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Automatic Detection and Severity Assessment of Crop Diseases Using Image Pattern Recognition

机译：使用图像模式识别的作物病害自动检测和严重性评估

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Disease diagnosis and severity assessment are necessary and critical for predicting the likely crop yield losses, evaluating the economic impact of the disease, and determining whether preventive treatments are worthwhile or particular control strategies could be taken. In this work, we propose to make advances in the field of automatic detection and diagnosis and severity assessment of crop diseases using image pattern recognition. We have developed a two-stage crop disease pattern recognition system which can automatically identify crop diseases and assess sevrity based on combination of marker-controlled watershed segmentation, superpixel based feature analysis and classification. We have conducted experimental evaluation using different feature selection and classification methods. The experimental result shows that the proposed approach can accurately detect crop diseases (i.e. Septoria and Yellow rust, which are the two most important and major types of wheat diseases in UK and across the world) and assess the disease severity with efficient processing speed.
机译：疾病诊断和严重性评估对于预测可能的农作物减产，评估疾病的经济影响以及确定是否值得采取预防性治疗或采取特定控制策略是必要且至关重要的。在这项工作中，我们建议在使用图像模式识别的农作物疾病的自动检测，诊断和严重性评估领域取得进展。我们已经开发了一个两阶段的农作物病害模式识别系统，该系统可以基于标记物控制的分水岭分割，基于超像素的特征分析和分类的组合，自动识别农作物病害并评估严重性。我们使用不同的特征选择和分类方法进行了实验评估。实验结果表明，该方法可以准确地检测出农作物疾病（例如，Septoria和Yellow锈，这是英国和世界范围内两种最重要和最主要的小麦疾病），并且可以通过有效的处理速度来评估疾病的严重程度。

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