Modelling and Forecasting of Greenhouse Whitefly Incidence Using Time-Series and ARIMAX Analysis

Chiu, Lin‐Ya; Rustia, Dan Jeric Arcega; Lu, Chen-Yi; Lin, Ta‐Te

doi:10.1016/j.ifacol.2019.12.521

Cited by 28 publications

(19 citation statements)

References 8 publications

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“…Building a model for prediction of plant infestation with pests is of paramount importance in control these pests either in greenhouses (Chiu et al, 2019) or fields (Skawsang et al, 2019). Such a model may provide a tool for the perfect timing in the prevention of the damaging effects of the pests on the plant which leads to an increase in the crop yield.…”

Section: Discussionmentioning

confidence: 99%

Applying Machine Learning Technology in the Prediction of Crop Infestation with Cotton Leafworm in Greenhouse

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Mostafa

et al. 2020

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The use of technology in agriculture has grown in recent years with the era of data analytics affecting every industry. The main challenge in using technology in agriculture is identification of effectiveness of big data analytics algorithms and their application methods. Pest management is one of the most important problems facing farmers. The cotton leafworm, Spodoptera littoralis (Boisd.) (CLW) is one of the major polyphagous key pests attacking plants includes 73 species recorded at Egypt. In the present study, several machine learning algorithms have been implemented to predict plant infestation with CLW. The moth of CLW data was weekly collected for two years in a commercial hydroponic greenhouse. Furthermore, among other features temperature and relative humidity were recorded over the total period of the study. It was proven that the XGBoost algorithm is the most effective algorithm applied in this study. Prediction accuracy of 84 % has been achieved using this algorithm. The impact of environmental features on the prediction accuracy was compared with each other to ensure a complete dataset for future results. In conclusion, the present study provided a framework for applying machine learning in the prediction of plant infestation with the CLW in the greenhouses. Based on this framework, further studies with continuous measurements are warranted to achieve greater accuracy.

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Section: Discussionmentioning

confidence: 99%

Applying Machine Learning Technology in the Prediction of Crop Infestation with Cotton Leafworm in Greenhouse

Tagel-Din

Adly

Mostafa

et al. 2020

Preprint

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“…Correlations coefflclents of the study with published literature due to absence of time series based analysis in India on mango hoppers however, the time series based analysis extensively applied on data series of litchi stink bugs, greenhouse whitefly, Trialeurodes vaporariorum, leopard moth, Zeuzera pyrina etc. (Boopathi et al 2015;Chiu et al, 2019;Hegazi et al, 2014) The proposed hybrid model is easier to use than the other models found in the literature for mango hopper prediction in other ecologies (Debnath et al, 2013;Pushplatha et al, 2008) and can improve the applicability to the end users for management of mango hoppers.…”

Section: Autocorrelations Partial Autocorrelationsmentioning

confidence: 96%

Prediction of mango hopper, Idioscopus nitidulus (Walker) using hybrid modelling in Konkan region

et al. 2021

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Mango hopper, Idioscopus nitidulus is the most destructive pest of mango in the India. Thus, aim of the study was to develop precise and easy early population prediction model of mango hopper for tropical mansoon climate conditions. Weekly occurrence data of mango hopper, I. nitidulus during five consecutive years (2014 to 2018) was used for developing hybrid of multiplicative seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) model. The population of I. nitidulus increases in the month of January-February on flower panicles and October-November on new vegetative shoots in the region. The linearity in the time series data was best fitted with SARIMA (0, 0, 2) × (0, 1, 1)52 model as their correlation values are not outside the confidence intervals (CI) limits. Further ANN modeling was done for fitting the SARIMA residuals. The fitted values of model prediction and the actual values of year 2017-2018 flowering season (SMW36-52 of 2017 and SMW 1-13 of 2018) were used for testing of prediction efficiency. The performance of the two models in respect to model fitting and effectiveness of SARIMA and hybrid SARIMA-ANN model was compared by evaluating diagnostic statistics of MSE, RMSE, MAE and MAPE. The best fitted developed hybrid model in present study and the data predicted by model was matched with actual data of mango hopper incidence during the year 2017-18. Hybrid model developed in this study will help to predict hoppers population in advance, thus provide a direction for planning of timely prevention and development of effective management strategies which will help to minimize the use of hazardous pesticides.

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“…This research proves that the best model for predicting the incidence of whiteflies was ARIMAX, with temperature and humidity included as the exogenous factors. [8] (Farih, L. N., et al, 2019) This thesis aims to estimate the total departure of ship customers in the main port of Makassar working the ARIMAX method with the effects of calendar variations. Moreover, the ARIMAX method is a system that can be used when there are exogenous variables, where in this example, the exogenous variable is in the form of a variable dummy which is the Eid holidays.…”

Section: Objective Of the Studymentioning

confidence: 99%

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Modelling and Forecasting of Greenhouse Whitefly Incidence Using Time-Series and ARIMAX Analysis

Cited by 28 publications

References 8 publications

Applying Machine Learning Technology in the Prediction of Crop Infestation with Cotton Leafworm in Greenhouse

Applying Machine Learning Technology in the Prediction of Crop Infestation with Cotton Leafworm in Greenhouse

Prediction of mango hopper, Idioscopus nitidulus (Walker) using hybrid modelling in Konkan region

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