Single Probe Imitation of Multi-Depth Capacitive Soil Moisture Sensor Using Bidirectional Recurrent Neural Network

Pal, Pankaj; Tripathi, Sachin; Kumar, Chiranjeev

doi:10.1109/tim.2022.3156179

Cited by 16 publications

(5 citation statements)

References 26 publications

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“…The probable reason is that DL-based approaches were claimed of higher accuracy and reduced dependency towards feature extraction compared to ML. The work carried out by Pal et al [32] has addressed the issue of minimal energy and cost towards tracking the moisture content of soil. The study of water movement in soil is captured using conditioning circuits.…”

Section: Approaches In Pa-iotmentioning

confidence: 99%

Sustainability insights on learning-based approaches in precision agriculture in internet-of-things

Panduranga,

Ranganathasharma

2024

IJECE

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Precision agriculture (PA) is meant to automate the complete agricultural processes with the sole target of enhanced crop yield with reduced cost of operation. However, deployment of PA in internet of things (IoT) based architecture demands solutions towards addressing various challenges where most are related to proper and precise predictive management of agricultural data. In this perspective, it is noted that learning-based approaches have made some contributory success towards addressing different variants of issues in PA; however, such methods suffer from certain loopholes, primarily related to the non-inclusion of practical constraints of IoT infrastructure in PA and lack of emphasis towards bridging the trade-off between higher accuracy and computational burden that is eventually associated with this. This paper contributes towards highlighting the strengths and weaknesses of recent learning approaches and contributes towards novel findings.

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Section: Approaches In Pa-iotmentioning

confidence: 99%

Sustainability insights on learning-based approaches in precision agriculture in internet-of-things

Panduranga,

Ranganathasharma

2024

IJECE

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“…Influences of field heterogeneity [164] [67,191] Sensor field placement [64,91,167,175,185,192] Effective sensing [91,170,175,183,[193][194][195][196] Security Data security and governance [165,166,168,182,190,192,194,[197][198][199][200][201][202] Hacking attacks [199,200,202,203] Data processing Data analysis [19,166,188,189 [175,185,186] Latency and throughput [19,166,168,184,211] Future research 5G challenges [167,171] Ground robot path planning and execution [212] Over...…”

Section: Miscellaneousmentioning

confidence: 99%

Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

Alexopoulos,

Koutras,

Ali

et al. 2023

Agronomy

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As the global population continues to increase, projected to reach an estimated 9.7 billion people by 2050, there will be a growing demand for food production and agricultural resources. Transition toward Agriculture 4.0 is expected to enhance agricultural productivity through the integration of advanced technologies, increase resource efficiency, ensure long-term food security by applying more sustainable farming practices, and enhance resilience and climate change adaptation. By integrating technologies such as ground IoT sensing and remote sensing, via both satellite and Unmanned Aerial Vehicles (UAVs), and exploiting data fusion and data analytics, farming can make the transition to a more efficient, productive, and sustainable paradigm. The present work performs a systematic literature review (SLR), identifying the challenges associated with UAV, Satellite, and Ground Sensing in their application in agriculture, comparing them and discussing their complementary use to facilitate Precision Agriculture (PA) and transition to Agriculture 4.0.

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“…The potential of iot in agriculture. Raspberry Pi-Controlled Rover [33] N/A Deployment of a surveillance robot controlled by a smartphone.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

IoT Based Machine Learning Weather Monitoring and Prediction Using WSN

Singh,

Limkar

2023

IJRITCC

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A novel approach to analysis and prediction is provided by the internet of things-based time monitoring and prediction system using wireless sensor networks (WSN) and machine learning techniques (ML). To give accurate meteorological data in real time, the integrated system uses IoT, WSN, and ML. Making informed decisions requires these insights. Includes strategically positioned infrared points that are used to gather meteorological information, such as temperature, humidity, pressure, and wind speed, among other things.The machine's automatic data processing methods are then used in a central processing unit to collect and analyse the data. By seeing patterns and drawing diagrams utilising previously collected data, ML models are able to comprehend intricate temporal dynamics. An important development in this system is its predictive capabilities. Artificial intelligence has the processing power to precisely forecast short-term weather patterns, enabling the rapid transmission of warnings for extreme localised events and the reduction of potential dangers.The combination of historical data, real-time sensor inputs, and automated analysis produces the predictive potential. The "Internet of Things" architecture used to develop this system makes it simpler to gather meteorological data. A number of industries, including as agriculture, transportation, emergency management, and event planning, are encouraged to make data-based decisions since users can quickly obtain current meteorological conditions and forecasts through user-friendly web interfaces or mobile applications.

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Single Probe Imitation of Multi-Depth Capacitive Soil Moisture Sensor Using Bidirectional Recurrent Neural Network

Cited by 16 publications

References 26 publications

Sustainability insights on learning-based approaches in precision agriculture in internet-of-things

Sustainability insights on learning-based approaches in precision agriculture in internet-of-things

Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

IoT Based Machine Learning Weather Monitoring and Prediction Using WSN

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