Development of an autonomous early warning system for Bactrocera dorsalis (Hendel) outbreaks in remote fruit orchards
Journal
Computers and Electronics in Agriculture
Journal Volume
88
Pages
1-12
Date Issued
2012
Author(s)
Abstract
Developing an autonomous early warning system for detecting pest resurgence is an essential task to reduce the probabilities of massive Oriental fruit fly (Bactrocera dorsalis (Hendel)) outbreaks. By preventing pest outbreaks, farmers would be able to reduce their dependence on chemical pesticides. Chemical pesticide abuse often brings harmful consequences to human health and natural environments. Since an agroecological system can change at a fast rate due to the soil degradation and the environmental factors changes, the rise of pest density cannot be immediately detected by traditional methodologies. In this study, an autonomous early warning system, built upon the basis of wireless sensor networks and GSM networks, is presented to effectively capture long-term and up-to-the-minute natural environmental fluctuations in fruit farms. In addition, two machine learning techniques, self-organizing maps and support vector machines, are incorporated to perform adaptive learning and automatically issue a warning message to farmers and government officials via GSM networks when the population density of B. dorsalis significantly rises. The proposed system also provides sensor fault warning messages to system administrators when one or more faulty sensors give abnormal readings to the system. Then, farmers and government officials would be able to take precautionary actions in time before major pest outbreaks cause an extensive crop loss, as well as to schedule maintenance tasks to repair faulted devices. The experimental results indicate that the proposed early warning system is able to detect the incidents of possible pest outbreaks in a variety of seasonal conditions with sensitivity, specificity, accuracy, and precision around 98%, 100%, 100%, and 100%, respectively, as well as to transmit the early warning messages to farmers and government officials via Short Message Service using the GSM network. The proposed early warning system can be easily adopted in different fruit farms without extra efforts from farmers and government officials since it is built based on machine learning techniques, and the warning messages are delivered to their mobile phones as text messages. The proposed early warning system also shows great potential to assist farmers to update their pest control operations in the fruit farms, and help government officials to improve farming systems. © 2012 Elsevier B.V.
Subjects
Agricultural management; Early warning system; Oriental fruit fly; Pest monitoring; Wireless sensor networks
Other Subjects
Adaptive learning; Agricultural management; Chemical pesticides; Control operations; Crop loss; Early warning; Early Warning System; Environmental factors; Environmental fluctuations; Farming system; Fast rate; Faulty sensor; Fruit flies; Government officials; GSM networks; Human health; Maintenance tasks; Natural environments; On-machines; Population densities; Sensor fault; Short message services; Soil degradation; System administrators; Text messages; Traditional methodologies; Two machines; Warning messages; Conformal mapping; Degradation; Fruits; Learning algorithms; Learning systems; Orchards; Pest control; Population statistics; Repair; Sensors; Telephone systems; Wireless sensor networks; Agriculture; crop damage; environmental factor; fly; fruit; orchard; pest control; pesticide; plant-herbivore interaction; population density; population outbreak; sensitivity analysis; soil degradation; state role; Bactrocera dorsalis
Type
journal article