Early Forecasting of Rice Blast Disease Using Long Short-Term Memory Recurrent Neural Networks

Kim, Yangseon; Roh, Jae Hwan; Kim, Ha Young

doi:10.3390/su10010034

Cited by 67 publications

(50 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The output of the cell can be blocked by the output gate, and all gates use sigmoidal nonlinearity, and the state unit can perform as an extra input to other gating units [45][46][47]. Through this process, the LSTM architecture can solve the problem of long-term dependencies at small computational costs [48].…”

Section: Long Short-term Memory (Lstm) Networkmentioning

confidence: 99%

Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

2018

View full text Add to dashboard Cite

With recent advances in computing technology, massive amounts of data and information are being constantly accumulated. Especially in the field of finance, we have great opportunities to create useful insights by analyzing that information, because the financial market produces a tremendous amount of real-time data, including transaction records. Accordingly, this study intends to develop a novel stock market prediction model using the available financial data. We adopt deep learning technique because of its excellent learning ability from the massive dataset. In this study, we propose a hybrid approach integrating long short-term memory (LSTM) network and genetic algorithm (GA). Heretofore, trial and error based on heuristics is commonly used to estimate the time window size and architectural factors of LSTM network. This research investigates the temporal property of stock market data by suggesting a systematic method to determine the time window size and topology for the LSTM network using GA. To evaluate the proposed hybrid approach, we have chosen daily Korea Stock Price Index (KOSPI) data. The experimental result demonstrates that the hybrid model of LSTM network and GA outperforms the benchmark model.

show abstract

Section: Long Short-term Memory (Lstm) Networkmentioning

confidence: 99%

Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

2018

View full text Add to dashboard Cite

show abstract

“…We also used a neural network-based simulation model with rainfall and temperature as input variables and predicted DRR incidence with an accuracy of 84%. Machine learning methods like ANN 25 – 28 have been rigorously used to understand plant–pathogen–environment interactions and to identify the factors most relevant to disease incidence 14 , 15 . ANN has the advantage of establishing nonlinear relationships and is thus superior to linear regression and multiple linear regression 13 .…”

Section: Discussionmentioning

confidence: 99%

Low soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis

Sinha

Irulappan

Patil³

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Rhizoctonia bataticola causes dry root rot (DRR), a devastating disease in chickpea (Cicer arietinum). DRR incidence increases under water deficit stress and high temperature. However, the roles of other edaphic and environmental factors remain unclear. Here, we performed an artificial neural network (ANN)-based prediction of DRR incidence considering DRR incidence data from previous reports and weather factors. ANN-based prediction using the backpropagation algorithm showed that the combination of total rainfall from November to January of the chickpea-growing season and average maximum temperature of the months October and November is crucial in determining DRR occurrence in chickpea fields. The prediction accuracy of DRR incidence was 84.6% with the validation dataset. Field trials at seven different locations in India with combination of low soil moisture and pathogen stress treatments confirmed the impact of low soil moisture on DRR incidence under different agroclimatic zones and helped in determining the correlation of soil factors with DRR incidence. Soil phosphorus, potassium, organic carbon, and clay content were positively correlated with DRR incidence, while soil silt content was negatively correlated. Our results establish the role of edaphic and other weather factors in chickpea DRR disease incidence. Our ANN-based model will allow the location-specific prediction of DRR incidence, enabling efficient decision-making in chickpea cultivation to minimize yield loss.

show abstract

“…LSTM was designed to overcome this issue through cell-and-gate structure, which enables the LSTM to learn when they forget and update memory 46 . LSTM has a better performance than tradition statistical models and has been applied in predicting such as emotional state, traffic flow, and disease, especially when combined with convolutional neural networks 47–49 .…”

Section: Methodsmentioning

confidence: 99%

A method for hand-foot-mouth disease prediction using GeoDetector and LSTM model in Guangxi, China

Liang

Song

et al. 2019

Sci Rep

View full text Add to dashboard Cite

Hand-foot-mouth disease (HFMD) is a common infectious disease in children and is particularly severe in Guangxi, China. Meteorological conditions are known to play a pivotal role in the HFMD. Previous studies have reported numerous models to predict the incidence of HFMD. In this study, we proposed a new method for the HFMD prediction using GeoDetector and a Long Short-Term Memory neural network (LSTM). The daily meteorological factors and HFMD records in Guangxi during 2014–2015 were adopted. First, potential risk factors for the occurrence of HFMD were identified based on the GeoDetector. Then, region-specific prediction models were developed in 14 administrative regions of Guangxi, China using an optimized three-layer LSTM model. Prediction results (the R-square ranges from 0.39 to 0.71) showed that the model proposed in this study had a good performance in HFMD predictions. This model could provide support for the prevention and control of HFMD. Moreover, this model could also be extended to the time series prediction of other infectious diseases.

show abstract

Early Forecasting of Rice Blast Disease Using Long Short-Term Memory Recurrent Neural Networks

Cited by 67 publications

References 30 publications

Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

Low soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis

A method for hand-foot-mouth disease prediction using GeoDetector and LSTM model in Guangxi, China

Contact Info

Product

Resources

About