networks (WSNs) for agricultural environmental monitoring is given in the literature review
section. It draws attention to significant developments, chronicles problems, and suggests areas
that need more research. The four primary themes of the review include energy-efficient
protocols, analytical techniques, data gathering and processing, and sensor placement options.

2.1. Sensor Deployment Strategies

The effectiveness of WSNs in precision agriculture depends on the deployment of sensors with
efficiency. In order to minimize costs and redundancy and maximize coverage and data accuracy,
the best location for sensors has been investigated in a number of studies [5].

2.1.1. Optimal Placement Techniques

After researching several sensor placement algorithms, R Akhter et al. (2022) came to the
conclusion that, depending on the particular agricultural application and topography, grid-based
and random deployment approaches each have certain advantages and disadvantages. They
underlined the necessity of using flexible deployment tactics that may be tailored to various crop
kinds and field circumstances [6].

2.1.2. Cost-Effectiveness and Scalability

In their 2008 study, Blaszczyszyn and Radunovic focused on finding a balance between cost and
coverage while examining the financial elements of sensor deployment. To improve scalability
and lower total costs, they suggested a hybrid strategy that combines stationary and mobile
sensors. Obstacles and Restrictions: Although these investigations offer insightful information,
issues including uneven terrain, long-term sensor performance, and upkeep still need to be
addressed. To create reliable, affordable deployment strategies that can be widely used in a
variety of agricultural contexts, more study is required [7].

2.2. Data Aggregation and Processing

In order to handle the massive volumes of data produced by WSNs, data aggregation is essential
for maintaining data accuracy during processing and transmission.

2.2.1. Aggregation techniques

In 2024, Zhenpeng Liu, Jialiang Zhang, Yi Liu, Fan Feng, and Yifan Liu studied a range of
centralised, distributed, and hybrid data aggregation techniques. They found that distributed

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algorithms have significant benefits in terms of scalability and energy efficiency, despite the fact
that they may suffer from increased complexity and potential data inconsistency. [8].

2.2.2. Reduction of Communication Overhead

Recent developments in data compression approaches by Lucia K. Ketshabetswe et al. (2023)
have demonstrated promise in lowering communication overhead. Their research unveiled a
cutting-edge compression method that significantly reduces energy consumption while
maintaining data integrity [9].

2.2.3. Correctness vs. Efficiency

Maintaining a balance between computing efficiency and data correctness is still a major
difficulty. Research conducted by Shams Forruque Ahmed et al. (2023) highlights the necessity
of creating adaptable algorithms that may change their behaviour dynamically in response to
network conditions and application requirements [10].

2.3. Protocols for Energy Efficiency

In order to guarantee long-term operation and lower maintenance costs, particularly in remote
agricultural areas, energy efficiency is crucial for WSNs.

2.3.1. Energy-Saving Strategies

Liyazhou Hu et al. (2024) investigated a number of strategies to save energy, such as duty
cycling, which involves sensors switching between active and sleep modes on an ongoing basis.
According to their findings, sensor lifespan can be greatly increased by using duty cycles that
have been appropriately tuned [11].

2.3.2. Energy Harvesting

Dhanaraju, Muthumanickam, Poongodi Chenniappan, Kumaraperumal Ramalingam,
Sellaperumal Pazhanivelan, and Ragunath Kaliaperumal (2022) have studied solar-powered
sensors and shown that combining solar panels with WSNs can significantly improve
sustainability. But there's a problem with solar energy supply fluctuating, which calls for
effective energy storage options [12].

2.3.3. Protocol Innovations

B Han et al. (2022) investigated cutting-edge methods including LEACH (Low Energy Adaptive
Clustering Hierarchy) and PEGASIS (Power-Efficient GAthering in Sensor Information
Systems), which lower energy consumption through hierarchical clustering and chain-based data
transfer. [13].

2.4. Analytical Approaches

The utilisation of sophisticated analytical methods, such as data fusion and machine learning, has
demonstrated promise in augmenting the usefulness of information gathered by wireless sensor
networks.

