Deep Learning Based IoT Module for Smart Farming in Different Environmental Conditions

Manikandan, R.; Ranganathan, G.; Bindhu, V.

doi:10.1007/s11277-022-10016-5

Cited by 18 publications

(10 citation statements)

References 32 publications

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“…Further, Manikandan et al [49] have used the DL approach towards smart farming, where a controller design uses fuzzy logic to facilitate an intelligent irrigation system. Studies in PA are not only restricted to monitoring or improving yield but also towards improving security.…”

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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“…Moreover, integrating IoT devices and systems into agricultural practices necessitates diligent efforts [44]. These efforts encompass acquiring IoT devices and utilizing various protocols and standards to ensure seamless compatibility and integration.…”

Section: A Iot Architecturesmentioning

confidence: 99%

“…These layers are also mentioned as the lower layer in the IoT architecture [21]. Furthermore, the other layers are the middleware and processing layer [44], [58], [59]. In the below subsection, we described the primary and main layer of IoT architecture for the SA system.…”

Section: A Iot Architecturesmentioning

confidence: 99%

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Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

Mowla,

Shah

et al. 2023

IEEE Access

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The increasing food scarcity necessitates sustainable agriculture achieved through automation to meet the growing demand. Integrating the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) is crucial in enhancing food production across various agricultural domains, encompassing irrigation, soil moisture monitoring, fertilizer optimization and control, early-stage pest and crop disease management, and energy conservation. Wireless application protocols such as ZigBee, WiFi, SigFox, and LoRaWAN are commonly employed to collect real-time data for monitoring purposes. Embracing advanced technology is imperative to ensure efficient annual production. Therefore, this study emphasizes a comprehensive, futureoriented approach, delving into IoT-WSNs, wireless network protocols, and their applications in agriculture since 2019. It thoroughly discusses the overview of IoT and WSNs, encompassing their architectures and summarization of network protocols. Furthermore, the study addresses recent issues and challenges related to IoT-WSNs and proposes mitigation strategies. It provides clear recommendations for the future, emphasizing the integration of advanced technology aiming to contribute to the future development of smart agriculture systems. INDEX TERMSInternet of things, wireless sensor networks, wireless network protocols, smart agriculture applications.

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“…In the past, various ML techniques have been used to address the various challenges in the domain of agriculture, e.g., weather prediction 5 , plant disease classification 6 , 7 , intelligent irrigation 8 , yield prediction 9 , crop selection, etc. 10 , shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based optimal crop selection system in smart agriculture

Rani,

Mishra,

Kataria

et al. 2023

Sci Rep

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The cultivation of most crops depends upon the regional weather conditions. So, the analysis of the agro-climatic conditions of a zone contributes significantly to deciding the right crop for the right land in the right season to obtain a better yield. Machine learning algorithms facilitate this process to a great extent for better results. In this paper, the authors proposed an ML-based crop selection model based on the weather conditions and soil parameters, collectively. Weather analysis is done using LSTM RNN and the process of crop selection is completed using Random Forest Classifier. This model gives better results for weather prediction in comparison to ANN. With LSTM RNN, the RMSE observed in Min. Temp. prediction is 5.023%, Max. Temp. Prediction is 7.28%, and Rainfall Prediction is 8.24%. In the second phase, the Random Forest Classifier showed 97.235% accuracy for crop selection, 96.437% accuracy in predicting resource dependency, and 97.647 accuracies in giving the appropriate sowing time for the crop. The model construction time taken with a random forest classifier using mentioned data size is 5.34 s. The authors also suggested the future research direction to further improve this work.

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Deep Learning Based IoT Module for Smart Farming in Different Environmental Conditions

Cited by 18 publications

References 32 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

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

Machine learning-based optimal crop selection system in smart agriculture

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