Urban street trees bring the beautiful ecological environment for human beings, but also may harm human health. Tree pollen is an important allergen that causes people to suffer from asthma and rhinitis, causing a serious medical burden. In order to protect human health and reduce medical costs, urban street trees need to be accurately identified. However, the identification of the urban street tree is influenced by the natural image (images with light intensity, season and shooting conditions), tree characteristics and identification models. To solve the problem, we proposed an interpretation model for identifying tree from natural images, named Ev2S_SHAP. Then, we applied the method to explain the influence of environmental and leaf factors on tree recognition and the identification accuracy. The open-source natural image dataset of urban street tree with complex situations such as different angles, distances, light, and times was used as the research object to verify the proposed tree identification model. The results showed that the overall evaluation of the identification results for 50 tree species: recall, precision, accuracy, and F1 score were 98.12 %, 98.18 %, 98.11 %, and 98.13 %, respectively. The identification accuracy of deciduous shrubs, evergreen shrubs, deciduous trees, and evergreen trees was 98.85 %, 98.47 %, 98.76 %, and 96.38 %, respectively. Complex lights and angles, long-range shooting, and winter conditions weakened the extraction of leaf features and were not conducive to the identification of tree species. Leaf shape characteristics had important influence on tree species identification. The effects of circularity, minimum circumcircle, and perimeter on tree identification accounted for 94.36 %, 75.61 %, and 69.12 %, respectively. Circularity was positively correlated to the identification contribution of elliptic leaf species, but opposite to that of lanceolate leaf species. The perimeter contributed positively correlated to the identification of lanceolate leaf species, but the minimum circumcircle was negatively. The Ev2S-SHAP effectively improved the identification accuracy of tree species. The study provides an innovative method for identifying and interpreting tree species.

中文翻译：

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基于自然影像的城市街道树种识别与因子解释模型


城市行道树为人类带来了优美的生态环境，但也可能危害人类健康。树花粉是一种重要的过敏原，会导致人们患上哮喘和鼻炎，造成严重的医疗负担。为了保护人类健康并降低医疗费用，需要准确识别城市行道树。然而，城市行道树的识别受到自然图像（具有光照强度、季节和拍摄条件的图像）、树木特征和识别模型的影响。为了解决这个问题，我们提出了一种从 Natural Images 中识别树木的解释模型，名为 Ev2S_SHAP。然后，我们应用该方法解释环境和叶片因素对树木识别和识别准确性的影响。以不同角度、距离、光线、时间等复杂情况的城市行道树开源自然影像数据集为研究对象，验证了所提出的树木识别模型。结果表明，对 50 个树种的鉴定结果：召回率、精确率、准确率和 F1 评分的总体评价分别为 98.12 %、98.18 %、98.11 % 和 98.13 %。落叶灌木、常绿灌木、落叶乔木和常绿乔木的识别准确率分别为 98.85 %、98.47 %、98.76 % 和 96.38 %。复杂的光线和角度、远距离拍摄和冬季条件削弱了叶特征的提取，不利于树种的识别。叶形特征对树种鉴定有重要影响。圆度、最小圆周和周长对树木识别的影响分别占 94.36 % 、 75.61 % 和 69.12 %。 圆度与椭圆形叶属的鉴定贡献呈正相关，但与披针形叶属相反。周长与披针形叶种的鉴定呈正相关，但最小外接圈呈负相关。Ev2S-SHAP 有效提高了树种的识别精度。该研究为识别和解释树种提供了一种创新方法。