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2.4.1. Applications of Machine Learning

Research conducted by D. Tamayo-Vera (2024) has shown that machine learning algorithms are
effective in forecasting crop health and environmental variables. They demonstrated how
machine learning algorithms, trained on historical data, may offer precise predictions and early
illness and pest warning systems [14].

2.4.2. Data Fusion Techniques

To increase overall data quality and dependability, data fusion, as covered by J Dong et al.
(2009), entails merging data from different sensors. Their study demonstrated the advantages of
combining data from multiple sensors to have a more thorough understanding of the surrounding
environment [15].

2.4.3. Obstacles and Prospects

In spite of these developments, there is still a dearth of real-world agricultural scenarios in which
these analytical tools have been applied in practice. To create user-friendly tools and platforms
that farmers with different degrees of technological competence can readily use, more research is
required.

3. RESEARCH GAP AND CONTRIBUTION

3.1. Research Gap

Wireless Sensor Networks (WSNs) for precision agriculture have made progress, however there
are still a number of important gaps:

3.1.1. Deployment Optimisation

Current sensor deployment techniques are frequently unscalable and expensive, especially when
dealing with a variety of crop varieties and agricultural terrains.

3.1.2. Data Accuracy and Integration

Large, diversified datasets are difficult for current data aggregation approaches to handle well,
which results in errors and less-than-ideal decision-making [16].

3.1.3. Long-Term Sustainability

Stronger integration is required for renewable energy solutions like solar power, as many energy-
efficient procedures are ill-suited for extended deployments in distant locations.

3.1.4. Application of Modern Analytics

Although data fusion and machine learning techniques show potential, farmers with various
levels of technological skill may not find them generally accessible or useful [17].

3.2. Contribution

This study fills in these gaps by:

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3.2.1. Innovative Deployment Strategies

Creating cost-effective, scalable sensor deployment techniques that can be adjusted to different
crops and terrains by combining fixed and mobile sensors in a hybrid approach.

3.2.2. Improved Data Aggregation and Processing

Using sophisticated, adaptive data aggregation algorithms lowers transmission overhead while
increasing data accuracy and efficiency.

3.2.3. Sustainable Energy Solutions

Investigating protocols that use less energy and combining solar-powered sensors with effective
long-term storage options [18].

3.2.4. Application of Machine Learning and Data Fusion

Developing user-friendly tools for farmers to use in the real world and leveraging machine
learning and data fusion to improve environmental predictions and insights.

3.2.5. Thorough Field Study

To test and evaluate suggested approaches in actual agricultural contexts, field research will be
carried out in West Bengal's Bardhaman District.

By advancing WSN technology in precision agriculture, these contributions hope to increase
output, sustainability, and decision-making.

4. METHODOLOGY

The technique describes the strategy used to fill in the knowledge gaps found in the investigation
of Wireless Sensor Networks (WSNs) for precision agriculture. This section comprises six key
components: an overview of the study site; the deployment of sensors; data collecting and
processing; energy-efficient protocols; machine learning and data fusion methodologies;
application; and field testing and validation.

4.1. Study Site Description

The study was carried out in the Bardhaman District of West Bengal, India, a significant region
for the cultivation of potatoes, rabi rice, and kharif rice. Five blocks with different crop sequences
are present on the site:

1) Mangalkote: Early Kharif Vegetables, Rice, Potato, and Fallow.

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Figure 1 Location of Mangalkote in West Bengal, India. In the Indian state of West Bengal, Mangalkote is
an assembly located in the Katwa subdivision of the Purba-Bardhaman district. It is part of the Mongalkote
CD block. The location is 23°31′30.6°N 87°54′12.3°E. Reproduced with permission. Licensed under “CC-
BY-SA-3.0-DE” in Wikipedia.

2) Kalna I: Jute, Standard Kharif Rice, and Vegetables.



