LSTM Filter for Smart Agriculture

Kim, Junwhan; Yu, Byunggu; O'Hara, Sabin

doi:10.1016/j.procs.2022.10.152

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…The work carried out by Kim et al [46] has developed a Dl-based filter to suppress the noise present in various agricultural data. LSTM has been used for designing this filter, which is reported to improve production with reduced cost.…”

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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Time series forecasting of wheat crop productivity in Egypt using deep learning techniques

Mahmoud,

Mohammed,

abdel wahab

et al. 2024

Int J Data Sci Anal

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Egypt's agricultural sector plays a critical role in the country's economy, with wheat cultivation being vital for ensuring food security. However, the challenges faced by wheat farming in Egypt, such as climate change, water scarcity, and pest infestations, contribute to yield fluctuations, highlighting the need for accurate and timely predictions of wheat productivity. To address this need, time series analysis is employed, which involves analyzing data collected at regular intervals over a specific time frame. Time series data can encompass multiple variables recorded simultaneously at each interval, known as multivariate time series data. Traditional statistical methods have been commonly used for time series analysis. However, these methods have limitations when dealing with nonlinear and complex data patterns, especially in agricultural data exhibiting spatiotemporal characteristics. Deep learning techniques have emerged as a promising solution to address these limitations. In this paper, we have developed the Multivariate Multi-Head Attention Temporal Convolutional Networks (MATCN) model specifically to handle the spatiotemporal nature of multivariate time series data in the context of wheat crop productivity prediction in Egypt. The experimental results revealed that our proposed MATCN model exhibited the most effective prediction performance when compared to other state-of-the-art models.

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LSTM Filter for Smart Agriculture

Cited by 2 publications

References 6 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

Time series forecasting of wheat crop productivity in Egypt using deep learning techniques

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