Yang Tingting, Zhou Suyin, Xu Aijun, Ye Junhua, Yin Jianxin
College of Chemistry and Materials Engineering, Zhejiang Agriculture and Forestry University, Hangzhou 311800, China.
Zhejiang Agriculture and Forestry University, Hangzhou 311800, China.
Plants (Basel). 2023 Sep 29;12(19):3438. doi: 10.3390/plants12193438.
Accurate plant leaf image segmentation provides an effective basis for automatic leaf area estimation, species identification, and plant disease and pest monitoring. In this paper, based on our previous publicly available leaf dataset, an approach that fuses YOLOv8 and improved DeepLabv3+ is proposed for precise image segmentation of individual leaves. First, the leaf object detection algorithm-based YOLOv8 was introduced to reduce the interference of backgrounds on the second stage leaf segmentation task. Then, an improved DeepLabv3+ leaf segmentation method was proposed to more efficiently capture bar leaves and slender petioles. Densely connected atrous spatial pyramid pooling (DenseASPP) was used to replace the ASPP module, and the strip pooling (SP) strategy was simultaneously inserted, which enabled the backbone network to effectively capture long distance dependencies. The experimental results show that our proposed method, which combines YOLOv8 and the improved DeepLabv3+, achieves a 90.8% mean intersection over the union (mIoU) value for leaf segmentation on our public leaf dataset. When compared with the fully convolutional neural network (FCN), lite-reduced atrous spatial pyramid pooling (LR-ASPP), pyramid scene parsing network (PSPnet), U-Net, DeepLabv3, and DeepLabv3+, the proposed method improves the mIoU of leaves by 8.2, 8.4, 3.7, 4.6, 4.4, and 2.5 percentage points, respectively. Experimental results show that the performance of our method is significantly improved compared with the classical segmentation methods. The proposed method can thus effectively support the development of smart agroforestry.
准确的植物叶片图像分割为自动叶面积估计、物种识别以及植物病虫害监测提供了有效的依据。本文基于我们之前公开的叶片数据集，提出了一种融合YOLOv8和改进的DeepLabv3+的方法，用于单个叶片的精确图像分割。首先，引入基于叶片目标检测算法的YOLOv8，以减少背景对第二阶段叶片分割任务的干扰。然后，提出了一种改进的DeepLabv3+叶片分割方法，以更有效地捕捉条形叶片和细长叶柄。使用密集连接空洞空间金字塔池化（DenseASPP）替换ASPP模块，并同时插入条形池化（SP）策略，使骨干网络能够有效地捕捉长距离依赖关系。实验结果表明，我们提出的结合YOLOv8和改进的DeepLabv3+的方法，在我们的公共叶片数据集上实现了叶片分割90.8%的平均交并比（mIoU）值。与全卷积神经网络（FCN）、轻量化缩减空洞空间金字塔池化（LR-ASPP）、金字塔场景解析网络（PSPnet）、U-Net、DeepLabv3和DeepLabv3+相比，该方法分别将叶片的mIoU提高了8.2、8.4、3.7、4.6、4.4和2.5个百分点。实验结果表明，与经典分割方法相比，我们方法的性能有显著提高。因此，该方法能够有效地支持智能农林业的发展。
