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首页> 外文期刊>Applied Engineering in Agriculture >MOBILE-DEVICE BASED IMAGE PROCESSING FOR RICE BROWN PLANTHOPPER CLASSIFICATION AND OUTBREAK MONITORING
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MOBILE-DEVICE BASED IMAGE PROCESSING FOR RICE BROWN PLANTHOPPER CLASSIFICATION AND OUTBREAK MONITORING

机译：基于移动设备的大米棕色Planthopper分类和爆发监测的图像处理

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摘要

The rice brown planthopper (BPH) outbreak is one of several causes of damage to rice crops in Thailand. A traditional way to monitor the early outbreak is to routinely and randomly count the density of BPHs spreading around the rice field. This article presents an assistive tool to monitor the BPH by using automatic image processing. Smart phone devices with a sufficient camera quality are currently affordable and convenient for farmers to capture images from their rice fields. Based on the Support Vector Machines algorithm trained on color and Gray Level Co-occurrence Matrix (GLCM) image features, the proposed system not only automatically detects the position of BPHs in the collected images, but is also able to classify the life stage of each hopper. The use of a red-frame mark on the camera screen to guide BPH image capturing helps improving the overall processing accuracy. Field experiments with the Rice Department of the Ministry of Agriculture and Cooperatives of Thailand shows the proposed system achieved an approximately 89% detection F-measure and an 87% BPH life-stage classification accuracy. Moreover, this article illustrates the preciseness of BPH density prediction with respect to the different numbers of sampling images from the rice field. The result suggests farmers to take at least 40 images per 1,600 square meters in order to gain more than 87% prediction accuracy.
机译：水稻棕色Planthopper（BPH）爆发是泰国稻田损伤的几种原因之一。一种传统的监测早期爆发的方式是经常和随机地计算稻田周围的BPH的密度。本文介绍了一种辅助工具，通过使用自动图像处理来监控BPH。具有足够的相机质量的智能手机设备目前负担得起，方便农民从稻田中捕获图像。基于验证的彩色和灰度级共发生矩阵（GLCM）图像特征的支持向量机算法，所提出的系统不仅自动检测收集图像中BPH的位置，而且还能够对每个的寿命分类料斗。在相机屏幕上使用红色框架标记来指导BPH图像捕获有助于提高整体处理精度。泰国农业部稻米部的现场实验表明，拟议的系统检测约89％的检测法衡量和87％的BPH寿命分类准确性。此外，本文说明了BPH密度对来自稻田的不同数量的采样图像的预测的精确性。结果表明农民每1600平方米至少花费40张图像，以获得超过87％的预测精度。

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