Pretraga

Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 1200089

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

Rathod, Santosha; Yerram, Sridhar; Arya Prawin; Katti, Gururaj; Rani, Jhansi; Padmakumari, Ayyagari Phani; Somasekhar, Nethi; Padmavathi, Chintalapati; Ondrasek, Gabrijel; Amudan, Srinivasan et al.
Climate-Based Modeling and Prediction of Rice Gall Midge Populations Using Count Time Series and Machine Learning Approaches // Agronomy, 12 (2022), 1; 22, 16 doi:10.3390/agronomy12010022 (međunarodna recenzija, članak, znanstveni)

CROSBI ID: 1200089 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

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

Autori
Rathod, Santosha ; Yerram, Sridhar ; Arya Prawin ; Katti, Gururaj ; Rani, Jhansi ; Padmakumari, Ayyagari Phani ; Somasekhar, Nethi ; Padmavathi, Chintalapati ; Ondrasek, Gabrijel ; Amudan, Srinivasan ; Malathi, Seetalam ; Rao, Nalla Mallikarjuna ; Karthikeyan, Kolandhaivelu ; Mandawi, Nemichand ; Muthuraman, Pitchiahpillai ; Sundaram, Raman Meenakshi

Izvornik
Agronomy (2073-4395) 12 (2022), 1; 22, 16

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
rice gall midge ; light trap catches ; climatological parameters ; INGARCHX ; SVRX ; ANNX

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Poljoprivreda (agronomija)



POVEZANOST RADA

Ustanove:
Agronomski fakultet, Zagreb

Profili:

Avatar Url Gabrijel Ondrašek (autor)

Poveznice na cjeloviti tekst rada:

doi www.mdpi.com

Citiraj ovu publikaciju:

Rathod, Santosha; Yerram, Sridhar; Arya Prawin; Katti, Gururaj; Rani, Jhansi; Padmakumari, Ayyagari Phani; Somasekhar, Nethi; Padmavathi, Chintalapati; Ondrasek, Gabrijel; Amudan, Srinivasan et al.
Climate-Based Modeling and Prediction of Rice Gall Midge Populations Using Count Time Series and Machine Learning Approaches // Agronomy, 12 (2022), 1; 22, 16 doi:10.3390/agronomy12010022 (međunarodna recenzija, članak, znanstveni)
Rathod, S., Yerram, S., Arya Prawin, Katti, G., Rani, J., Padmakumari, A., Somasekhar, N., Padmavathi, C., Ondrasek, G. & Amudan, S. (2022) Climate-Based Modeling and Prediction of Rice Gall Midge Populations Using Count Time Series and Machine Learning Approaches. Agronomy, 12 (1), 22, 16 doi:10.3390/agronomy12010022.
@article{article, author = {Rathod, Santosha and Yerram, Sridhar and Katti, Gururaj and Rani, Jhansi and Padmakumari, Ayyagari Phani and Somasekhar, Nethi and Padmavathi, Chintalapati and Ondrasek, Gabrijel and Amudan, Srinivasan and Malathi, Seetalam and Rao, Nalla Mallikarjuna and Karthikeyan, Kolandhaivelu and Mandawi, Nemichand and Muthuraman, Pitchiahpillai and Sundaram, Raman Meenakshi}, year = {2022}, pages = {16}, DOI = {10.3390/agronomy12010022}, chapter = {22}, keywords = {rice gall midge, light trap catches, climatological parameters, INGARCHX, SVRX, ANNX}, journal = {Agronomy}, doi = {10.3390/agronomy12010022}, volume = {12}, number = {1}, issn = {2073-4395}, title = {Climate-Based Modeling and Prediction of Rice Gall Midge Populations Using Count Time Series and Machine Learning Approaches}, keyword = {rice gall midge, light trap catches, climatological parameters, INGARCHX, SVRX, ANNX}, chapternumber = {22} }
@article{article, author = {Rathod, Santosha and Yerram, Sridhar and Katti, Gururaj and Rani, Jhansi and Padmakumari, Ayyagari Phani and Somasekhar, Nethi and Padmavathi, Chintalapati and Ondrasek, Gabrijel and Amudan, Srinivasan and Malathi, Seetalam and Rao, Nalla Mallikarjuna and Karthikeyan, Kolandhaivelu and Mandawi, Nemichand and Muthuraman, Pitchiahpillai and Sundaram, Raman Meenakshi}, year = {2022}, pages = {16}, DOI = {10.3390/agronomy12010022}, chapter = {22}, keywords = {rice gall midge, light trap catches, climatological parameters, INGARCHX, SVRX, ANNX}, journal = {Agronomy}, doi = {10.3390/agronomy12010022}, volume = {12}, number = {1}, issn = {2073-4395}, title = {Climate-Based Modeling and Prediction of Rice Gall Midge Populations Using Count Time Series and Machine Learning Approaches}, keyword = {rice gall midge, light trap catches, climatological parameters, INGARCHX, SVRX, ANNX}, chapternumber = {22} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus

Citati:

    Altmetrijski pokazatelji:


    Podijeli:





    Contrast
    Increase Font
    Decrease Font
    Dyslexic Font
    CROSBI koristi kolačiće (cookies) kako bi poboljšao funkcionalnost stranice.