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Peer Reviewed
Innovations in Sensor-Based Systems and Sustainable Energy Solutions for Smart Agriculture: A Review
This is a peer-reviewed entry published in Encyclopedia Journal, full entry can be found at 10.3390/encyclopedia5020067

Smart agriculture is transforming traditional farming by integrating advanced sensor-based systems, intelligent control technologies, and sustainable energy solutions to meet the growing global demand for food while reducing environmental impact. This review presents a comprehensive analysis of recent innovations in smart agriculture, focusing on the deployment of IoT-based sensors, wireless communication protocols, energy-harvesting methods, and automated irrigation and fertilization systems. Furthermore, the paper explores the role of artificial intelligence (AI), machine learning (ML), computer vision, and big data analytics in monitoring and managing key agricultural parameters such as crop health, pest and disease detection, soil conditions, and water usage. Special attention is given to decision-support systems, precision agriculture techniques, and the application of remote and proximal sensing technologies like hyperspectral imaging, thermal imaging, and NDVI-based indices. By evaluating the benefits, limitations, and emerging trends of these technologies, this review aims to provide insights into how smart agriculture can enhance productivity, resource efficiency, and sustainability in modern farming systems. The findings serve as a valuable reference for researchers, practitioners, and policymakers working towards sustainable agricultural innovation.

wireless sensor networks (WSN) communication protocol smart agriculture irrigation fertilization pest control soil moisture remote sensing energy harvesting

Background and Motivation

The growing global population, increasing food demand, and depletion of natural resources present critical challenges for modern agriculture. Conventional farming practices often face limitations such as inefficient resource use, high dependency on manual labor, and inconsistent yield outcomes. To address these issues, smart agriculture has emerged as a promising approach by integrating advanced sensor-based systems, renewable energy solutions, and data-driven decision making. A significant technological transformation is occurring in agriculture through the adoption of Internet of Things (IoT), artificial intelligence (AI), computer vision, and big data analytics. Computer vision systems, particularly in livestock monitoring, crop surveillance, and pest detection, enable real-time observation and automated management of agricultural operations [1][2]. In recent years, the emergence of 5G communication technologies has further transformed smart agriculture by enabling ultra-low latency, high reliability, and massive IoT connectivity. The paper of Rehman [3] emphasizes the pivotal role of 5G in integrating remote-sensing data with real-time decision making, thereby enhancing productivity and sustainability in modern agricultural systems. Meanwhile, big data analytics empowers farmers by offering predictive insights into crop health, soil conditions, and irrigation needs, facilitating precision farming practices [4][5]. Despite remarkable technological advancements, there remains a gap in the holistic integration of RF energy-harvesting techniques, sustainable energy management, and precision agriculture systems [6]. The efficient and wide deployment of smart sensing systems is often constrained by power availability, especially in remote and rural farming environments. Therefore, designing energy-autonomous sensing networks powered by RF energy harvesting and sustainable energy sources becomes crucial. The main motivation of this review is to systematically analyze recent innovations in sensor-based smart agriculture technologies, critically discuss energy-harvesting solutions to power such systems, and explore sustainable energy strategies for developing resilient and efficient agricultural ecosystems. The review highlights the integration of sensing, communication, energy harvesting, and data analytics technologies and proposes future directions to address existing gaps in smart agriculture. This paper’s main contribution is to provide a comprehensive, updated review combining sensor technologies, wireless communication protocols, renewable energy integration, and advanced data analytics for smart agriculture. It specifically emphasizes the role of wideband RF energy harvesting and self-powered sensing systems—a relatively underexplored but essential field for next-generation agricultural applications.
Weather forecasting, soil nutrient monitoring, insect and pest control, early notifications of viruses and bacteria, automatic control systems, remote sensing, and various other parameters form the core components of smart agriculture. These technologies enhance the agroecological environment, increase production and quality, and reduce the use of pesticides and chemical fertilizers, thereby improving the reliability and sustainability of agricultural activities. Additionally, the adoption of renewable energy solutions such as solar and wind power further promotes sustainability by reducing dependence on fossil fuels and minimizing environmental impact.
This paper introduces the role of smart agriculture in providing food security for both developing and developed countries. For developed countries, the main concerns are optimizing large-scale farming and increasing productivity while lowering environmental impacts. In contrast, developing countries face challenges such as land scarcity and labor shortages. Deploying smart agriculture systems can significantly improve productivity, reduce hunger, and provide resilience against the challenges posed by climate change, making it essential for ensuring food security for the growing global population.

