Predicting Rice Pest Population Occurrence with Satellite-Derived Crop Phenology, Ground Meteorological Observation, and Machine Learning: A Case Study for the Central Plain of Thailand

Sukij Skawsang

Masahiko Nagai

Nitin K. Tripathi and Peeyush Soni

Resumen

Integration of ground-based weather variables, satellite-derived host-plant phenology and ANN modelling were applied for rice pest warning and prediction to support an integrated pest management (IPM) programme in the central plain of Thailand.

Palabras claves

brown planthopper - host-plant phenology - weather - satellite - remote sensing - multiple linear regression - artificial neural network - random forest

Acceso

P�GINAS

pp. 0 - 0

N�MERO

Volumen: 9 Parte: 22 (2019)

MATERIAS

INGENIER�A Y CONSTRUCCI�N CIVIL
TECNOLOG�A

REVISTAS SIMILARES

Applied Sciences
Aceh International Journal of Science and Technology
Periodica Polytechnica: Chemical Engineering

DOI

https://doi.org/10.3390/app9224846

Predicting Rice Pest Population Occurrence with Satellite-Derived Crop Phenology, Ground Meteorological Observation, and Machine Learning: A Case Study for the Central Plain of Thailand

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