Tea diseases detection based on fast infrared thermal image processing technology.

Abstract

Background As one of China's important economic crops, tea is economically damaged due to its large yield. The overall goal of this study is to develop an effective, simple, apt computer vision algorithm to detect tea disease area using infrared thermal image processing techniques and to estimate tea disease. Results This paper finds that the area of tea disease has certain regularity with its infrared image gray distribution. Using this rule, we extracted two characteristic parameters into a classifier to help achieve rapid tea disease detection, which increases the accuracy of detection a small amount. The tea disease detection algorithm consisted of the following steps: classify canopy infrared thermal image; convert red, green and blue image to hue, saturation and value; thresholding; color identification; noise filtering; binarization; closed operation; and counting. A correlation coefficient R 2 of 0.97 was obtained between the tea disease detection algorithm and counting performed through human observation, which is 2% higher than traditional algorithms without classifiers. Conclusions This article provides guidance for monitoring the condition of tea gardens with airborne thermal imaging cameras. © 2019 Society of Chemical Industry.

Plant phenotyping relevance

赤外線熱画像から茶樹の病害領域を抽出・推定する画像処理アルゴリズムを開発し、人手計数と比較検証しており、植物病害状態の取得手法が中心である。

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