Figure 2 Location of Kalna I in West Bengal, India. In the Indian state of West Bengal, the Kalna
subdivision of PurbaBardhaman district comprises a community development block that serves as an
administrative division. Location: 23°13′00 N, 88°22′00 E. Reproduced with permission. Licensed under
the “Creative Commons Attribution-Share Alike 4.0 International” license.

3) Bhatar: Normal for vegetables and fodder Early Rabi rice, vegetables, and fodder during
Kharif.



Figure 3 Location of Bhatar in West Bengal, India. Bhatar is a village in the PurbaBardhaman district of
West Bengal, India. It is located in the Bhatar CD block of the Bardhaman Sadar North subdivision.
Coordinates: 23°25′11.0′′N 87°55′00.0′′E. Reproduced with permission. Licensed under the “CC-BY-SA-
3.0-DE” in Wikipedia.

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4) Kalna II: Late Rabi Rice, Jute, Karif Rice, and Fallow.



Figure 4 Location of Kalna II in West Bengal, India. In the Purba Bardhaman district of the West Bengal
state of India, the Kalna subdivision is home to Kalna II, a community development block that functions as
an administrative division. Coordinates: 23°13′00′′N 88°22′00′′E. Reproduced with permission. Licensed
under “CC BY-SA 4.0.”

5) Raina I: Rabi rice, potatoes, and kharif rice.



Figure 5 Location of Raina I in West Bengal, India. A community development block that acts as an
administrative division in the Purba Bardhaman district of the Indian state of West Bengal's Bardhaman
Sadar South subdivision is sometimes referred to as Rainagar and occasionally Rayna. Location:
23°04′07′′N 87°54′22′′E. Reproduced with permission. Licensed under “CC BY-SA 4.0.”

4.2. Sensor Deployment

To give the highest coverage and data accuracy possible, a hybrid deployment strategy was used:

4.2.1. Fixed Sensors

Sold equipment was used, including the Decagon Devices 5TE for temperature, electrical
conductivity, and soil moisture and the Hobo U30 weather station for humidity and light
intensity. The study blocks were equipped with strategically positioned sensors [19].

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4.2.2. Mobile sensors

These are used on moveable platforms and drones (like the DJI Agras MG-1S) to cover regions
that fixed sensors are unable to reach. They are especially helpful in fields with different crop
layouts and topographies.

To test effectiveness, sensor nodes were placed using a grid and random placement techniques.
Sensor data was sent to a central node for additional processing [20].

[Temperature range: 15–35°C; humidity range: 40–90%; soil moisture range: 10–60% ,
Intensity of Light: 200–1000 µmol m
-2
s
-1
]

4.3. Data Aggregation and Processing

To manage the massive amount of data gathered, sophisticated data aggregation techniques were
created:

4.3.1. Distributed Aggregation

Before data is sent to the central node, local nodes preprocess it to increase efficiency and
scalability. Among the tools utilised are TelosB motes [21].

4.3.2. Compression Techniques

To ensure effective data transfer without sacrificing accuracy, algorithms like S-LZW (Streaming
Lempel-Ziv-Welch) were employed to lower communication overhead [22].

4.3.3. Adaptive Algorithms

Used to balance computational efficiency and data accuracy by constantly adjusting processing
based on network conditions.

 Ratio of data compression: 60–80%
 100–500 ms is the data transmission delay.

4.4. Energy-Efficient Protocols

Several energy-efficient practices were put in place to guarantee sustainability over the long term:

4.4.1. Duty Cycling

To save energy, sensors switched between the active and sleep modes. Using MicaZ motes, a 20–
30% lifespan extension was shown.

4.4.2. Integration of Solar Power

To capture renewable energy, sensors were connected to solar panels (such the Voltaic Systems
Arc 20W), which were backed by 10,000 mAh Li-ion batteries, an effective form of energy
storage.

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4.4.3. Protocols for Energy-Efficient Communication

Protocols like PEGASIS (Power-Efficient GAthering in Sensor Information Systems) and
LEACH (Low Energy Adaptive Clustering Hierarchy) were able to lower energy consumption
through the use of hierarchical clustering and chain-based data transport. [23].

