Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Korea.
Sensors (Basel). 2021 Sep 30;21(19):6565. doi: 10.3390/s21196565.
Plant diseases must be identified at the earliest stage for pursuing appropriate treatment procedures and reducing economic and quality losses. There is an indispensable need for low-cost and highly accurate approaches for diagnosing plant diseases. Deep neural networks have achieved state-of-the-art performance in numerous aspects of human life including the agriculture sector. The current state of the literature indicates that there are a limited number of datasets available for autonomous strawberry disease and pest detection that allow fine-grained instance segmentation. To this end, we introduce a novel dataset comprised of 2500 images of seven kinds of strawberry diseases, which allows developing deep learning-based autonomous detection systems to segment strawberry diseases under complex background conditions. As a baseline for future works, we propose a model based on the Mask R-CNN architecture that effectively performs instance segmentation for these seven diseases. We use a ResNet backbone along with following a systematic approach to data augmentation that allows for segmentation of the target diseases under complex environmental conditions, achieving a final mean average precision of 82.43%.
必须在最早阶段识别植物病害，以便采取适当的治疗措施，减少经济和质量损失。因此，对于诊断植物病害而言，低成本和高度精确的方法是必不可少的。深度神经网络在人类生活的许多方面都取得了最先进的性能，包括农业领域。目前的文献表明，可用于自主草莓病虫害检测的数据集数量有限，无法进行细粒度实例分割。为此，我们引入了一个新的数据集，包含 2500 张七种草莓病害的图像，这些图像可用于开发基于深度学习的自主检测系统，以便在复杂背景条件下对草莓病害进行分割。作为未来工作的基准，我们提出了一个基于 Mask R-CNN 架构的模型，该模型可以有效地对这七种病害进行实例分割。我们使用 ResNet 主干，并采用系统的数据增强方法，允许在复杂的环境条件下对目标病害进行分割，最终平均精度达到 82.43%。
