Record Details

Climate-Based Modeling and Prediction of Rice Gall Midge Populations Using Count Time Series and Machine Learning Approaches

KRISHI: Publication and Data Inventory Repository

View Archive Info
 
 
Field Value
 
Title Climate-Based Modeling and Prediction of Rice Gall Midge Populations Using Count Time Series and Machine Learning Approaches
Not Available
 
Creator Santosha Rathod , Sridhar Yerram , Prawin Arya , Gururaj Katti , Jhansi Rani , Ayyagari Phani Padmakumari , Nethi Somasekhar , Chintalapati Padmavathi , Gabrijel Ondrasek , Srinivasan Amudan , Seetalam Malathi , Nalla Mallikarjuna Rao , Kolandhaivelu Karthikeyan , Nemichand Mandawi , Pitchiahpillai Muthuraman and Raman Meenakshi Sundaram
 
Subject rice gall midge; light trap catches; climatological parameters; INGARCHX; SVRX; ANNX
 
Description Not Available
The Asian rice gall midge (Orseolia oryzae (Wood-Mason)) is a major insect pest in rice
cultivation. Therefore, development of a reliable system for the timely prediction of this insect
would be a valuable tool in pest management. In this study, occurring between the period from
2013–2018: (i) gall midge populations were recorded using a light trap with an incandescent bulb, and
(ii) climatological parameters (air temperature, air relative humidity, rainfall and insulations) were
measured at four intensive rice cropping agroecosystems that are endemic for gall midge incidence
in India. In addition, weekly cumulative trapped gall midge populations and weekly averages of
climatological data were subjected to count time series (Integer-valued Generalized Autoregressive
Conditional Heteroscedastic—INGARCH) and machine learning (Artificial Neural Network—ANN,
and Support Vector Regression—SVR) models. The empirical results revealed that the ANN with
exogenous variable (ANNX) model outperformed INGRACH with exogenous variable (INGRCHX)
and SVR with exogenous variable (SVRX) models in the prediction of gall midge populations in both
training and testing data sets. Moreover, the Diebold–Mariano (DM) test confirmed the significant
superiority of the ANNX model over INGARCHX and SVRX models in modeling and predicting rice
gall midge populations. Utilizing the presented efficient early warning system based on a robust
statistical model to predict the build-up of gall midge population could greatly contribute to the
design and implementation of both proactive and more sustainable site-specific pest management
strategies to avoid significant rice yield losses.
Not Available
 
Date 2022-04-05T13:50:04Z
2022-04-05T13:50:04Z
2021-12-23
 
Type Research Paper
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/71196
 
Language English
 
Relation Not Available;
 
Publisher Not Available