[论文解读] A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust

Giuliano L. Manso, Helder Knidel+2 more|arXiv (Cornell University)|Mar 19, 2019

Smart Agriculture and AI参考文献 28被引用 36

一句话总结

该论文提出一个智能手机应用程序，通过叶片图像检测咖啡叶锈病和咖啡叶蠹，从叶片分割、特征提取、用人工神经网络/极限学习机进行分类，并计算病害严重程度。

ABSTRACT

Generally, the identification and classification of plant diseases and/or pests are performed by an expert . One of the problems facing coffee farmers in Brazil is crop infestation, particularly by leaf rust Hemileia vastatrix and leaf miner Leucoptera coffeella. The progression of the diseases and or pests occurs spatially and temporarily. So, it is very important to automatically identify the degree of severity. The main goal of this article consists on the development of a method and its i implementation as an App that allow the detection of the foliar damages from images of coffee leaf that are captured using a smartphone, and identify whether it is rust or leaf miner, and in turn the calculation of its severity degree. The method consists of identifying a leaf from the image and separates it from the background with the use of a segmentation algorithm. In the segmentation process, various types of backgrounds for the image using the HSV and YCbCr color spaces are tested. In the segmentation of foliar damages, the Otsu algorithm and the iterative threshold algorithm, in the YCgCr color space, have been used and compared to k-means. Next, features of the segmented foliar damages are calculated. For the classification, artificial neural network trained with extreme learning machine have been used. The results obtained shows the feasibility and effectiveness of the approach to identify and classify foliar damages, and the automatic calculation of the severity. The results obtained are very promising according to experts.

研究动机与目标

• 促使自动识别咖啡叶病虫以帮助农户。
• 开发一种移动方法，通过智能手机图像检测叶面损伤并将锈病与叶蠹区分。
• 实现把损伤面积作为叶面积的百分比自动计算。
• 比较分割方法和特征集，以支持鲁棒分类。

提出的方法

• 使用颜色空间变换（HSV、YCbCr、YCgCr）和阈值（Otsu、迭代）进行背景测试，从图像中分割叶片。
• 评估多种分割方法（Otsu、k-means、YCgCr阈值化），以最大化叶片准确性。
• 从受损区域提取纹理（GLCM）和颜色特征（均值、标准差、方差、熵），并计算共现指标（对比度、相异性、同质性、能量、相关性）。
• 使用人工神经网络（反向传播）和极限学习机（ELM）对受损叶片区域进行咖啡叶蠹或咖啡叶锈的分类。
• 使用分割结果的像素级面积测量，计算严重性为（损坏面积/叶面积）×100。
• 使用在智能手机（ASUS Zenfone 2）拍摄的图像数据集，并为分类建立平衡的训练集。

实验结果

研究问题

• RQ1 基于智能手机的管道是否能够在叶片图像中准确区分咖啡叶锈和咖啡叶蠹？
• RQ2 哪些分割和特征提取策略能最好地界定损坏区域以实现可靠的严重性估计？
• RQ3 对于这一个二类任务，ANN（反向传播）和极限学习机分类器的比较如何？
• RQ4 计算的严重性对背景变化和图像条件的鲁棒性如何？

主要发现

• 该方法在叶面损伤的自动检测和分类方面展现出可行性与有效性。
• 研究表明背景变化和颜色空间的选择会影响分割质量，评估了基于Otsu和YCgCr驱动的方法。
• 来自受损区域的纹理和颜色特征，结合ANN/ELM分类器，能够区分叶锈和叶蠹。
• 可以从分割区域自动计算严重性，实现随时间的客观监测。
• 专家对结果给予非常有希望的评价，支持移动解决方案的可行性。