IEEE/ACM Trans Comput Biol Bioinform. 2023 May-Jun;20(3):2016-2028. doi: 10.1109/TCBB.2022.3229114. Epub 2023 Jun 5.
Apple leaf diseases seriously affect the quality of apples and may lead to yield losses, detecting apple leaf diseases accurately can prevent diseases from spreading and promote the healthy growth of the industry. However, recent studies cannot achieve accurate detection of leaf diseases with high accuracy because the lesions are of different sizes. So, this paper proposed a novel apple leaf disease detection method called VMF-SSD (V-space-based Multi-scale Feature-fusion SSD), which is designed to extract more reliable multi-scale feature representations for varied sizes of diseased spots and improve the final detection performance. The multi-scale feature extraction is established with multi-scale feature representation to further improve the disease detection performance, especially for small spots. After that, a V-space-based location branch is presented to enhance the texture feature information and help further identify disease spot location. Finally, attention mechanisms are utilized to automatically learn the importance of feature channels at different scales for distinguishing diseased spots of different sizes. Experimental results showed that the VMF-SSD method achieves 83.19% mAP and obtains the detection speed of 27.53 FPS on the test set, which indicates that the proposed VMF-SSD method can achieve competitive performance on apple leaf diseases detection task and satisfy the requirements of agricultural production applications.
苹果叶病害严重影响苹果的品质，可能导致产量损失，准确检测苹果叶病害可以防止病害传播，促进产业健康发展。然而，由于病变大小不同，最近的研究无法实现对叶病的高精度准确检测。因此，本文提出了一种名为 VMF-SSD（基于 V 空间的多尺度特征融合 SSD）的新型苹果叶病检测方法，旨在提取更可靠的多尺度特征表示，以适应不同大小的病变，提高最终的检测性能。通过多尺度特征表示建立多尺度特征提取，进一步提高了病害检测性能，尤其是对小斑点的检测性能。然后，提出了一个基于 V 空间的位置分支，增强了纹理特征信息，有助于进一步识别病斑位置。最后，利用注意力机制自动学习不同尺度特征通道的重要性，以区分不同大小的病斑。实验结果表明，VMF-SSD 方法在测试集上的 mAP 达到 83.19%，检测速度达到 27.53 FPS，表明所提出的 VMF-SSD 方法在苹果叶病检测任务中具有竞争力，满足农业生产应用的要求。
