Internet of Things (IoT) for Sensor-Based Smart Farming: Challenges and Opportunities

Kaur, Jaspinder; Yadav, Sankalp; Gill, Harjot Singh

doi:10.1007/978-3-031-04524-0_9

Cited by 4 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A smart irrigation system for gardening based on the Internet of Things (IoT) using Arduino is proposed in [14]. The prototyping model was used to create a smart irrigation system for gardening.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, we conclude that the sensor readings are accurate. When compared to other works such as [14], [15], [16], this work is simple to operate and can be easily managed by literate and nonliterate farmers. Furthermore, the proposed device can adapt to the irrigation needs of different crops which is a feature lacking in the other works.…”

Section: Watere F F Iciency =mentioning

confidence: 99%

See 1 more Smart Citation

A Crop Adaptive Irrigation System for Improving Farm Yield in Rural Communities

Obatimehin,

Ogayemi,

Osifeko

et al. 2024

IJCDS

View full text Add to dashboard Cite

Inefficient irrigation is one of the major causes of low crop yield and poverty among rural farmers in developing countries. Existing smart irrigation systems are often complicated, expensive, and require a high level of literacy, hindering their use by rural farmers. Thus, we propose an adaptive irrigation system that is easy to operate and accessible to farmers in rural communities. The proposed system utilizes sensors to monitor soil moisture levels, temperature, and humidity and delivers water to plants only when necessary, increasing efficiency and reducing water waste. An Arduino UNO board is used to analyze the data from sensors to determine the correct amount of water for each crop. A two-level feedback control system activates the water pump, ensuring the moisture level falls within the desired range. The system also has a user-friendly interface, with an SMS feature that allows users to control and monitor the system remotely. The system was tested for three distinct crops: beans, maize, and tomatoes, and its efficacy, efficiency, and sensor accuracy were evaluated. Results indicate that the system conserves water and increases crop yields by delivering the correct amount of water to each crop. The proposed adaptive smart irrigation system can optimize water consumption, reduce waste, and boost yields, resulting in significant energy and water savings for farmers. This research has implications for agriculture in rural communities. It can reduce the burden on farmers by providing an automated irrigation system that requires minimal human intervention. The system can also be controlled remotely, for easy monitoring and modification. Additionally, the system is cost-effective, making it accessible to small-scale farmers with limited resources.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Watere F F Iciency =mentioning

confidence: 99%

A Crop Adaptive Irrigation System for Improving Farm Yield in Rural Communities

Obatimehin,

Ogayemi,

Osifeko

et al. 2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…Sensors and devices connected through the IoT allow remote monitoring of crops and soil conditions, making the process more efficient and datadriven. Smart farming, facilitated by the IoT, holds the potential to address critical food-related challenges [57]. The integration of blockchain with IoT aims to establish secure communication infrastructure.…”

Section: Related Workmentioning

confidence: 99%

Optimized Data Fusion With Scheduled Rest Periods for Enhanced Smart Agriculture via Blockchain Integration

Ahmed,

Parveen,

Abdullah

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The study introduces an efficient data aggregation technique for smart agriculture by leveraging Blockchain technology and a novel method referred to as the "cluster head sleep schedule." The primary objective is to enhance the data collection process within a large-scale agricultural setting where multiple sensors continually generate vast amounts of data while monitoring and safeguarding crops from pest attacks. The proposed method involves the segmentation of sensors into clusters, each led by a designated cluster head responsible for collecting data from its constituent members deployed in the field to monitor pest attacks and promptly report any issues to the management. To curtail data redundancy, the study employs a fuzzy matrix to group nodes based on high-similarity data. This approach enables the selective suspension of certain nodes while others remain active. The data received from these nodes undergoes analysis using a fuzzy similarity matrix for clustering, ensuring that only unique data is transmitted to the base station. Redundant nodes from all clusters are identified and placed in a sleep mode, thus conserving energy and prolonging the network's lifespan. This sleep scheduling mechanism is implemented subsequent to data redundancy reduction, facilitating immediate pest attack control in agriculture. By implementing these techniques, smart agriculture stands to benefit from optimized energy utilization and reduced costs associated with monitoring and pest control, thereby fostering sustainable and efficient operations. The cluster head is responsible for storing the data on a base station positioned at the network's edge, allowing for local processing and prompt communication of pest attack information to the farmer for immediate action. Moreover, this edge system stores the data on a Blockchain network for future analysis and serves as a guideline for pest attack control in the pesticide industry, thereby enhancing data security and immutability. In addition to these advantages, the research also emphasizes the importance of controlling pest attacks to enhance crop production in the field, ultimately contributing to the country's economic growth. Simulation results affirm that the proposed approach leads to notable cost reductions, decreased energy consumption, improved crop production, precise crop monitoring to prevent pest attacks, and a prolonged network lifespan. These outcomes underscore the effectiveness of this approach within the context of smart agriculture and its role in enhancing the monitoring system for smart agriculture and bolstering security through Blockchain technology.

show abstract

Berries of Low-Cost Smart Irrigation Systems for Water Management an IoT Approach

Mustafa,

Ali,

Javed

et al. 2024

BBE

View full text Add to dashboard Cite

Irrigation is a time-honored process that regularly requires more physical effort and is used in agriculture. Sensors and microcontrollers can automatically water plants by detecting when they need to be watered and behaving appropriately. This procedure is known as "determining when the plants require water." When you go through this procedure, is known as "determining when the plants require water". The use of automation technologies can enhance production rates, costs, and resource use efficiency. The major purpose of this project is to develop and create a microcontroller-based system capable of autonomously watering plants while also supplying cultivators with essential information. This will be achieved by developing and building a system with the necessary capabilities.

show abstract

Internet of Things (IoT) for Sensor-Based Smart Farming: Challenges and Opportunities

Cited by 4 publications

References 14 publications

A Crop Adaptive Irrigation System for Improving Farm Yield in Rural Communities

A Crop Adaptive Irrigation System for Improving Farm Yield in Rural Communities

Optimized Data Fusion With Scheduled Rest Periods for Enhanced Smart Agriculture via Blockchain Integration

Berries of Low-Cost Smart Irrigation Systems for Water Management an IoT Approach

Contact Info

Product

Resources

About