Exploring IoT and Machine Learning Integration for Soil Nutrients Monitoring and Crop Recommendation: A Survey

Computer Science & Engineering: An International Journal (CSEIJ), Vol 15, No 1, February 2025
DOI:10.5121/cseij.2025.15101 1
EXPLORING IOT AND MACHINE LEARNING
INTEGRATION FOR SOIL NUTRIENTS
MONITORING AND CROP
RECOMMENDATION: A SURVEY
Vijaya R. Khemnar, Mininath R. Bendre
Department of Computer Engineering, Pravara Rural Engineering College,
Loni, India
ABSTRACT
The integration of Internet of Things (IoT) and machine learning (ML) technology has the
capacity to transform agriculture via the improvement of precision agricultural methods.
This survey paper explores the integration of IoT and ML in the field of soil nutrient
monitoring and crop recommendation systems. In the context of crop recommendation
systems and soil nutrient monitoring, this paper explores the integration of IoT and ML. Its
goal is to provide a thorough overview of the most recent developments, applications,
challenges, and potential future directions. IoT sensors collect and give immediate soil
health information, while ML algorithms analyses this data to suggest nutrient deficits and
recommend the most suitable crops. The paper reviews the progress made in sensor
technology and machine learning applications, highlighting the advantages they provide to
precision farming. This integration promises to enhance agricultural productivity and
sustainability through datadriven decision-making and resource optimization. At the end
research gaps are discussed, along with future research directions.
KEYWORDS
Precision Agriculture, IoT Sensors, Machine Learning, Soil Nutrient Monitoring, Crop
Recommendation Systems.
1. INTRODUCTION
The agricultural field is the most prominent and is undergoing fast transformation, driven by
technology innovations that seek to improve production and sustainability. With the ongoing
increase in the world population, there is a growing need for agricultural techniques that are both
efficient and sustainable. Conventional approaches of monitoring soil nutrient levels and
managing crops frequently fail to adequately meet the complex and ever-changing requirements
of contemporary agriculture. To address this discrepancy, the advancements of IoT and ML
presents a transformative method. One of the notable breakthroughs is the integration of IoT with
ML technology, which is considered as a groundbreaking approach. The IoT facilitates the
gathering of data in realtime by using a network of sensors that observe different soil
characteristics, such as moisture levels, pH levels, temperature, and nutrient concentrations.
Continuous monitoring enables farmers to promptly get insights into soil health, allowing for
quick and accurate actions.

2
Machine learning enhances IoT by providing robust tools for data analysis and predictive
modelling. ML algorithms can analyse the extensive data produced by IoT sensors to recognise
trends, identify deviations, and provide precise forecasts on soil nutrient deficits and the most
suitable crop selections. Machine learning algorithms have the ability to analyse large volumes
of data produced by IoT sensors, revealing hidden patterns and trends that may not be
immediately obvious.
By using predictive analytics and pattern recognition, ML provides a practical and useful
information, such as the most effective schedules for fertilisation, methods for crop rotation,
and early alerts for possible soil deficiencies. The integration of IoT and ML improves the
accuracy of soil nutrient management and facilitates the creation of customised crop
recommendations. The integration of IoT with ML has the capacity to transform soil nutrient
management and crop recommendation systems, resulting in enhanced decision-making,
optimised resource utilisation, and refined agricultural production.
The aim of the presented survey paper is to provide a thorough and comprehensive examination
of the present status of research and development in this particular sector. This paper analyses
the structure and operation of integrated IoT and ML systems. It explores the challenges and
potential future scope in the field of precision agriculture.
Rest of the paper is organised as follows: Section II discusses about the background and related
work to IoT and ML in agriculture sector. Section III presents the identified research gaps.
Section IV describes the future direction for further investigation in precision agriculture by
highlighting the scope of integrated IoT and ML technologies together. Section V provides the
conclusion with key findings from presented literature survey.
2. LITERATURE REVIEW
In recent years, there has been significant study on the combination of IoT and ML for the
purpose of monitoring soil nutrients and providing crop recommendations. This literature
review examines prominent research and advancement in this multidisciplinary domain,
emphasising notable contributions, approaches, and discoveries.
2.1. IoT-Based Soil Monitoring Systems
IoT-based systems for soil monitoring have gained traction due to the ability to provide real-
time data. Researchers such as [1][2] have developed advanced sensor networks to measure soil
moisture, temperature, pH, and nutrient levels. Their systems utilize wireless sensor networks
(WSNs) and communication protocols to transmit data to centralized platforms. In [3] authors
continuously measured variables at the agro-field. The implemented work provides the
development and testing of IoT based smartphones platform with IoT, DSS and Real-time data.
Authors in [4] implemented the automated remote field monitoring system and provides the
visualization in the cloud with LoRaWAN and Real-time data. [5] introduced a low-cost,
energy-efficient IoT sensor system for precision agriculture, which significantly improved the
accuracy of soil condition assessments.
2.2. Machine Learning for Data Analysis
Machine Learning techniques have been applied to analyse the vast amounts of data generated
by IoT sensors. Studies in [6] proposes the Soil Classification based on micronutrients. Soil
classification result in accuracy of 94 % with ELM and Private dataset. In [7] explored the crop

