| Journal of Smart Sensors and Computing
Received: 28 September 2025; Revised: 29 December 2025; Accepted: 30 December 2025; Published Online: 30 December 2025.
J. Smart Sens. Comput., 2025, 1(3), 25214 | Volume 1 Issue 3 (December 2025) | DOI: https://doi.org/10.64189/ssc.25214
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
Internet of Things and Machine Learning in Smart
Agriculture: A Comprehensive Review
Sushilkumar Salve,
*
Prathamesh Dhotre, Krishna Sathe and Vaibhav Salegaye
Department of Electronics and Telecommunications Engineering, Sinhgad Institute of Technology, Lonavala, Maharashtra, 410401,
India
*Email: sushil.472@gmail.com (Sushilkumar S. Salve)
Abstract
Traditional farming practices in developing nations often face inefficiencies due to limited access to real-time
information on soil health, weather conditions, and crop growth, resulting in reduced productivity and resource
wastage. This review article summarizes smart agriculture systems that integrate the Internet of Things (IoT)
and Machine Learning (ML) to enhance crop monitoring, optimize resource utilization, and support sustainable
farming practices. IoT-based wireless sensor networks (WSNs) enable continuous real-time data collection on
environmental and soil parameters, while ML algorithms analyze this data to support informed decision-
making. The experimental results demonstrate that the proposed ensemble-based ML model achieves high
predictive accuracy, validating the effectiveness of combining multiple learning algorithms for smart agriculture
applications. Furthermore, real-time data updates allow farmers to respond promptly to changing field
conditions, thereby minimizing losses and improving overall productivity. The integration of IoT and ML
establishes a robust, data-driven agricultural framework that enhances efficiency, sustainability, and food
security.
Keywords: Internet of Things; Machine learning; Precision farming; Soil monitoring.
1. Introduction
One measure of a country's economic growth is the state of its agriculture.
[1]
Agriculture remains the backbone
of many economies, providing food, employment, and raw materials for industries. However, traditional farming
practices often face challenges such as unpredictable weather, inefficient resource use, and pest infestations. In
recent years, the integration of Internet of Things (IoT) technologies has revolutionized the agricultural sector
by enabling real-time monitoring of soil conditions, crop health, and environmental factors.
[2]
Furthermore,
Machine Learning (ML) enhances these systems by analyzing collected data to predict outcomes such as yield
estimation, irrigation needs, and disease outbreaks, leading to smarter and more sustainable farming
practices.
[3]
While offering fewer environmental dangers, IoT in agriculture enhances farm management, lowers
waste, and boosts agricultural yields.
[4]
Innovations like cloud computing, radio frequency identification tags,
communication between machines, sensor networks using wireless technology, and data analysis are the main
reasons why our food production process is changing.
[5]
IoT is growing in popularity and operates in real time.
[6,7]
By planning, gathering, identifying, and applying big data and artificial intelligence to manage systems for
services, IoT technology is developing.
[8-10]
Conventional farming techniques often rely on manual observation
and experience-based decision making, which can result in inconsistent crop yields and resource wastage.
Farmers lack timely, data driven insights into soil moisture, nutrient levels, and pest risks. There is a pressing
need for an automated system that can collect, process, and analyze agricultural data to support intelligent
decision-making. To improve agricultural productivity, machine learning (ML) techniques are used to analyze
such data.
[11]
By transforming unprocessed agricultural data into useful knowledge, machine learning (ML) in
smart agriculture increases productivity, lowers expenses, and guarantees sustainability. It is a major facilitator
of contemporary precision farming since it facilitates automation, resource optimization, and predictive
analytics.
[12]
Additionally, combining machine learning and data analysis methods expands crop prediction's
potential.
[13]
Large datasets are processed effectively by these algorithms, which also adjust to changing
circumstances to continuously increase forecast accuracy. In this regard, machine learning becomes an effective
instrument for combining multi-dimensional data sources, including weather information, satellite imagery,
and assessments of soil condition.
[14]
The confluence of IoT-enabling algorithms for learning in agriculture
represents an evolutionary step toward precision farming, enabling immediate tracking, accurate forecasting,
and sustainable resource usage.
[15]
This study focuses on the development of a prototype smart agriculture
system designed for small- to medium-sized farms. The IoT network is limited to sensors measuring
temperature, humidity, and soil moisture. Machine learning models are trained using sample datasets and tested
under controlled conditions.
