4.7 Article

Toward anticipating pest responses to fruit farms: Revealing factors influencing the population dynamics of the Oriental Fruit Fly via automatic field monitoring

Journal

COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 109, Issue -, Pages 148-161

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2014.09.018

Keywords

Information and comniunications technology; Wireless sensor network; Oriental Fruit Fly; Bactrocera dorsalis; Population dynamics; Forecast modeling

Funding

  1. National Science Council of the Executive Yuan
  2. Council of Agriculture of the Executive Yuan, Taiwan [NSC 102-3113-P-002-037, NSC 101-2221-E-002-149-MY3, 02AS-7.1.2-BQ-B1]
  3. National Science Council
  4. National Taiwan University
  5. Intel Corporation [NSC 1012911-1-002-001, NTU 102R7501, NTU 102R7616-2]

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The Oriental Fruit Fly (OFF), Bactrocera dorsalis (Hendel), is one of most devastating insect pests that have periodically caused serious damage to fruit farms in Taiwan and many countries in the world. In the past, many studies reported that the population dynamics of OFF was partially correlated to the weather and the historical population development of OFF in the field. By making the best use of modern info-communication technologies (ICTs), long-term pest population data and microclimate variables measured with uniquely fine spatiotemporal resolution are now available to reveal the population dynamics of OFF. An analysis of data over three years using the Vector Auto-Regressive and Moving-Average model with exogenous variables (VARMAX) was proposed to unravel the regulatory mechanism between the population dynamics of OFF and microclimate factors. In addition, the proposed model provides a 7-day forecast for population dynamics of OFF. The accuracy of 7-day risk level forecasting yielded by the proposed model ranges from 0.87 to 0.97, and the average root-mean squared errors of forecasting the population dynamics fall in the interval between 0.31 and 4.95 per day per farm. The proposed forecasting model can allow authorities to gain a better understanding of the dynamics of OFF and anticipate pest-related problems, so they can make preemptive and effective pest management decisions before the real problems occur. (C) 2014 Elsevier B.V. All rights reserved.

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