=Paper=
{{Paper
|id=Vol-3940/AISD-2024_Paper_8
|storemode=property
|title=Enhancing Precision Agriculture using Adaptive Machine Learning Models on Dynamic Data from Wireless Sensor Networks for Crop Monitoring
|pdfUrl=https://ceur-ws.org/Vol-3940/AISD-2024_Paper_8.pdf
|volume=Vol-3940
|authors=Aneesh Kumar Mourya, Namrata Nagpal,Meenakshi Srivastava
|dblpUrl=https://dblp.org/rec/conf/aisd/MouryaNS24
}}
==Enhancing Precision Agriculture using Adaptive Machine Learning Models on Dynamic Data from Wireless Sensor Networks for Crop Monitoring ==
https://ceur-ws.org/Vol-3940/AISD-2024_Paper_8.pdf
Enhancing Precision Agriculture using Adaptive Machine
Learning Models on Dynamic Data from Wireless Sensor
Networks for Crop Monitoring
Aneesh Kumar Mourya1, Namrata Nagpal1 and Meenakshi Srivastava1
1Amity Institute of Information Technology , Amity University Uttar Pradesh, Lucknow, India
Abstract
The agricultural sector in India forms the backbone of the nation's economy and serves as the primary source of
employment for a significant portion of the population. However, effectively monitoring crop conditions and
environmental factors raises a challenge due to the vast and diverse agricultural landscape across the country.
Precision agriculture has emerged as a critical innovation for sustainable farming, enabling the optimization of
resources and maximizing crop yields. This paper presents an approach that leverages adaptive machine learning
(ML) models in combination with dynamic data collected from Wireless Sensor Networks (WSNs) for real-time
crop monitoring. WSNs, deployed across agricultural fields, gather data such as soil moisture, temperature,
humidity, and nutrient levels, providing continuous environmental and crop health insights. Traditional
monitoring systems struggle to cope with the variability and vast amount of sensor data, but adaptive ML models
are designed to adjust to changing environmental conditions, ensuring robust decision-making and predictions.
The experiment setup shows the implementation of linear regression and K-means Clustering applied separately
and in combined form later to give better results on the crop dataset. The combined approach gives 92% precision
and 90% accuracy and promises to monitor crops in a better manner.
Keywords
Wireless sensor network, Machine learning, Crop monitoring, linear regression, k-means clustering, precision
agriculture
1. Introduction
Agriculture is the mainstay of the Indian economy, where the livelihood of the majority of India's rural
population depends on farming. However, agricultural productivity is mainly impeded due to
unpredictable weather, pest attacks, and a lack of proper irrigation facilities. Most traditional methods of
monitoring crops consumed much time, and many failed to deliver real-time data for minor responses
against potential threats. Then again, the vastness of the agricultural environment of India cannot allow
successful manual monitoring in each portion of the field, hence data spacing remains.
The sustainable agriculture sector has several challenges facing it, which mitigate productivity, efficiency,
and sustainability. One major issue is resource wastage, particularly water, since there is a failure to
monitor in real time environmental conditions. Most farmers rely on subjective assessment or outdated
methods, thus over-irrigating and reducing the soil quality Moreover, the constant changes in weather
that are hard to predict due to climate change make it even harder to optimize the production consistently
in traditional agriculture.
AISD-2024: Second International Workshop on Artificial Intelligence: Empowering Sustainable Development, October 2, 2024,
co-located with the Second International Conference on Artificial Intelligence: Towards Sustainable Intelligence (AI4S-2024),
Virtual Event, Lucknow, India.
aneeshmaurya081@gmail.com (A.K. Mourya); nnagpal@lko.amity.edu (N. Nagpal); msrivastava@lko.amity.edu (M.
Srivastava)
0009-0002-8742-4297 (A.K. Mourya); 0000-0002-1741-861X (N. Nagpal) ; 0000-0002-5202-1183(M. Srivastava)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Labor is manual and decision-making takes up quite some time when it can also be made a part of
large-scale operation inefficiencies. Moreover, the lack of technology brings an inability of the farmers
to control the pest, nutrient, or plant health properly. Therefore, these constraints lead to a more precise
and efficient approach to farming being the necessity.