[16,17]
1.1 Importance of agriculture in global economy and food security
In many growing and developing nations, agriculture is a vital industry that frequently accounts for 1530% of
GDP. In addition to providing livelihoods and trade, agriculture is the cornerstone of worldwide food security,
ensuring that there will always be an adequate supply of wholesome food available to present and future.
[18]
Agriculture is one of the main drivers of economic stability and growth, employing over 65% of the working
population worldwide, according to the World Bank. While it supports sectors like food processing, textiles, and
trade in developed countries, agriculture continues to be a major source of revenue and a crucial sector for
reducing poverty in developing countries.
[19]
1.2 Role of IoT in precision farming and productivity improvement
Increasing crop yield and creating an intelligent cropping system are the goals of precision farming. Precision
farming is the intelligent use of agricultural resources and information using communication and sensing
technologies to maximize financial return and production.
[20]
Wireless sensor networks and precision farming
transform the agricultural industry into a technological path for increasing agricultural output with the least
amount of human labor. The utilization of sensor networks that are wireless in precision agriculture will provide
farmers with a multitude of information, such as energy harvesting techniques, wireless communication
technologies, and the hierarchy of energy efficiency.
[21]
AIML enables precision farming by enabling farmers to
make information-driven choices to reduce waste through real-time weather, crop, and soil monitoring.
information about soil, weather, and crops.
[22]
Recent developments in systems for irrigation have introduced
agricultural irrigation instruments, motion manipulation, satellite devices, imaging technologies, and wireless
connections, which track both environmental and soil conditions and assess irrigation parameters, like flow and
pressure, to improve farm water utilization efficiency.
[23]
1.3 Need for ML to analyse sensor data and predict outcomes
Machine learning, which may be applied in agriculture to aid in identification of diseases, crop monitoring, and
decision-making, is a key component of intelligent farming.
[24]
These smart devices intelligently move the
collected data to designated storage places.
[25,26]
A controller can understand the electrical signals that these
sensors convert from physical quantities with the help of machine learning.
[27]
To bridge the gap, sensor-driven
agriculture needs machine learning (ML) to transform raw data into information that can be put to use. In
agricultural data, machine learning finds hidden patterns, correlations, and anomalies.
[28]
Consequently,
combining machine learning with sensor-driven agriculture turns unprocessed data into useful insights that
allow for reliable forecasting that raises revenues, lowers expenditures, and supports sustainable farming
methods.
[29]
1.4 Research gaps in existing smart agriculture systems
Effective integration and evaluation of agricultural data produced by various Internet of Things devices,
satellites, drones, and weather stations is difficult due to their lack of standardization. Even though machine
learning algorithms are used to schedule irrigation, detect illnesses, and predict production, their performance
can occasionally be reduced by noisy, imbalanced, or incomplete records.
[30]
The scalability of smart agricultural
systems in large, diverse, and resource-constrained farming contexts is rarely demonstrated, despite the fact
that many of them are tested on prototype or small-scale companies.
[31]
The spread of connected farming tools
increases the risk of cyberattacks and the inappropriate use of private agricultural data, an understudied
problem.
[32]
In order to create smart farming methods that are more effective, scalable, and able to guarantee a
reliable food supply in the face of changing environmental and socioeconomic challenges, it is crucial to identify
and close such research gaps.
[33]
Fig. 1 illustrates the problems and advancements in global food security by
highlighting the relationship between the world's growing population, rising food supplies and crop prices, and
the main crops that contribute to global food energy.
[34]
Title
Year
Application
Method
Key Finding
Machine Learning
for Smart
Irrigation
2022
Optimized irrigation
scheduling
ML models (Random
Forest, Neural
Networks)
predicted irrigation needs reduced
water usage by 25% without affecting
yield.
Smart Agriculture
Using IoT and ML
2021
Crop monitoring and
yield prediction
IoT sensors + Machine
Learning models (SVM,
RF)
Real-time monitoring improves yield
prediction accuracy; reduced resource
wastage.
IoT-based
Precision Farming
System
2020
Soil moisture and
temperature
monitoring
IoT sensors + Data
Analytics
Automated irrigation based on sensor
data increased water efficiency by
30%.
Deep Learning for
Plant Disease
Detection
2019
Disease identification
Convolutional Neural
Networks (CNN)
CNN models achieved 95% accuracy in
detecting common crop diseases from
leaf images.
Table 2: IoT devices in agriculture.