Objectives of the Study

The goal of this study is to systematically analyze recent innovations in sensor-based smart agriculture technologies, critically discuss energy-harvesting solutions to power such systems, and explore sustainable energy strategies for developing resilient and efficient agricultural ecosystems. The main objectives of this paper are to:
  • Summarize the latest developments in smart agriculture technologies.
  • Highlight the growing role of artificial intelligence and machine learning in agricultural processes.
  • Discuss the benefits and limitations of various smart agriculture systems.
  • Provide insights into future research directions and potential applications of these technologies.

Main Contributions

This paper makes several key contributions to the field of smart agriculture:
  • Comprehensive Review: Provides an in-depth review of recent advancements in IoT-based sensors, wireless communication protocols, energy-harvesting methods, and automated irrigation and fertilization systems.
  • AI and ML Integration: Emphasizes the growing role of artificial intelligence and machine learning in monitoring and managing various agricultural processes, including crop health assessment, pest control, and soil and water resource optimization.
  • Technological Insights: Discusses the benefits and limitations of various smart agriculture systems, offering valuable insights for researchers and practitioners.
  • Future Directions: Identifies potential future research directions and applications of smart agriculture technologies, contributing to the ongoing development of sustainable and efficient farming practices.

Various Wireless Nodes

One advanced technology associated with smart agriculture is remote sensing. Wireless sensor networks (WSNs) are a critical component of smart agriculture, enabling real-time monitoring and data collection. Various wireless nodes are used in agricultural applications to measure environmental parameters such as temperature, humidity, soil moisture, and light. To monitor the physical conditions of the land and environment, wireless sensor network (WSN) technology creates a network of connected wireless nodes. Wireless nodes in the agricultural sector are illustrated in Figure 1. This figure shows different types of wireless nodes, including MICA2, Cricket, IRIS, and MICAz, which are used to monitor environmental parameters such as temperature, humidity, and soil moisture. These nodes are essential for real-time data collection and decision making in smart agriculture.
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Figure 1. Illustration of various wireless nodes used in the agricultural sector. Reprinted from ref. [7].
By sensing air temperature, pressure, humidity, soil moisture, and other elements, the health of plants can be significantly improved. Wireless sensor networks (WSNs) play a crucial role in this process by providing real-time data on these parameters. These sensors help farmers make informed decisions about irrigation, fertilization, and pest control, thereby optimizing resource usage and enhancing crop yield. Additionally, the integration of WSN with IoT technologies allows for remote monitoring and automated control of agricultural processes, further improving efficiency and sustainability. Some of the widely used wireless nodes in agricultural practice are listed in Table 1. This table lists different wireless nodes, their signaling rates, and the sensing parameters they monitor. For example, the MICA2 node has a signaling rate of 38.4 K Baud and can sense temperature, light, pressure, and humidity, among other parameters.
Table 1. Overview of widely used wireless nodes in the agricultural sector.
Table
S/N Wireless Node Signaling Rate Sensing Parameters Reference
1 MICA2 38.4 K Baud Sounder, video sensor, accelerometer, GPS [7]
2 Cricket 38.4 K Baud Temperature, light, pressure, humidity, relative humidity, acoustic, magnetometer [8]
3 IRIS 250 Kbps Light, pressure, acceleration, magnetic, relative humidity, acoustic, seismic, video sensor [9]
4 MICAz 250 Kbps Light, video sensor, GPS, relative humidity, humidity, magnetometer, temperature, pressure, accelerometer, acoustic, sounder, microphone [9]
5 MICA2DOT 38.4 K Baud GPS, relative humidity, light, temperature, humidity, pressure, accelerometer, acoustic [7]
6 Imote2 250 Kbps Temperature, light, accelerometer, humidity [8]