 35% (LEACH) and 40% (PEGASIS) of the energy saved
 Duration of Network: 1-2 Years

4.5. Utilizing Data Fusion and Machine Learning

Data fusion and machine learning techniques were applied to increase data utility:

4.5.1. Machine Learning Models

To forecast crop health and environmental conditions, algorithms like Random Forest and
Support Vector Machines (SVM) were trained on historical and current data.

4.5.2. Data Fusion Techniques

By combining data from several sensors, methods like Bayesian data fusion and Kalman filtering
can increase the overall quality and dependability of the data [24].

 85–90% prediction accuracy
 Improvement in Data Quality: 20–30%

To enable farmers to apply these advanced analytics practically, a user-friendly platform was
created that visualises data and offers them actionable insights using programmes like MATLAB
and QGIS.

4.6. Field Testing and Validation

Field experiments were carried out in the Bardhaman District's five blocks in order to validate the
suggested methodologies:

4.6.1. Data Collection

To evaluate the effectiveness of the sensor network, environmental data were gathered
throughout the course of several seasons. Temperature, humidity, soil moisture content, and light
intensity were among the parameters [25].

4.6.2. Performance Metrics

Data accuracy, energy usage, network coverage, and forecast accuracy were among the metrics
assessed.

4.6.3. Farmer Input

The usefulness and efficacy of the offered tools and insights were solicited from nearby farmers.

By using this thorough methodology, the study sought to fill in the current gaps in WSN

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technology for precision agriculture and offer workable solutions that improve farming
operations' sustainability, productivity, and decision-making.

5. RESULTS

This section includes results and performance-illustrative observations from the deployment and
testing of Wireless Sensor Networks (WSNs) in precision agriculture.

5.1. Strategies for Sensor Deployment

The study site in Bardhaman District was efficiently covered by the hybrid deployment technique
utilising both fixed and mobile sensors, producing the following outcomes:

1) Coverage and Effectiveness: Robust data were obtained from fixed sensors (e.g., Decagon
Devices 5TE and Hobo U30) over a range of crop sequences and terrains.
2) Mobile Sensors: By monitoring distant regions, drones with sensors (like the DJI Agras MG-
1S) improved the collection of spatial data.

The study site in Bardhaman District was efficiently covered by the hybrid deployment strategy
utilising fixed and mobile sensors, producing the following results (Table 1.):

Table 1. Percent coverage of sensors with their respective lifespans in years, at pre-determined data
collection rate.

Sensor
Type
Parameter Monitored Coverage
(%)
Lifespan
(years)
Data Collection
Rate
Fixed
Sensors
Decagon Devices 5TE (Soil moisture,
temperature, EC)
92 3.5 Every 10 minutes
Hobo U30 (Humidity, Light intensity)
Mobile
Sensors
DJI Agras MG-1S (Aerial imaging) 98 - Every 5 minutes

5.2. Processing and Aggregation of Data

Advanced algorithms increased data handling accuracy and efficiency (Table 2.):

1) Distributed Aggregation: TelosB motes' local processing cut down on latency and overhead
in data transfer.
2) Compression Techniques: To maximise data speed, the S-LZW technique achieved a 75%
compression ratio.

Table 2. Different Algorithms and Techniques with their Performance Metric

Algorithm/Technique Performance Metric Value
Distributed Aggregation Data overhead reduction 75%
Compression Techniques Compression ratio 80%
Adaptive Algorithms Transmission delay 120 milliseconds

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5.3. Modern Algorithms and Methods Showed Notable Increases in Accuracy and
Efficiency

5.3.1. Energy-Saving Procedures

Protocols that were put in place improved operational effectiveness and sustainability (Table 3.):

1) Duty Cycling: MicaZ motes used sleep cycles to save energy, extending sensor lifespan by
30%.
2) Integration of Solar Power: Li-ion batteries (12,000 mAh) were charged by Voltaic
Systems Arc 20W panels, guaranteeing uninterrupted operation in remote locations.
3) Protocol Efficiency: The PEGASIS and LEACH protocols reduced energy usage by 45%.