Among the various current technological advancements applicable to agriculture, precision
agriculture (PA) is the solution to these challenges since it can make the most efficient use of advanced
technologies to give the plants the greatest benefit. Precision Agriculture requires the use of data-driven
techniques in field monitoring and management of variability, which aids the optimization of resources,
such as water, and the best combination of fertilizers and pesticides. The system allows the farmers to
give the most suitable quantity at the right time and right space for the minimum loss and maximum
crops yield. Through the combination of solutions like remote sensing and data analytics, precision
agriculture holds the potential to decrease it by helping the farmers make the right decisions, the farms'
intervention and hence the productivity. This change from the usual methods to the more focused models
is the clear indication that there is a great need for either more precise, efficient or sustainable farming
methods.
Wireless Sensor Networks (WSNs), which are the network of spatially distributed sensors that collect
and transmit data from the field in real time, are the core component of precision agriculture. The sensors
that farmers use to monitor their crops are the ones that sense the critical environmental factors such as
temperature, soil moisture, humidity, and others. Farmers can easily access their crops remotely using
WSNs which provides a better understanding of the situation and results giving timely interventions.
The immediate feedback from the WSNs is indispensable equipment for both efficient resource allocation,
the maximization of the limited resources and the achievement of appropriate crop health.
Crop detection is no doubt the heart of smart agriculture, and wireless sensor network technology is
the main force in this process. Wireless sensor networks can collect moisture, heat, and wind information
from the field in real-time, which allows the farmer to know the exact locations of the field that need
more attention. The advantage of this is that the problem with water and pests in the field is specifically
addressed, rather than by universal treatment of the whole field. The resulting precise monitoring ensures
that the crops are provided with neither excessive nor insufficient moisture, adding only the necessary
nutrients and pesticides to the right places. Thus, the robustness of the crops is increased, and the
resources are more wisely utilized. Smart farming, when paired with WSN, results in higher crop yields
that have enhanced quality.
The use of Machine Learning (ML) in precision agriculture leads to the automation of data analysis
through WSNs. Through ML algorithms, patterns and trends that are hidden sometimes can be identified
in the data which let the farmers make the right decisions without having to do the manual data
treatment. Take, for instance, the merger of WSN and ML, which can be done to disassemble the content
on soil wetness information for the provision of remarking on the pressure irrigation units will undergo
soon. By telemetry of the agricultural data analysis, it succumbs to a progression to sparser and more
data-oriented agriculture where the decision-making process is stripped down and more accurate.
Automation of agricultural data analysis will enhance the ability of ML to predict crop health outcomes,
detect environmental changes, and identify potential hazards. This will result in more accurate
interventions, better resource utilization and hence a more productive agriculture.
The integration of WSN technology and machine learning (ML) into conventional agriculture has
managed to take up the traditional systems of farming to one that involves real-time control and
automatic decision making. Precision agriculture has been a solution to the traditional farming systems
which, in being efficient and sustainable as well as more productive, overcomes the limitations of older
methods. Farmers would be able to utilize resources more effectively, protect against diseases, and
increase their yields by means of WSN-based crop monitoring and ML-based analysis. Such an attentive
farming method is not a mere technique modernization; it is a required measure towards the immerging
agriculture sustainability in the future.
2. Literature Review
Conventional methods of monitoring find it difficult to handle the massive and widely spread agricultural
data, this is particularly the case in India where the landform is not uniform. Precision agriculture is the
method of farming where utilities and resources are wisely utilized and crop yields are enhanced with
the help of technology-drive insights, with the goal of improving productivity as well as the environment.
One of the breathtaking recent contributions to this area of science is the joining of Wireless Sensor
Networks (WSNs) with Adaptive Machine Learning (ML) Models for near-real-time crop monitoring. The
WSNs, the sum of the interconnected sensors that cover the fields, give a constant stream of details on
environmental factors like soil moisture, temperature, humidity, and nutrient levels. This regular data is
very necessary for understanding the crop health and the field conditions and therefore, it helps the
farmers to decide on the best way to irrigate, fertilize and fight pests.
Researchers throughout the world have been experimenting with providing fruitful results that would
encourage precision agriculture and revolutionize the way crops are managed, especially in resource-
constrained environments, by making farming more data-driven and responsive to changing conditions.
Table 1 given below throws light on various studies conducted by researchers describing the technology
being used and the key points seen with every study to achieve the goals.
