FarmFox: A Quad-Sensor-Based IoT Box for Precision Agriculture

Sengupta, Anirbit; Debnath, Biswajit; Das, Abhijit; De, Debashis

doi:10.1109/mce.2021.3064818

Cited by 32 publications

(19 citation statements)

References 10 publications

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“…Though the empirical derivations are simple to understand, the model has to be designed with more efficiency to handle the planting process in other fields. Another linear regression model for soil moisture analysis has been designed in Sengupta et al 19 It is observed that the incorporation of neural network models can provide better results in terms of accuracy with a higher rate of optimization. The work 20 developed a soil analysis model using artificial neural networks (ANN) for predicting the soil quality based on meteorological factors based on real‐time data.…”

Section: Related Workmentioning

confidence: 99%

Retracted: Soil monitoring and evaluation system using EDL‐ASQE: Enhanced deep learning model for IoT smart agriculture network

Sumathi¹,

Subramanian²,

Karthikeyan³

et al. 2021

Int J Communication

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The enormous growth of the Internet of Things (IoT) network provides abundant support to agriculture and development, which states the future scope of IoT-based agriculture. In a recent scenario, agriculture IoT can be integrated with sensors, communication protocols, and microcontrollers for automated process executions to increase productivity. Moreover, deep learning effectiveness produces appropriate results and solves several real-time issues related to agriculture-based advancements. The proposed system presents the design of an IoT network communication system to estimate the soil conditions. Soil quality is an important factor in modernized agriculture, productivity enhancement, and hydrological cycles. By the soil quality analysis, the accurate prediction is very significant for sensible usage of resources. An enhanced deep learning model for IoT network-based automated soil quality evaluation observes the complex soil features and meteorological factors with those concerns. Here, the real-time samples are collected from the local area sensor network for analysis. The deep learning model is developed with big data fitting ability for soil quality prediction. The weight factors (W.F.) are derived for measuring the soil quality accurately. The proposed IoT network-based agriculture structure allows a flexible approach to different types of crops and implementation in agricultural areas. Experimental results obtained in the laboratory and onsite confirmed the performance and reliability of the system. The result evaluations are carried out based on precision, accuracy, and processing time, and results show that the model achieves better results than compared models. K E Y W O R D S agriculture IoT network, enhanced deep learning, soil moisture, soil quality, weight factor | INTRODUCTIONIn the present scenario, the combination of the Internet of Things, cloud computing, and big data processing offers a great agricultural data growth platform. 1 For enhancing the agriculture yield with minimal resources and efforts, several innovations are made. However, the increasing population never makes the necessities and supplies all the time.

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Section: Related Workmentioning

confidence: 99%

Retracted: Soil monitoring and evaluation system using EDL‐ASQE: Enhanced deep learning model for IoT smart agriculture network

Sumathi¹,

Subramanian²,

Karthikeyan³

et al. 2021

Int J Communication

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show abstract

“…Various protocols have been utilized to establish M2G communication, including 1) standard protocols such as Zigbee [77,[129][130][131][132][133][134][135], Bluetooth [136][137][138], Wi-Fi [89,90,[139][140][141], cellular network [110, 129-131, 133, 142], and Radio-frequency identification (RFID) [95,121,[143][144][145][146], 2) Low Power Wide Area Networks (LPWAN) such as LORA [147][148][149][150][151][152][153][154][155], NB-IoT [156,157] and Sigfox [99,158,159].…”

Section: ) M2g Communication Protocolsmentioning

confidence: 99%

Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

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As today's agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgrading the farming practices by shifting towards Smart Farming (SF) -utilizing advanced information and communication technologies to improve the quantity and quality of the crop with minimal labor interference. SF has gained lots of interest in recent years utilizing a variety of technological innovations in the field, which imposes a challenge on farmers and technology integrators to identify the suitable technologies and best practices for a particular application. This paper provides a survey of the most recent SF scientific literature to identify common practices toward technology integration, challenges, and solutions. The survey was conducted on 588 papers published on the IEEE database following Cochrane methods to ensure appropriate analysis and interpretation of results. The papers' contributions were analyzed to identify necessary technologies that constitute SF, and consequently, research themes were identified. The identified themes are sensors, communication, big data, actuators and machines, and data analysis. Besides presenting an in-depth analysis of each identified theme, the paper discusses integrating more than one technology in systems to achieve independency. The most common SF systems are remote monitoring, autonomous, and intelligent decision-making systems.

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“…Akhter and Shabir Ahmad Sofi [15] explore the challenges and effects of the applications of data analytics, machine learning, and IoT in agriculture. Sengupta, Anirbit, et al [20] propose a quadcopter based IoT system named "FormFox" to monitor plant health systems in terms of pH and turbidity.…”

Section: Literature Reviewmentioning

confidence: 99%

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

et al. 2022

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Accurate Evapotranspiration for saline soils (ETs) is important as well as challenging for the reclamation of saline soils through an effective leaching process. Evapotranspiration (ET) by FAO-56 Penman-Monteith standard method is complex, especially for saline soils. Moreover, existing studies focus on the use of the Internet of Things (IoT) and machine learning-enabled smart and precision irrigation water recommendation systems along with the ET estimation by limited parameters. The ETs for saline soils are also equally important for the reclamation of saline soils, which is ignored by the existing literature. The study proposed IoT and machine leaching-based architecture of context-aware monthly ETs estimations for saline soil reclamation with the effective leaching process. The IoT-enabled crop field contexts in terms of crop field temperature, soil salinity, and irrigation water salinity are used as input features to the Long Short-Term Memory (LSTM) and ensembled LSTM models for monthly ETs predictions. The performance of the proposed solution is observed in terms of the accuracy of the machine learning models along with the comparison against the FAO-56 PM-based standard method. The implementation of the proposed solution reveals that the ensembled LSTM-based approach for ETs is more accurate as compared to the LSTM model with accuracies of 92 and 90% for the training and validation datasets, respectively. The predictions made by the ensembled LSTM are more in line with the FAO-56 PM-based method with a Pearson correlation of 0.916 as compared to LSTM models. The implementation of the proposed solution in real-time environments reveals that the proposed solution is more effective in reducing the soil salinity as compared to the traditional method.

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FarmFox: A Quad-Sensor-Based IoT Box for Precision Agriculture

Cited by 32 publications

References 10 publications

Retracted: Soil monitoring and evaluation system using EDL‐ASQE: Enhanced deep learning model for IoT smart agriculture network

Retracted: Soil monitoring and evaluation system using EDL‐ASQE: Enhanced deep learning model for IoT smart agriculture network

Technologies Driving the Shift to Smart Farming: A Review

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

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