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Computer vision based technique for identification and quantification of powdery mildew disease in cherry leaves

机译：基于计算机视觉的樱桃叶白粉病鉴定与定量技术

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摘要

There are different reasons like pests, weeds, and diseases which are responsible for the loss of crop production. Identification and detection of different plant diseases is a difficult task in a large crop field and it also requires an expert manpower. In this paper, the proposed method uses adaptive intensity based thresholding for automatic segmentation of powdery mildew disease which makes this method invariant to image quality and noise. After the segmentation of powdery mildew disease from leaf images, the affected area is quantified which makes this method efficient for grading the level of disease infection. The proposed method is tested on the comprehensive dataset of leaf images of cherry crops, which achieved good accuracy of 99%. The experimental results indicate that proposed method for segmentation of powdery mildew disease affected area from leaf image of cherry crops is convincing and computationally cheap.
机译：有害生物，杂草和疾病等多种原因造成了农作物减产。在大面积作物田中，识别和检测不同的植物病害是一项艰巨的任务，并且还需要专业人员。在本文中，该方法使用基于自适应强度的阈值进行白粉病的自动分割，这使得该方法对图像质量和噪声不变。从叶片图像中分离出白粉病后，对患病区域进行了量化，这使该方法可有效地对疾病的感染程度进行分级。该方法在樱桃作物叶片图像的综合数据集上进行了测试，其准确率达到了99％。实验结果表明，提出的从樱桃作物叶片图像中分离白粉病病害区域的方法是有说服力的，并且计算便宜。

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