3
recommendation for predictive accuracy of soil health and crop yield forecasts. Their work
highlighted the potential of ML to uncover complex relationships between soil parameters and
crop performance. Crop recommendation were predicted in accuracy of 98.22 % with MLP
and Kaggle dataset. Authors in [8] implemented the system to aid the farmer in developing a
platform that provides recommendations for crops. Results shows that crop recommendation
by Random forest achieved 97.18 % accuracy with RF and Kaggle dataset.
2.3. Integration Challenges and Solutions
Integrating IoT and ML in agriculture involves several challenges, including data management,
system interoperability, and scalability. Authors in [9], proposed the solution to provide
consistent and up-to-date agricultural data and field updates to assist farmers. The proposed
mode achieved the accuracy of 97.3 % with MSVMDAG-FFO and own dataset. In [10],
discussed the implementation using RFC and one's own dataset, crop recommendation
achieved 99% accuracy. Also discussed the novel approaches to improve data accuracy and
consistency. In [11], worked on crop selection with SCS and dataset from Pakistan achieved
accuracy 97.4 %. Along with that addressed issues of system integration and interoperability,
recommending standardized protocols and frameworks for seamless communication between
IoT devices and ML models. In [12], authors has a system with an Arduino board, an Amazon
Web Service, and a set of sensors. To analyse and visualise data, they made use of mobile
applications, cloud computing, and machine learning techniques. This system makes it possible
to ascertain the minerals that are present in the soil and prescribe the appropriate amount of
fertiliser. Crop recommendation and soil nutrient monitoring are made more comprehensive by
the combination of IoT and ML technology[13-21].
Fig. 1:Methodology framework for soil nutrients monitoring and crop recommendation using an
advanced ML-Enabled IoT system [1].
Figure 1, shows framework having the integration of ML and IoT. The suggested framework in
[1] uses three sensors—JXBS-3001, FC-28, and DHT11—to measure soil nutrient
concentrations (NPK), moisture, humidity, and temperature in the agricultural field. The
NodeMCU sends gathered data to a remote server via MQTT for processing. These sensors
give real-time data to the suggested machine learning algorithm for using data to recommend
planting schedules, crop kinds, and fertilisation strategies. This helps farmers choose crops and
boost output. In addition, soil monitoring assures the health and safety of harvested product,
and implanted sensors analyse food nutritional content, pesticide residues, and hazardous

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pollutants. The framework's User-Friendly Mobile App lets farmers remotely monitor and
manage their agricultural activities with seamless device and data access.
Table 1 shows a brief review provided on the related work for soil nutrients monitoring and
crop recommendation with the Machine learning enabled IoT system.
3. RESEARCH GAPS
After extensive literature survey the following gaps are identified in soil nutrients monitoring and crop
recommendation:
• Labor-intensive and subjective soil nutrient analysis:
Conventional techniques for analysing soil nutrients and recommending crops frequently depend
on labour-intensive, subjective evaluations.
• Inefficient resource utilization:
Ineffective resource management and fertiliser application choices can result in lower yields and
inefficient use of resources.
Table 1: Summary of Existing Literature Survey
Sr.
No.
Authors Year Problem Statement ML-IoT
Enabled
Solution
1 Md Reazul
Islam et al [1]
2023 The integration of
IoT and machine
learning algorithms
to monitor soil
nutrients and
recommend crops.
Yes Innovative ML-enabled
monitoring and
recommendation system
with IoT device and
Real-time data.
2 S.J. Ramson et
al [2]
2021 Soil health monitoring
in UpToDate manner.
No Development and
testing of a low cost
(5667 USD) monitoring
system with IoT and
Real-time data.
3 S.V. Gaikwad et
al [3]
2021 Continuously
measures variables
at the agrofield.
No Develop and test the
smartphones platform
with
IoT, DSS and Real-time
data.
4 B.M. Zerihun et
al [4]
2022 A system that
monitors fields
remotely using
automation.
No Visualization in the cloud
with LoRaWAN and
Realtime data.
5 V. Shukla et al
[5]
2022 Soil monitoring
system.
No Identify soil type
effectively and use IoT
and real-time data to
visually represent related
information.