Various Agricultural Sensors

Traditional agriculture systems often rely on human perception and experience rather than smart sensors, which can lead to inconsistent production quality. To address this issue, real-time sensing is crucial for continuously tracking environmental conditions and changes in plant health. This technology enables farmers to make timely decisions, allowing for early treatment, resource optimization, cost reduction, improved crop health, and increased efficiency. By minimizing the excess use of resources such as water, pesticides, and fertilizers, real-time sensing lowers the environmental impact and maximizes yield production through data analysis on plant growth and environmental conditions. Table 2 summarizes the various sensors used in smart agriculture. This table provides details on different types of sensors, including soil moisture sensors, temperature sensors, and photosynthesis sensors, along with the specific parameters they measure. These sensors are essential for optimizing agricultural practices and improving crop productivity.
Table 2. Overview of various sensors used in smart agriculture. Adapted from ref. [10].
Table
Serial No. Sensor Name Parameters
1 EC 250, ECH2O Soil temperature, soil moisture, salinity level of water, conductivity
2 107-L, LT-2 M, 100K6A1B, MP406 Temperature of the plant
3 H2TM, 237 LWS Level of CO2, H2 and Temperature, Wetness of the plant
4 CM1000TM, YSI 6025 Photosynthesis
5 LW100, TT4 Moisture, temperature, wetness of the plant
6 TPS-2 Photosynthesis and level of CO2
7 Cl-340, PTM-48A Photosynthesis, moisture, temperature, wetness, H2, CO2 Level of the plant
8 CM-100, MSO- 70 Temperature, pressure and humidity of the air, wind speed
9 HMP45C, Cl-340, XFAM-115KPASR, SHT71, SHT75 Temperature, pressure and humidity of the air
10 107-L AT Air temperature
Integrating IoT and smart sensors for smart farming is illustrated in Figure 2. This figure illustrates different types of sensors, such as soil moisture sensors, temperature sensors, and photosynthesis sensors, which are deployed in smart agricultural systems. These sensors provide critical data for optimizing irrigation, fertilization, and pest control practices. These devices play a crucial role in monitoring and managing agricultural processes, providing real-time data on environmental conditions and plant health. By leveraging these sensors, farmers can optimize resource usage, improve crop yield, and enhance the overall efficiency and sustainability of their farming practices.
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Figure 2. Integrating IoT and smart sensors for smart farming. Reprinted from ref. [8].

List of Wireless Communication Protocols (WCP)

Wireless communication protocols are crucial for the automatic control systems in smart agriculture. These protocols enable seamless communication between sensors, controllers, and data processing units. Technologies such as Cellular, ZigBee, 6LoWPAN, RFID, Bluetooth, Wi-Fi, GPTS, SigFox, and LoRaWAN are commonly used in this domain [11][12][13][14][15][16][17][18][19][20][21][22][23]. These platforms connect with sensors to collect information and transmit data to farmers, reducing the need for physical presence in the field and enabling real-time adjustments as needed. Wireless communication systems eliminate the need for physical cables or communication lines, thereby reducing the need for expensive infrastructure. Table 3 provides an overview of some commonly used wireless communication protocols (WCP) in the smart agricultural sector [10]. This table outlines various communication protocols, their network topology, data rate, standard, power consumption, and communication range. These protocols facilitate efficient data transmission and remote monitoring in smart agriculture.
Table 3. List of wireless communication protocols (WCP) used in the smart agricultural sector. Adapted from ref. [10].
Table
Communication Protocols Network Topology Data Rate Standard Power Consumption Communication Range
6LoWPAN Technology Star, Mesh 0.3–50 Kbps IEEE 802.15.4 [24] Low 2 to 5 km urban, 15 km sub-urban
ZigBee Technology Star, Mesh, cluster 250 Kbps IEEE 802.15.4 [24] Low 10 to 100 m 15 km sub-urban
Bluetooth Technology Star, Bus 1–2 Mbps IEEE 802.15.1 [25] Low 30 m
RFID Technology P2P 50 tags/s RFID [26] Ultra Low 10 to 20 cm
LoRa WAN Technology P2P, Star 27–50 Kbps IEEE 802.11ah [27] Very Low 5 to 10 km
Wi-Fi Technology Star 1–54 Mbps IEEE 802.11 [28] Medium 50 m
An overall smart agricultural system is presented in Figure 3. This figure demonstrates how various components, including IoT-based sensors, wireless communication protocols, and automated control systems, work together to enhance agricultural productivity and sustainability.
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Figure 3. Diagram of a comprehensive smart agriculture system integrating sensors, communication networks, and automated controls. Reprinted from ref. [9].

References

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Subjects: Computer Science, Artificial Intelligence; Agricultural Engineering
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Md. Mahadi Hasan Sajib
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Abu Sadat Md. Sayem
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Online Date: 22 May 2025
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