IoT Device
Parameter Measured
Connectivity
Application
Soil Moisture Sensor
Soil water content
LoRa, Zigbee, Wi-Fi
Smart irrigation and water
resource management
Temperature & Humidity
Sensor (DHT11/DHT22)
Air temperature and
relative humidity
Wi-Fi, Zigbee, Bluetooth
Monitoring micro-climate
for crop growth
pH Sensor
Soil pH levels
Wi-Fi, LoRa
Soil quality assessment
and fertilizer planning
NPK Sensor
Nitrogen, Phosphorus,
Potassium content
Wi-Fi, GSM
Precision fertilization and
soil nutrient balance
Fig. 4: IoT architecture for smart farming.
Model
Input Features
Dataset Used
Accuracy (%)
Pros
Cons
Support Vector
Machine (SVM)
Leaf images, soil
properties,
weather data
Plant Village,
UCI datasets
8592
Works well with
small datasets,
good for
classification
Struggles with
large datasets,
tuning is complex
Random Forest
(RF)
Soil nutrients,
temperature,
rainfall, crop yield
records
Kaggle crop
yield dataset,
regional
agricultural data
8894
Handles noisy
data, less
overfitting
Less interpretable,
slower with very
large data
k-Nearest
Neighbours (k-
NN)
Crop disease
images, sensor
data
Plant Village,
field survey data
8087
Simple, effective
for small datasets
Computationally
expensive,
sensitive to noise
Artificial Neural
Network (ANN)
Weather, soil
moisture, yield
history
Custom farm
datasets, FAO
data
9095
Learns complex
relationships,
adaptable
Requires large
-
Convolutional
Neural Network
(CNN)
Crop/leaf images
for disease
detection
Plan Village
(50k images)
9599
High accuracy in
image recognition,
automates feature
extraction
Needs large label
datasets, high
computational
power
Application
Method
Features and Benefits
Precision farming resource
waste
IoT sensors track plant-level data; ML gives
targeted input (fertilizer, pesticide)
Boosts yield, reduce
Yield Prediction
IoT stations collect real-time local weather
data; ML improves forecast accuracy
Helps in planning
Weather Forecasting
IoT wearables track animal health; ML
detects illness or stress patterns.
Better planning for planting
Livestock Monitoring
IoT cameras scan fields; ML identifies and
locates weeds reduces herbicide use
Improves animal health
Weed Detection
IoT tracks harvest, storage, and transport;
ML predicts demand and spoilage risk
Enables targeted weeding
Table 5: Applications of IoT and ML in agriculture.
Challenges
Solutions
Unpredictable weather
Water scarcity
crops need
Crop diseases and pests
collected by IoT devices
Overuse of fertilizers and pesticides
amounts to use
Low yield or productivity
harvesting.
etc
Fig. 7:
7. Challenges and limitations
7.1 Technical
Connectivity is one of the major challenges in implementing IoT in agriculture, as reliable system performance
depends on stable internet access, particularly in remote rural areas where network infrastructure is often
weak.
[55]
This makes it hard to send data and check on things in real time.
[56]
To fix this, building better internet
infrastructure in those areas can help create a system that reliably sends and receives data.
[57]
Security and privacy of data: Data security becomes increasingly crucial as more is gathered. Data that has been
compromised may be taken, altered, or distributed without authorization. Farmers may suffer financial losses,
reputational harm, and even legal problems as a result of this.
[58]
To prevent this, the risk can be decreased by
implementing measures such data encryption, establishing stringent access controls, and maintaining
security.
[59]
Interoperability and standardization: It can be hard to make sure different IoT devices and systems from
different companies work together without problems. For this to work, the industry needs to agree on common
standards and work together.
[60]
Scalability: By automating processes that would normally need human labour, IoT in agribusiness helps
decrease waste, minimize environmental impact, and save time and money.
[61]
7.2 Environmental
Adoption and awareness: Getting farmers to use IoT can be tough. Some are hesitant to invest in new tech
decide. So, creating programs that teach farmers about the benefits of IoT and help them overcome their doubts
is important.
[62]
Smart Grids, Microgrids, and Renewable Energy: Due of sensors, navigational systems, and data
transport, smart farming requires a lot of electricity. Using alternative sources of energy helps address
persistent power problems in rural places. Local energy systems include microgrids and smart grids.
Additionally, new energy storage systems may store heat and power, increasing the efficiency of energy
consumption.
