Treffer: 苹果树叶多病害及不可辨别病害的轻量识别算法.

Detection technology has been widely used for apple leaf diseases and pests in recent years, with the rapid development of deep learning. However, most of the existing identification equipment is deployed in the field, with limited network conditions and computing resources, whereas, a wide variety of diseases and pests, with limited recognition accuracy, which is not conducive to the development of digital agriculture. In this study, a fast identification (S-DenseNet-E) was proposed to realize the best effect on public data sets. The DenseNet's Dense module was selected to add an auxiliary model, in order to facilitate the identification of indistinguishable diseases. The S-DenseNet model was designed for the multi-diseases using the DenseNet121. The aggregation connection was adopted only in the output layer of the module in the S-DenseNet. The high reasoning speed of DenseNet121 was achieved to solve the dense connection. The average all-category F1-score of SDenseNet was 85.14%, the inference time was 33.03 ms, and the number of parameters was 1.1 M. Compared with DenseNet121, the average F1-score of all categories was improved by 0.13 by 4.28, the inference time was shortened by 32.87 ms, and the number of parameters was reduced by 6.96 M. An auxiliary model was used to assist S-DenseNet to identify the indistinguishable disease, which was built S-DenseNet-E of composite recognition framework. Furthermore, the one-vote strategy was adopted under two-model voting. The S-DenseNet-E maintained the high recognition of the S-DenseNet for the multi-diseases, while the effective recognition of the indistinguishable disease indicated a low inference time and small parameters. S-DenseNet-E achieved an average F1-score of 85.86% in all categories, an F1-score of 70.10% for indistinguishable disease, an inference time of 38.92 ms, and a parameter of 2.2 M. Compared with the S-DenseNet, the SDenseNet-E improved the F1-score by 4.28 on indistinguishable diseases, and the inference time by 5.89 ms. Compared with the DenseNet121, S-DenseNet-E shared an average F1-score improvement of 0.85 percentage points in all categories, an F1- score improvement of 5.18 percentage points in indistinguishable disease, a reduction in the number of parameters by 5.86 M, where the inference time was reduced by 26.98 ms. Therefore, the S-DenseNet-E presented better recognition performance for two complex situations, namely, apples suffering from multiple diseases and unidentifiable diseases, where was required fewer computational resources. The practical application of the model was also verified in the field. Specifically, 1 730 apple leaf disease images were collected on site in Banan District, Chongqing, China. Three types were divided into healthy, Scab, and rust leaves. The improved model was compared with other models for experiments. The S-DensseNet model showed a recognition accuracy of 91.91% for healthy apple leaves, 91.83% for Scab apple leaves, and 98.43% for Trust apple leaves. The S-DensseNet model also demonstrated the least inference time, only requiring 42.72 ms. The experimental results show that the S-DenseNet and S-DenseNet-E can be expected to run on embedded edge computing devices, while more accurately identifying the multiple diseases and unidentifiable diseases of apple leaves, and fully meeting the actual production needs of apple orchards. The finding has an important significance for the development of digital agriculture. [ABSTRACT FROM AUTHOR]

为提高苹果园中现有设备的病害树叶识别精度，该研究提出了一种快速识别方法 S-DenseNet-E。首先，基于 DenseNet 的 Dense 模块提出了 S-Dense 模块，并基于 S-Dense 模块搭建了 S-DenseNet 模型。S-Dense 模块在输出层中以 前馈直连方式将模型内每一层输出聚合连接在一起，改善了 Dense 模块的密集连接存在计算量大的问题，有效减小了模 型计算量。通过在 Phytopathology 2021 FGVC8 的苹果树叶病害公开数据集上测试表明，S-DenseNet 的 F1-score 达到 85.14%，高于常用的 CNN 类模型；其识别推理时间（或延迟）是 33.03 ms，低于 MobileNetV2 模型。其次，针对 SDenseNet 模型在不可辨别病害上的 F1-score 较低（65.82%）的问题，该研究在 S-DenseNet 基础上增加辅助模型专门识 别不可辨别病害，形成 S-DenseNet-E 方法。在同一数据集上测试表明，S-DenseNet-E 在不可辨别病害上的 F1-score 达 到 70.10%，识别推理时间为 38.92 ms，比 S-DenseNet 模型仅升高 5.89 ms，并且保持了原来 S-DenseNet 对其他病害的 识别效果。因此，该研究表明，S-DenseNet-E 方法针对苹果患多种病害和不可辨别病害两种复杂情形的识别效果好，并 且计算资源的需求较少，满足果园实际需求。 [ABSTRACT FROM AUTHOR]

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