Table 1
Studies in Different Research Papers
Technology Main
Title Strengths Weaknesses
Used Contribution
WSNs, Monitors water levels, Limited sensor
WSN Design for Provides real-time
ATMEGA8535 and humidity, and compatibility
Monitoring Farming environmental data,
ICS8817 BS temperature in farming requires skilled
Conditions [1] improves crop yield
Processors conditions management
Limited to
Zigbee-based Enhance data
controlled
Greenhouse acquisition and Reduces operational
Zigbee Technology environments,
Monitoring System processing for costs, saves energy
requires stable
[2] greenhouses
connectivity
WSN-Based
Optimizes irrigation High initial setup
Irrigation WSNs, Soil Efficient water usage,
schedules for water cost, energy
Management Moisture Sensors real-time data updates
conservation consumption issues
System [3]
Improved decision- Potential issues
Real-time Crop
making with real-time Vital for early pest and with sensor
Monitoring System WSNs
data on environmental disease detection durability in harsh
Based on WSN [4]
parameters environments
WSN and IoT Automates farm Data management
Integration for management to reduce Improves efficiency, and energy
WSNs, IoT
Agriculture labor and increase reduces manual effort efficiency
Automation [5] efficiency challenges
WSN for Pest and Reduces crop losses, Requires extensive
Early detection of
Disease Detection WSNs, Sensors provides accurate real- deployment for
diseases and pests
[6] time alerts scalability
IoT and WSN Limited to
Energy-saving and cost-
Integration for Energy-efficient, low greenhouses,
Zigbee, WSNs, IoT efficient greenhouse
Greenhouse operational cost affected by
management
Monitoring [7] connectivity issues
Application of Precision irrigation High power
Enhances crop health,
WSNs for Precision WSNs management for consumption, high
reduces water wastage
Agriculture [8] optimized water usage setup cost
WSN for Pest and Early detection of Deployment on
Reduces risk of crop
Disease Detection in WSNs diseases, improving crop large-scale farms is
failure, saves resources
Agriculture [9] yield costly
Overview of
Data privacy,
Review of IoT and productivity
Low labor cost, improves deployment
WSNs in IoT, WSNs enhancement through
resource management challenges in rural
Agriculture [10] real-time data and IoT
areas
integration
Early prediction of crop Complex
IoT-based Smart Effective in preventing
IoT, Machine diseases using IoT implementation,
Farming for Disease losses, accurate
Learning sensors and ML costly for small
Prediction [11] predictions
algorithms farmers
High energy
Automates crop
Smart Agriculture Reduces manual consumption
WSNs, IoT, monitoring and resource
using Deep Learning intervention, increases requires stable
Machine Learning management using
and IoT [12] productivity network
advanced ML
connectivity
Gong and others have researched using the WSN in combination with the BS processors ATMEGA8535
and ICS8817 to monitor environmental conditions like water, moisture, and humidity levels for
maximizing crop yields by providing farmers real-time data to make better decisions about watering and
pest management [1]. Researcher Kang and colleagues studied on how Zigbee technology improved the
efficient use of energy and reduction of cost in Gr$ environments with its real-time controls by
monitoring temperature, humidity, and other environmental parameters, which is especially
advantageous for big greenhouses [2].
Patel and others focused on the improvement of irrigation techniques with the help of WSNs
integrated with soil moisture sensors for the real-time measurement of moisture content, thus conserving
water resources and reducing the wastage of water in arid areas [3]. V. Patel, K. Patel, and K. Patel have
discussed the merits of the application of Wireless Sensor Networks (WSNs) in crop monitoring to enable
real-time monitoring for informed decision making and pest/disease detection by farmers [4].
The results by Chaudhary and others. on the integration of Wireless Sensor Networks (WSNs) and
the Internet of Things (IoT) which when applied in agriculture showed automatic control of the various
processes and by this lowering the labor costs, leading to the proper management of crops through the
provision of real-time data, were highlighted [5]. Kim and others. strongly suggests the use of WSNs to
detect crop pests and diseases as they are the best tool for the quick elimination of such threats from
thecropland which guarantees the harvest of the crops [6].
Liu and others. offered an exposition on the power of Zigbee technology in the association of WSNs
and IoT which leads to the monitoring of greenhouse environments that in turn optimizes the use of
energy and resources [7]. Sun and others. stressed the point about the coupling of deep learning
algorithms with IoT for the automation of the monitoring and resource management in agriculture that
ultimately enables us to steer the crops into the best direction of a sustainable world that depends on
ouractions [8].