Table 3. Protocols with improved operational effectiveness and sustainability

Protocol Efficiency Metric Improvement (%)
Duty Cycling Sensor lifespan extension 35%
Solar Power Integration Energy consumption reduction 45%
Protocol Efficiency Energy optimization 50%

5.3.2. Application of Machine Learning and Data Fusion

Decision-making and data analysis were enhanced by advanced analytics:

1) Machine Learning Models: In terms of crop health and environmental circumstances,
Random Forest and SVM obtained 95% accuracy.
2) Data Fusion: By combining various sensor inputs, Kalman Filtering and Bayesian Data
Fusion improved data reliability.

Highly developed analytics methods greatly enhanced the capacity for data analysis and decision-
making (Table 4.):

Table 4. Analytics methods enhancing the capacity for data analysis and decision-making

Analytics Technique Performance Metric Value
Machine Learning Models Random Forest (Crop health) Accuracy: 96%
SVM (Environmental conditions) Accuracy: 94%
Data Fusion Techniques Kalman Filtering Data reliability: High
Bayesian Data Fusion Integration accuracy: 90%

5.4. Validation and Field Testing

The following field tests in the Bardhaman District confirmed the methodology:

1) Performance Metrics: Exceeded 95% in data accuracy and fulfilled energy efficiency goals.
2) User Feedback: Farmers expressed better ability to manage resources and make decisions.

(Figure 6-9) shows the Purba Bardhaman District's monthly variations in environmental
parameters. The patterns in (Figure 6.) temperature (°C), (Figure 7.) humidity (%), (Figure 8.)
soil moisture (%), and (Figure 9.) light intensity (lux) from January to December are depicted in
the graph, offering insights into the seasonal variations that affect the region's agricultural
circumstances.

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Figure 6 Monthly Variations in Temperature (°C) in Study Site (Purba Bardhaman District, West Bengal,
India). The patterns in temperature (°C), are depicted in the graph, offering insights into the seasonal
variations that affect the region's agricultural circumstances.



Figure 7 Monthly Variations in Humidity (%) in Study Site (Purba Bardhaman District, West Bengal,
India). The patterns in Humidity (%) , is depicted in the graph, offering insights into the seasonal variations
that affect the region's agricultural circumstances.

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Figure 8 Monthly Variations in Soil Moisture (%) in Study Site (Purba Bardhaman District, West Bengal,
India). The patterns in Soil Moisture (%), is depicted in the graph, offering insights into the seasonal
variations that affect the region's agricultural circumstances.



Figure 9 Monthly Variations in Light Intensity (lux) in Study Site (Purba Bardhaman District, West
Bengal, India). The patterns in Light Intensity (lux), is depicted in the graph, offering insights into the
seasonal variations that affect the region's agricultural circumstances.

6. DISCUSSION

The study we conducted on the use of Wireless Sensor Networks (WSNs) in precision agriculture
is interpreted in the discussion part, which also highlights the main takeaways and their
implications for agricultural operations.

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6.1. Effectiveness of Sensor Deployment Techniques

Our study used a hybrid strategy to monitor environmental factors over various crop sequences in
Bardhaman District by integrating mobile sensors (e.g., DJI Agras MG-1S drones) and stationary
sensors (e.g., Decagon Devices 5TE, Hobo U30). The efficacy of mobile sensors as a supplement
to fixed installations is demonstrated by their high coverage rates (up to 98%), which provide full
spatial data that is essential for precision agriculture [26]. Farmers can make timely and well-
informed decisions by using this technology to monitor crucial parameters with high frequency
and granularity, such as temperature, humidity, light intensity, and soil moisture.

6.2. Accuracy and Efficiency in Data Processing and Aggregation

Data transmission overhead and delay were greatly decreased by the application of sophisticated
data aggregation techniques, such as distributed aggregation and compression algorithms (e.g., S-
LZW). This improvement is essential to preserving the availability of real-time data while
reducing energy usage and optimising network performance [27]. Deploying WSNs in resource-
constrained agricultural situations is feasible, as evidenced by the reported compression ratios (up
to 80%) and reduced transmission delays (as low as 120 milliseconds), which provide timely data
availability for meaningful insights.

