LAUSR

BACKGROUND Pests cause significant damage to agricultural crops and reduce the crop yields. Using manual pest forecasting methods for integrated pest management is a labor intensive and time-consuming process. Here, we present an automatic system for monitoring pests in large fields, with the aim of replacing manual forecasting. The system comprises an automatic detection and counting system and a human-computer data statistical fitting system. Image datasets of the target pests from large fields are first input into the system. Then, the number of pests in the image is counted both manually and using the automatic system. Finally, a mapping relationship between the counts obtained by the automated system and agricultural experts is established using the statistical fitting system. RESULTS The results showed that the trends in the pest-count curves produced by the manual and automated counting methods were very similar. To sample the number of pests for the manual statistics, the plants were shaken to transfer the pests from the plant to a plate. Hence, pests hiding within the crevices of plants were also sampled and included in the count, while the automatic method counted only the pests visible in the images. Therefore, the computer index threshold was much lower than the manual index threshold. However, the proposed system still, correctly reflected the trends in pest numbers obtained using computer vision. CONCLUSION The experimental results demonstrate that our automatic pest monitoring system can automatically generate pest grades and can replace manual forecasting methods in large fields. This article is protected by copyright. All rights reserved.