Zhang and others used a case where IoT sensors and machine learning can reach to the predictive
side of the disease to illustrate the benefits of wireless systems and the internet [9]. Gupta and others
(2023) critically analyzed the convergence of IoT and WSNs and its impact on productivity and
resourcemanagement in agriculture, as well as discussing the issues of data privacy [10].
Verma and others (2023) mentioned smart farming and the successful case in forecasting of
diseases, where the use of IoT and machine learning helped farmers in being proactive despite the
requirement of high cost for smaller farms [11]. Wei and others (2023) discussed the application of deep
learning models and IoT technologies in automating agricultural resource management, which though
boosting efficiency, requires a lot of energy consumption and reliable connectivity.
3. Precision Agriculture: Sustainability Obtained Through Quality,
An optimal solution
A perfect example of precision agriculture that leads to sustainability could be noted as the one which
integrates high quality technologies as well as data-centric methods in order to create the right
combination of agricultural productivity and environmental stewardship. In the face of challenges like
climate change, resource depletion, and increasing food demand, traditional farming methods often fall
short in achieving sustainability. Precision agriculture addresses these challenges by integrating site-
specific crop management practices.
It minimizes resource wastage by delivering precise amounts of water, nutrients, and pesticides
to crops, ensuring minimal environmental disruption. WSN and ML combined allow for efficiency in the
management of resources in farming, which is at the same time more accurate, so it directly addresses
the problems of sustainable farming [13]. Table 2 describes how several elements of the research relate
to issues of sustainability.
Table 2
Sustainability Aspects
Aspect of Research Sustainability Contribution
Precision in Resource WSNs can be used by farmers when they are deployed to track environmental factors
Use like soil moisture, humidity, and temperature in real time. So, the use of the proper
amount of water and fertilizer is permitted in this way. The wasted vital resources
are reduced.
Reduced Environmental The ability to detect and address crop issues early minimizes the need for chemical
Impact treatments, which helps reduce the negative environmental effects of excessive
pesticide use.
Increased Crop Yield and This would allow for better management of the crops and informed decisions with
Food Security regards to them, because maximizing the yield per crop is important for food security
considering the current situation of climate change and lack of resources that all
regions are experiencing.
Energy Efficiency The use of automated ML algorithms to process and analyze data collected by WSNs
reduces the need for manual labor and frequent intervention, promoting more
energy-efficient farming practices.
Sustainable Water By employing soil moisture sensors to regulate irrigation schedules, the research
Management supports efficient water use, which is crucial in areas where water scarcity is a
growing concern.
Scalability and Low-cost The study aims to provide scalable, low-cost solutions that can be implemented by
Solutions for Small small and medium-scale farmers, promoting more inclusive and sustainable
Farmers agricultural practices.
4. Methodology
The study has been designed in a multi-step, starting from a field survey that has been conducted to
assess the needs of monitoring for an agricultural field. After the conduction of this part of the study,
sensors have been placed at points throughout the field for data acquisition purposes. These data are then
relayed to a central hub for storage and processing. The last phase is the analysis of this data using
machine-learning plan with the aim of achieving the skills that can practical applications in the
agriculture field.
Figure 1: Proposed Methodology
The various stages of the suggested methodology:
• Field Survey: The field condition must be assessed initially to find out which part of field
requires more monitoring.
• Sensor Placement: Best locations for placing wireless sensors are determined.
• Network Setup- Configuring the network for data collection. Apply shape like Star, Mesh or
Hybrid depending on the field size and shape.
• Data Collection & Transmission: Data using WSNs is gathered and sent to a central hub..
• Data Storage & Processing: Data is saved and processed. Use UBIDOTs like cloud-based
platform for centralized data storage and management.
• Data Analysis: The data is Analysis to support decision-making. By Implementing machine
learning algorithms such as Regression Analysis & K-Means Clustering to find out any changes
or patterns in the environment due to the temperature and humidity behaviors.
• Results: Final outcomes and actionable insights for field management.
Upon implementing the said methodology, data from wireless sensors are recorded and then machine
learning algorithms are applied to the dataset to draw appropriate required results.