5
Sr.
No.
Authors Year Problem Statement ML-IoT
Enabled
Solution
6 T. Blesslin
Sheeba et al [6]
2022 Soil Classification
based on
micronutrients.
Yes Soil classification
result in accuracy of 94
% with ELM and Private
dataset.
7 K.
Bakthavatchalam
et al [7]
2022 Crop
recommendation.
Yes Crop recommendation
were predicted in
accuracy of 98.22 % with
MLP and Kaggle dataset.
8 Thilakarathne
NN et al [8]
2022 To create a platform
for crop
recommendations in
order to help farmers.
No Crop recommendation by
Random forest achieved
97.18 % accuracy with
RF and Kaggle dataset.
9 Senapaty MK et
al [9]
2023 To regularly provide
farmers with up-to-
date crop and field
information.
Yes The proposed mode
achieved the accuracy of
97.3 % with
MSVMDAG-FFO and
own
dataset.
10 S. Sundaresan et
al [10]
2023 Crop
Recommendation
system.
Yes Using RFC and one's own
dataset, crop
recommendation
achieved 99% accuracy.
11 A. Ikram et
al [11]
2022 Correct Selection of
Crop.
Yes Crop selection with SCS
and dataset from Pakistan
achieved accuracy 97.4 %
• Lack of real-time monitoring and decision-making:
Conventional farming methods frequently lack the ability to monitor and make decisions in real time.
• Limited access to crop quality assessment:
Information concerning the quality of the crops that consumers eat is frequently not readily available to
them.
• Fragmented solutions and high costs:
High costs and fragmented solutions are common problems with current IoT-based soil monitoring
systems.
4. FUTURE DIRECTION
• Enhanced Sensor Technology: The creation of more sophisticated, affordable sensors with
increased precision and extended battery life will enhance realtime data gathering and
minimize upkeep needs.

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• Data Integration and Management: Enhancing techniques for merging data from various
IoT devices and making sure that data management systems are strong will improve the
accuracy and breadth of the information gathered.
• Advanced Machine Learning Algorithms: More advanced machine learning (ML)
algorithm research, such as deep learning and reinforcement learning, may increase
prediction accuracy and yield more useful information for crop recommendations and soil
management.
• Edge Computing: Especially in remote agricultural areas, integrating edge computing into
local data processing on IoT devices can improve real-time analytics, lower latency, and
increase system responsiveness.
• Interoperability Standards: Standardized frameworks and protocols for ML models and
IoT devices will enable smooth communication and integration between various platforms
and systems.
• Explainability and Transparency: Improving ML models' interpretability will help
farmers make better decisions by making results easier to comprehend and apply.
• Scalability and Cost-Effectiveness: Small and medium-sized farms will be able to afford
more sophisticated IoT-ML systems thanks to research into scalable and economical
solutions, which will encourage wider adoption.
• Integration with Recent Technologies: To further improve the capabilities and uses of IoT-
ML systems in agriculture, new technologies like blockchain for data security and
augmented reality (AR) for visualization should be investigated.
5. CONCLUSION
The combination of IoT and Machine Learning is a significant development in the field of soil
nutrient monitoring and crop recommendation. Through the utilization of real-time data
collection using IoT technology and advanced analytics through ML, the collaboration provides
substantial enhancements in precision agriculture, specifically in the areas of soil management
and crop yield. Despite the persistent obstacles of data integration, system interoperability, and
algorithmic complexity, continuous research and technological advancements offer hope for
conquering these challenges. Developments in sensor technology, data management, and
machine learning algorithms will enhance agricultural practices, promoting sustainability and
efficiency in precision farming.
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