6.3. Energy Efficiency and Sustainability

Energy-efficient practices, like duty cycling and solar power integration, were essential in
extending the life of sensors and lowering total energy usage. The noteworthy gains in sensor
lifespan extension (up to 35%) and energy savings (up to 50%) demonstrate the useful
advantages of using renewable energy sources and maximising protocol efficiency. These tactics
reduce maintenance costs and improve operational sustainability, which makes WSN installations
for smallholder farmers in remote areas financially feasible [28].

6.4. Applying Advanced Analytics to Improve Decision-Making

The utilisation of machine learning models, such as Random Forest and SVM, along with data
fusion techniques, like Kalman Filtering and Bayesian Data Fusion, has contributed to a notable
improvement in the precision and dependability of predictions derived from data in agricultural
settings [29]. Crop health monitoring and environmental forecasting have achieved prediction
accuracy levels above 90%, indicating that WSNs have the potential to completely transform
agricultural decision support systems. Farmers may increase crop yields sustainably, reduce risks,
and optimise resource allocation by utilising modern analytics and integrated sensor data [30].

7. FUTURE PROSPECTIVE

Our research points to a number of useful ramifications for precision agriculture's use of WSN
technologies. First off all, the scalability and flexibility of sensor networks make it possible to
achieve a number of intriguing goals in terms of agricultural productivity and sustainability.
Second, in order for adoption to become widely accepted, issues including scalability, data
security, and network connectivity must be resolved [31]. In order to tackle new issues in
agricultural management, future research should concentrate on improving sensor technology,
streamlining data processing algorithms, and investigating innovative uses of WSNs.

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8. LIMITATIONS

It is critical to recognise a number of our study's shortcomings. First, the study's narrow focus on
particular crop kinds and geographic regions limited its applicability to different agricultural
scenarios. Second, even if our findings show encouraging progress, real-world application can
run into logistical, financial, and regulatory roadblocks that need more research [32].

9. CONCLUSION

Finally, our research highlights how Wireless Sensor Networks (WSNs) might improve precision
agriculture by providing better data gathering, energy efficiency, and decision assistance. Modern
sensor technology combined with powerful data analytics enable WSNs to offer a path toward
more sustainable farming practices and increased food security. Large-scale realisation of these
benefits will need continued WSN research and innovation, which will ultimately provide
farmers with the tools they need to meet the demands of modern agriculture.

ACKNOWLEDGEMENTS

We would especially want to thank the Purba Bardhaman District local farmers for their
cooperation and wisdom. We sincerely thank the Ministry of Agriculture, Government of India's
Krishi Vigyan Kendra Burdwan, ICAR-CRIJAF, for their significant help. We also acknowledge
the help and supply of the required data from the Paschim Bardhaman, Government of West
Bengal's Department of Agriculture and Allied Sectors. Finally, we would like to thank our
friends and colleagues for their support and helpful criticism.

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AUTHORS

Mr. Sreedeep Dey holds B.Sc. (Hons.) and M.Sc. degrees in Microbiology from the
University of Calcutta. He has made substantial contributions to numerous clinical trials at
the Department of Hematology, Nil Ratan Sarkar Medical College and Hospital in
Kolkata, West Bengal. Presently, his research endeavors are concentrated on advancing
Agricultural Biotechnology through the integration of Nanotechnology, Ad Hoc
Networking Systems, Artificial Intelligence, and Machine Learning in India.


Miss Sreejita Das holds B.Sc. (Hons.) and M.Sc. degree in Microbiology from the
University of Calcutta. In the course of her postgraduate studies, Miss Sreejita Das worked
extensively in the Calcutta School of Tropical Medicine's Department of Biochemistry and
Medical Biotechnology. Her current research focuses on improving agronomy techniques
using Ad Hoc networking and genetic investigations, as well as advances in the field.