5. Implementation
To implement the methodology, two key machine learning algorithms: Linear Regression and K-Means
Clustering are used. Linear Regression helps to figure out the relation between environmental factors like
temperature and humidity with time and K-Means Clustering groups data based on similarities in
temperature and humidity. These algorithms help to understand the environmental conditions for the
sample crop. The data used to generate Figures 2 (Linear Regression) and 3 (K-Means Clustering) is
sourced from Gaggle.com and WeatherAPI.com, which track key environmental variables like
temperature and humidity. This information is crucial to find out about the problem areas related to the
agricultural practices and at the same time give feedback.
6.1 Linear Regression
Linear Regression is a process of creating a model that describes the relationship that exists between a
dependent variable with an independent variable. The test setup takes humidity (%) as the dependent
variable while Days as the independent variable. As a result, a straight line formed, called the regression
line, which best fits to predict the dependent variable from the independent variable and output provides
two key metrics that evaluate the performance of the linear regression model
➢ 1- Mean Squared Error (MSE): Show the average squared difference between the actual and
predicted value of humidity.
➢ 2-R-squared (R²): R² shows how well model explains the changeability of the humidity data.
Figure 2 Linear regression model depicting the relationship between humidity and time over several
days.
The humidity is the dependent variable (Y-axis) whereas the time is the independent variable (X-axis).
The accuracy of the model is checked by using such metrics as Mean Squared Error (MSE), which shows
average squared difference between the predicted humidity and the actual observations, and R-squared.
(R²), which is a measure of how good the model fits the real data.
Tools used: The visualization was plotted using Python's Matplotlib library (version 3.9.0).
The value of R² runs from 0 to 1, where the higher the value, the more of the variability it explains.
In this case, a negative R² indicates that the model does not form a horizontal line in the mean of data
mapped against the humidity values (Refer fig 2).
6.2 K-Means Clustering:
K-means-clustering is a type of machine learning algorithm that tries to categorize data into a given
number of clusters according to their similarity.
Figure 3 K-Means Clustering Analysis classifying temperature and humidity data into clusters that may
reveal different environmental trends.
The cluster 0 in blue shows Cooler with moderate humidity. Cluster 1 in Orange shows Warmer, but
less humid while cluster 2 depicts moderate temperature and humidity in green color. The center values
in each cluster, also called Centroids, are shown in red color.
Tools Used: The clusters are plotted similarly to Figure 2, using `Matplotlib` (version 3.9.0).
Application: It will cluster this data into a pattern, like temperature and humidity conditions together.
This information will be very useful for farmers or researchers to make informed decisions based on the
environment (Refer figure 3).
6.3 Combined Approach
A combined approach utilizes both Linear Regression and K-Means Clustering together to
produce improved prediction results which will encompass specific crop management advice.
The clusters can also be correlated with the regression line to show whether some clusters are
susceptible to changes in the humidity over time, which would be very helpful in guiding decisions on
planting, irrigation, or applying fertilizers.
Figure 4: Combined Linear Regression and K-Means Clustering model illustrating the environmental
conditions over time.
There is a red line representing the linear regression line and it indicates that a negative linear
relationship may exist between the number of days. Humidity will tend to drop as more days emerge (See
figure 4).
Figure 5: Trend of humidity over time, highlighting the variance in actual humidity values.
According to the graph, the humidity decreases regularly with the time passing, but there is a
certain degree of variance in the actual humidity, The straight line here is a very fair indication of the
entire data set's trend (See figure 5).
Figure 6: Downward trend in humidity observed over the course of the study period.
This graph shows that there has been a downward trend in the humidity over time (Refer fig 6).
6. Analysis & Result
This section shows the performance of the machine learning models implemented. Evaluation of the
Linear Regression model includes Mean Squared Error (MSE) and R-squared (R²), whereas the evaluation
for the K-Means Clustering model is based on Precision, Recall, and F1-Score. Additionally, it describes
the benefits derived from the combination of these two models in improving the accuracy and reliability
of the predictions that would be made to derive critical decisions towards agriculture.
Linear Regression Coefficient- These are the numbers used to predict dependent variables.
Table 3
Linear Regression Result
Intercept -0.08052024 It intercepts the point where regression line crosses
the Y-axis
Coefficient 1 0.49577119 Shows effect of temperature
Coefficient 2 0.498019014 Shows the influence of other factors
Figure 7: Classification Report For K-Means Clustering.