[63]
Environmental and Sustainability Concerns: IoT devices that run on batteries need to be
replaced or charged often, which costs money and creates a lot of electronic waste. Solar-powered IoT devices
are being developed to help, but they are still expensive
broken or faulty sensors need to be replaced, which adds to costs and e-waste. Making IoT devices more durable,
weather resistant, and recyclable is important to reduce their environmental impact.
[64]
Technology
Accessibility: Using cutting-edge technology might be challenging in some areas due to a lack of new tools and
reliable internet. Farmers find it challenging to employ intelligent agricultural practices as a result.
[65]
For these
techniques to gather and exchange sensor data, high quality equipment and a fast internet connection are
required. Farmers in areas without these find it more difficult to take advantage of these advancements as they
are unable to properly utilize the newest farming equipment.
[66]
Regulatory Compliance: Farmers using
precision farming have to follow many rules set by national and municipal governments regarding
environmental protection, land management, and data use. These rules can make things more complex and
expensive.
[67]
Sticking to all the rules about data, how land is used, and how the environment is treated can be a
big challenge, adding more time and cost for farmers using precision farming. It helps maintain transparency
by recording farming practices, fertilizer use, and pesticide applications in line with regulations. Compliance
also builds trust among consumers and policymakers, supporting sustainable and legal agricultural practice.
[68]
Table 6 summarizes the key challenges associated with smart agriculture and the solutions recommended in
the literature.
Table 6: Challenges vs solutions in smart agriculture.
Challenges
Solutions
Unpredictable weather
Water scarcity
crops need
Crop diseases and pests
collected by IoT devices
7.3 Social/economic
Farmers may benefit from using Technology in farming by producing higher-quality, more transparent, and
sustainable goods.
[69]
This makes companies stand out from the competition, satisfies consumer demand for
environmentally friendly items, and raises the price at which they sell their goods. Adopting IoT gives farmers
a significant opportunity to increase revenue and set their goods apart in a competitive market, as consumers'
concerns about environmentalism and transparency grow.
[70,71]
IoT helps farmers save money by managing
resources better and using predictive maintenance.
[72]
With real-time data, they can run their farms more
efficiently, cut down on waste, and boost their profits over time.
[73]
8. Emerging technologies in smart agriculture
8.1 Edge AI and federated learning for local farm data analysis
Drones are increasingly being used in farming as IoT and connectivity technologies advance.
[74]
Drone abilities
in agriculture could be greatly improved in the future by AI. AI may be used by drones to help with tasks
including agricultural inspection, water management, crop health monitoring, planting, crop spraying, and soil
analysis.
[75]
Tracking agricultural conditions is made easier by drones fitted with a variety of sensors, including
ordinary cameras, thermal photos, 3D images, and multispectral photography. Disease detection, plant density
measurement, and soil health monitoring.
[76]
8.2 5G/6G for real-time farm monitoring
In order to influence the direction of agriculture in the future, it is crucial to encourage global collaboration and
the open exchange of data in the area of precision farming.
[77]
The goal of this cooperative is to create a
comprehensive yet accurate knowledge database that will provide agricultural decision-makers with a wealth
of helpful information.
[5]
Through international collaboration, farmers may gather data from diverse regions.
Combining this worldwide expertise yields a useful resource for enhancing precision farming techniques,
honing forecasts, and implementing sustainable practices over a wider region. This ultimately supports the
global goal of ensuring food security and promoting agriculture that is better for the environment.
[78]
8.3 AI enabled drones for precision spraying
Drones are increasingly being used in farming as IoT and connectivity technologies advance. Drone abilities in
agriculture could be greatly improved in the future by AI.
[79]
AI may be used by drones to help with tasks
including agricultural inspection, water management, crop health monitoring, planting, crop spraying, and soil
analysis.
[80]
Tracking agricultural conditions is made easier by drones fitted with a variety of sensors, including
ordinary cameras, thermal photos, 3D images, and multispectral photography.
[81]
Disease detection, plant
density measurement, and soil health monitoring.
[82]
8.4 Blockchain for food supply chain traceability
Smart farming could be revolutionized by these three technologies. The sustainability, effectiveness, and
transparency of farming could all be enhanced by these techniques.
[83]
One problem is that most contemporary
artificial neural networks depend on the use of cloud services, which necessitates frequent internet connections
and substantial data transfers.
[84]
Edge AI processes data directly on agricultural machinery, which is faster and
more reliable, especially in areas with poor internet.
[85]
Drones and Edge AI-powered sensors can examine crop
images, detect pest problems, and adjust watering schedules without the need for extra data processing. Remote
farms benefit from this speedy decision-making.
