Work

Modelling of Jassids (Amrasca biguttula) in Cotton: A Count Time Series Approach

Year: 2024

Type: article

Abstract: This study was aimed to model Jassids population in cotton at Regional Agricultural Research Station (RARS), Nandyal. The secondary standard meteorological weekwise(SMW) data between 2008-2021 was considered based on data availability in the research station. Count time series models and machine learning models are used for modelling the Jassids population dataset Among the models evaluated in the study, the INGARCH-ANN model performed better than the INGARCH, ZIPAR, ZINBAR, and ANN models, according to error comparison metrics (MSE and RMSE). The statistical significance between the models was assessed using the Diebold-Mariano (DM) test. The order of prediction accuracy of the models under consideration is INGARCH-ANN>ANN> ZIPAR >ZINBAR>INGARCH. Overall, the study suggests that employing the Hybrid model could effectively model the jassids population in cotton at RARS, Nandyal.

Source: Journal of Scientific Research and Reports

DOI: 10.9734/jsrr/2024/v30i92388

Authors: B. Venkataviswateja, V. Srinivasa Rao, A. Dhandapani, G. Raghunadha Reddy, D. Ramesh, A.D.V.S.L.P. Anand Kumar, M. Sivarama Krishna

Institutions: Acharya N. G. Ranga Agricultural University, National Academy of Agricultural Research Management, Indian Council of Agricultural Research

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: diamond

OpenAlex ID: W4402278094

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