Table 4
Classification Report for K-Mean Clustering
Precision Recall F1-Score Support
Cluster 0 1.00 1.00 1.00 6
Cluster 1 1.00 1.00 1.00 10
Cluster 2 1.00 1.00 1.00 14
Accuracy 1.00 30
Macro avg 1.00 1.00 1.00 30
Weighted avg 1.00 1.00 1.00 30
6.1 K-Means Clustering Model
All three metrics are perfect (1.00) across all clusters (0, 1 and 2) that means cluster was correctly classified
without any positives (precision) and all those points belong to a cluster successfully identified and F1-
Score of 1 for all clusters shows good balance between Precision and Recall. Accuracy 1 showing perfect
performance (Refer figure 7 and Table 4).
Figure 8- Classification Report for Combined Model
6.2 Combined Model
Cluster 0 precision 0.50, recall 1.00 and f1-score 0.70 showing perfect recall but some misclassification.
Cluster 2 has perfect 3-matrix result =1 depicting flawless classification. Cluster 2 precision 1.00, recall
0.75, f1-score 0.86 showing perfect precision but slightly change in recall and f1-score. It achieves an
accuracy of 90%, showing strong performance as seen in figure 8 and table 5.
Table 5
Classification Report for Combined Model
Precision Recall F1-Score Support
Cluster 0 0.50 1.00 0.7 1
Cluster 1 1.00 1.00 1.00 1
Cluster 2 1.00 0.75 0.86 4
Accuracy 0.90 6
Macro avg 0.82 0.92 0.84 6
Weighted avg 0.92 0.82 0.85 6
Some key points witnessed during the study are:
A: The coefficient shows that both temperature and other factors like moisture positively affect
the humidity level with time and there is always a tradeoff while integrating the two methods.
B: Combining linear regression with cluster classification approach allows for better
understanding of environmental conditions with time and identify similar patterns and characteristics.
C: The model can be useful in identifying and managing specific crop-growing conditions.
D: By integrating both algorithms together and implementing the combined approach offers
better understanding of the environmental factors for decision making during crop monitoring.
6.3 Comparison with Other Studies
The metrics like precision and accuracy achieved in this study are compared to other research using
similar techniques. The proposed approach using the combined approach showed the highest precision
value of 92% and an accuracy of 90% in predicting the environmental conditions for the crop.
Table 6
Comparison of different Machine Learning Approaches in WSN Applications
Study ML Approach WSN Precision Accuracy Notes
Usage
This Linear Regression Combined approach
paper + K-Means Yes 92% 90% improves predictions for
environmental conditions
Paper 1 SVM, Decision Focuses on optimizing
[13] Trees Yes 88% 85% irrigation scheduling using
ML
Paper 2 Random Forest, Improves yield prediction
[14] Neural Networks Yes 90% 87% and pest detection through
multi-sensor WSN
Paper 3 SVM + K-Nearest Uses a combination of ML
[15] Neighbors (KNN) Yes 85% 80% models for disease
detection, but shows lower
results than this paper
Figure 9- Bar chart comparing the precision and accuracy of various machine learning approaches used
in Wireless Sensor Networks (WSN) across different studies
7. Conclusion
The integration of WSN and ML has opened new vistas in precision agriculture. It has shown a
remarkable enhancement in crop monitoring by the proposed methodology using a combination of Linear
Regression and K-Means clustering with the outcome of 92% precision and 90% Accuracy. This fuses the
technologies for real-time collection and analysis, hence enabling timely and highly informed decisions.
With this system, all the processes would be automated and hence reduce the manual burden of farmers.
This will ultimately lead to better agricultural productivity and sustainability. The adaptability of the
proposed methodology in different crops and various agricultural conditions should be improved in
future work. Being that this approach is specified for factors such as temperature and humidity, its
application would need extension to several crops with different requirements. Adding to these, the
fluctuating weather conditions into the framework of precision farming would be highly required.
Finally, the integration of Deep Learning techniques will enhance this system's ability in removing much
complexity in dataset processing and hence attaining higher degrees of accuracy in prediction.
Developing these aspects, the model becomes a very flexible and robust tool that will enable even superior
realizable agricultural practices in very diverse environments.
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