Wireless Sensor Networks-based Smart Agriculture: Sensing Technologies, Application and Future Directions

Rahu, Mushtaque Ahmed; Karim, Sarang; Shams, Rehan; Soomro, Ayaz Ahmed; Chandio, Abdul Fattah

doi:10.30537/sjet.v5i2.1104

Cited by 8 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The lifespan property of WSN, or the amount of time it takes for a sensor to run out of power, was investigated by authors in [19]. Also, they looked at the WSN lifespan problem using a metaheuristic approach.…”

Section: Reviews Of Water Qualitymentioning

confidence: 99%

An IoT and machine learning solutions for monitoring agricultural water quality: a robust framework

Rahu,

Shaikh,

Karim

et al. 2024

Mehran Univ. res. j. eng. technol.

View full text Add to dashboard Cite

All living things, comprising animals, plants, and people require water to survive. The world is covered in water, just 1 percent of it is fresh and functional. The importance and value of freshwater have increased due to population growth and rising water demands. Approximately more than 70 percent of the world's freshwater is used for agriculture. Agricultural employees are the least productive, inefficient, and heavily subsidized water users in the world. They also utilize the most water overall. Irrigation consumes a considerable amount of water. The field's water supply needs to be safeguarded. A critical stage in estimating agricultural production is crop irrigation. The global shortage of fresh water is a serious issue, and it will only get worse in the years to come. Precision agriculture and intelligent irrigation are the only solutions that will solve the aforementioned issues. Smart irrigation systems and other modern technologies must be used to improve the quantity of high-quality water used for agricultural irrigation. Such a system has the potential to be quite accurate, but it requires data about the climate and water quality of the region where it will be used. This study examines the smart irrigation system using the Internet of Things (IoT) and cloud-based architecture. The water's temperature, pH, total dissolved solids (TDS), and turbidity are all measured by this device before the data is processed in a cloud using the range of machine learning (ML) approaches. Regarding water content limits, farmers are given accurate information. Farmers can increase production and water quality by using effective irrigation techniques. ML methods comprising support vector machines (SVM), random forests (RF), linear regression, Naive Bayes, and decision trees (DT) are used to categorize pre-processed data sets. Performance metrics like accuracy, precision, recall, and f1-score are used to calculate the performance of ML algorithms.

show abstract

Section: Reviews Of Water Qualitymentioning

confidence: 99%

An IoT and machine learning solutions for monitoring agricultural water quality: a robust framework

Rahu,

Shaikh,

Karim

et al. 2024

Mehran Univ. res. j. eng. technol.

View full text Add to dashboard Cite

show abstract

“…The labeled training datasets are used in supervised learning to derive predictive functions. Each training instance contains input values and predicted corresponding outputs [30]. Several methods, including linear regression, decision trees (DT), support vector machines (SVM), artificial neural networks (ANN), k-nearest neighbor (KNN), Naive Bayes, random forests (RF), etc., have been designed and used for supervised learning in data classification and regression.…”

Section: Overview Of Machine Learningmentioning

confidence: 99%

“…To optimize the quantity and water quality for all consumers, especially planters, it reveals new approaches and strategies for intelligent water control systems in agriculture. Energy-efficient water managing units, reliable irrigation projects for agriculture in sparsely populated rural areas of the Mediterranean, water, and manure use reduction in agro systems, water reutilizing based on arithmetical technological innovations, and socioeconomic experiments to expand water management governance are some of the possible barriers [30].…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

Water Quality Monitoring in Agriculture: Applications, Challenges and Future Prospectus with IoT and Machine Learning

Jatoi,

Rahu,

Karim

et al. 2023

JAET

View full text Add to dashboard Cite

Water is one of the key elements involved in maintaining the quality of life. It is essential in the field of agriculture for food production and livestock farming. Water quality is continuously declining as a consequence of the growing trend of urbanization and industrialization, which can harm the environment and human health. In recent years, several researchers in the field of agriculture have shown advancements in techniques with the implementation of the Internet of Things (IoT) Artificial Intelligence (AI) machine learning (ML) and smart technologies. These advancements aim at improving water usage and enhancing the quality and quantity of different crops, with the ability to lower analysis costs, time and facilitate the achievement of management results. This paper outlines an overview of recent studies on water control and management using the IoT and ML models in the field of agriculture. Thus, new technologies for analyzing water quality metrics like pH, temperature, color, turbidity, Total Dissolved Solids (TDS), salinity, and nitrogen are described. It also describes few exposed contests used to draw related study suggestions in the future, such as the use of advanced intelligent tools and strategies for water quality assessment and management in the agriculture sector.

show abstract

“…Also, for smart farming, it makes sense to use batteryless wireless sensors, when they are spread all over the vast farmlands and the number of nodes increases. Wireless sensors gather critical information such as soil moisture, temperature, humidity, and light levels and communicate wirelessly enabling real-time monitoring [36]. [37] In logistics, [37] have an e-ink display, a wireless communication module and a perovskite solar cell to power the device limitless.…”

Section: Energy Harvesting Applicationsmentioning

confidence: 99%

Development of a low-power solar energy harvester PMU integrated circuit optimized for indoor environments.

Lopez-Gassó

View full text Add to dashboard Cite

Wireless electronic devices face a common and important constraint: the limited lifespan of their batteries. The concept of quasi-perpetual electronic devices, particularly for tiny and low-power electronic systems, is progressively becoming attainable. En-ergy harvesting can be the part of the solution by improving, attenuating or even eliminating this limitation by extending its autonomy. The ﬁnal goal is to convert them into a virtually perpetual system. Energy Harvesting can be deﬁned as the collection and storage of ambi-ent energy from the surroundings, to later convert it into electrical energy and use it to power a small and low-power electronic devices. These devices are often. The energy harvesting technique is intended to be used in devices where it is impractical or inconvenient to use traditional battery or grid power sources. Moreover, it have recently received more attention due to its in-creasing use in the Internet of Things (IoT) and the appearing of Wireless Sensor Networks (WSN). This work describes an energy-eﬃcient monolithic Power Management Unit (PMU) that includes a charge pump adapted to photovoltaic cells with the capability of charging a large supply capacitor and managing the stored energy eﬃciently to provide the required supply voltage and power to low energy consumption wireless sensor nodes such as RFID sensor tags. The proposed system starts-up self-suﬃciently with a light source lumi-nosity equal to or higher than 245 lux, meaning a voltage input of 595 mV using only a 1.42 cm2 solar cell. The result energy harvester integrates an energy monitor that gives the ability to supply autonomous sensor nodes with discontinuous operation modes. The full PMU occupies an area of 1.073 mm2 using a standard 180 nm CMOS technology. The half-ﬂoating architecture of the integrated charge pump avoids losses of charging the top/button plate of the stray capacitors in each clock cycle. Measurements’ results on a fabricated IC exhibit an an output power of 13.9 µW with 70.2 percent of peak-to-peak eﬃciency at indoor light conditions (680lux) with an output voltage of 2V.

show abstract

Wireless Sensor Networks-based Smart Agriculture: Sensing Technologies, Application and Future Directions

Cited by 8 publications

References 33 publications

An IoT and machine learning solutions for monitoring agricultural water quality: a robust framework

An IoT and machine learning solutions for monitoring agricultural water quality: a robust framework

Water Quality Monitoring in Agriculture: Applications, Challenges and Future Prospectus with IoT and Machine Learning

Development of a low-power solar energy harvester PMU integrated circuit optimized for indoor environments.

Contact Info

Product

Resources

About