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Vision-based system for detecting grapevine yellow diseases using artificial intelligence

机译：基于视觉的方法，用于使用人工智能检测葡萄黄疾病的系统

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摘要

Grapevine yellows (GY) of grapes, a critical threat to grapevines because of the severe symptoms and the lack of healing treatments, has been detected worldwide. The detection of GY diseases is a very difficult and time consuming task, and relies on symptoms identification, which are very similar with other diseases. Additionally, the analysis of asymptomatic GY-infected grapes could lead to high rates of false-negative due of the low concentration of the pathogen in the host. Herein, we present a supporting vision-based tool for GY disease detection using artificial intelligence (AI) and machine learning (ML). Leaves of bois noir-infected plants (previously tested by qPCR) were collected in July-October, 2017. Grapevine yellow was detected in a data set of 322 images and six diseases. Other than grapevine yellow, the diseases include downy mildew, esca disease, grapevine leafroll, powdery mildew and Stictocephala bisonia. A linear support vector machine (SVM) classified features from a pre-trainedconvolutional neural network - AlexNet trained on ImageNet. The system obtains a 95.23% accuracy and a Matthew's correlation coefficient of 0.832. For reference, a baseline system with local binary patterns (LBP) and color histogram with a SVM obtains only 26.7% and -0.124, respectively. Our work shows promise for automatic detection of grapevine yellow by computers. Future work will focus on improving the sensitivity of the system and implementation on drones with Nvidia Jetson. This system could reduce the rate of false positive/negative in large-scale vineyard monitoring.
机译：在全世界发现，在全球范围内发现了葡萄葡萄（GY）葡萄，对葡萄藤的危险威胁，并在全球范围内检测到缺乏治疗治疗。 GY疾病的检测是一种非常困难和耗时的任务，并依赖于症状鉴定，与其他疾病非常相似。另外，无症状GY感染葡萄的分析可能导致宿主低浓度低浓度的假阴性率高。在此，我们介绍了使用人工智能（AI）和机器学习（ML）的GY疾病检测的支持视觉型工具。 2017年7月至10月收集了Bois Noir感染植物的叶子（以前通过QPCR测试）。在322个图像和六种疾病的数据集中检测到葡萄黄。除了葡萄黄黄色之外，疾病包括柔软的霉菌，ESCA病，葡萄叶·塞森林，粉状霉菌和Stictocephala Bisonia。从预训练前的神经网络的线性支持向量机（SVM）分类功能 - ImageNet培训的AlexNet。该系统获得95.23％的精度和Matthew的相关系数为0.832。参考，具有局部二进制图案（LBP）和具有SVM的颜色直方图的基线系统，分别仅获得26.7％和-0.124。我们的工作表明，通过计算机自动检测葡萄黄色的承诺。未来的工作将侧重于提高系统的敏感性和NVIDIA Jetson对无人机的敏感性。该系统可以降低大规模葡萄园监测中的假阳性/负率。

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  • 来源
    《Acta Horticulturae》 |2020年第1279期|共6页
  • 作者

    Y. Ampatzidis; A. Cruz; R. Pierro;

  • 作者单位

    Department of Agricultural and Biological Engineering Southwest Florida Research and Education Center University of Florida 2685SR29 Immokalee FL 34142 USA;

    Department of Computer and Electrical Engineering and Computer Science California State University Bakersfield 9001 Stockdale Highway Bakersfield CA 93311 USA;

    Department of Biological and Environmental Sciences and Technologies University of Salento via ProvJe Monteroni 73100 Lecce Italy;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 园艺;
  • 关键词

    grapevine yellows; artificial intelligence; machine learning; deep learning; vision-j based; disease detection;

    机译：葡萄叶片;人工智能;机器学习;深入学习;视觉-J基于疾病检测;

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