[86]
Edge AI will be essential to automated precision farming as
AI technology becomes more effective and reasonably priced.
[87]
A novel approach to enhancing supply chain
transparency and trust is the tracking of agricultural products from start to finish using blockchain
technology.
[88]
From the agricultural process to the consumer, blockchain documents and safeguards every phase
of a product's lifecycle. This lets consumers know where their food comes from and helps verify whether
products are sustainable, organic, or properly traded.
[89]
It quickly identifies possible contamination areas
during a recall, increasing consumer confidence and helping to guarantee food safety.
[90]
This protects public
health and benefits the entire food industry. In summary, the technology of Bitcoin is a powerful tool that helps
create a future where openness is at the heart of agriculture. Accountability and trust.
[91]
8.5 Multi-modal prediction
By improving resource efficiency and minimizing environmental effect, precision farming techniques may be
adapted for implementation in metropolitan and vertical farming, which has several advantages.
[92]
Utilizing
data-driven strategies in tiny areas reduces the total environmental impact, conserves water, and makes better
use of available resources.
[93]
This strategy promotes sustainable practices while satisfying the rising demand
for locally produced, fresh food. All things considered, adapting precision farming for vertical and urban
agriculture is a clever strategy to satisfy consumer demand for locally grown, environmentally friendly
products.
[94]
9. Conclusion
Farmers may increase efficiency, production, and sustainability by employing smart irrigation, precision
farming, supply chain management, smart greenhouses, animal tracking, agricultural drones, pest and disease
control, and crop and soil monitoring. The review's key findings demonstrate the manner in which IoT
technology is significantly altering agriculture. Accurate real-time information greatly boosts output and helps
keep crops from withering. Farmers can remotely monitor and control crops in real me thanks to IoT technology.
All of the crucial farming-related updates and statistics are available on the Blynk app. All farming operations
are fully protected by this technology, increasing output while requiring less labour. Combining IoT with
Machine Learning gives a strong chance to boost productivity, sustainability, and decision-making in farming.
However, there are still big challenges, like high costs, limited internet in rural areas, and low understanding of
technology among farmers. At the same time, there are great opportunities, such as precision farming, early
disease detection, and climate-friendly solutions that can change how food is produced. Still, there are key
research areas that need more attentionlike creating affordable and scalable systems, ensuring data can be
shared easily, and making tools that are easy for farmers to use. Fixing these gaps is important to make smart
farming accessible, dependable, and effective around the world. These technologies offer powerful capabilities
to increase production, improve resource usage, and lessen environmental damage. There are still issues,
though, such as expensive setup fees, spotty internet in rural locations, and farmers' lack of technological
expertise. However, there are also opportunities that are transforming farming, such as automation, early
danger identification, and precision farming. Important research topics including developing solutions that
function in local settings, standardizing data, and constructing cost-effective systems require additional focus
in order to fully realize this promise. Building robust, effective, and equitable food systems for the future
requires addressing these issues. Farm Beats is a well-known and affordable Connectivity of Things (IoT)
solution for farming. It makes use of TVWS, an affordable long-range technology, to support high-speed sensors.
Farm Beats' weather-sensitive, sunlight-powered wireless device base station and sophisticated gateway
ensure that services are available both globally and offline. The drone's battery life is further increased by its
enhanced path planning algorithms. The system is already being used by farmers for three purposes: storage
monitoring, animal monitoring, and precision farming. Two farms have been used to test the technique. In order
to develop more Farm Beats platform apps in the future, we are working with farmers. Technological speaking,
there is a lot of promise for improving the scalability and reliability of systems with developments in power
efficient detectors, Ambient AI, and cryptocurrency for secure data processing. However, there are still a lot of
unanswered questions. The lack of practical testing for AI-based systems for identification in complex
environments, such intercropping or agroforestry, is a major issue, especially in tropical and subtropical areas.
Furthermore, the absence of open-source, vendor-neutral frameworks limits the manner by which data may be
used, analysed, and shared across national borders, particularly in middle- and low-income nations. As
networks with Edge AI and driverless expand, many setups continue to face ethical and cybersecurity issues
such as information privacy, structure transparency, and system integration into smart farming systems. In
conclusion, the complete potential of machine learning and Internet of Things (IoT) in agriculture requires
interdisciplinary cooperation, ethical application, and fair access. Addressing the current issues will be essential
to building robust, flexible, and sustainable food systems for centuries to come.
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