School of Data Science, Guangzhou Huashang College, Guangzhou, 511300, China.
School of Accounting, Guangzhou Huashang College, Guangzhou, 511300, China.
Sci Rep. 2024 Oct 15;14(1):24160. doi: 10.1038/s41598-024-75391-4.
Pest detection is important for crop cultivation. Crop leaf is the main place of pest invasion. Current technologies to detect crop pests have constraints, such as low efficiency, storage demands and limited precision. Image segmentation is a fast and efficient computer-aided detection technology. High resolution image capture solidly supports the crucial processes in discerning pests from images. Study of analytical methods help parse information in the images. In this paper, a regional convolutional neural network (R-CNN) architecture is designed in combination with the radial bisymmetric divergence (RBD) method for enhancing the efficiency of image segmentation. As a special application of RBD, the hierarchical mask (HM) is produced to endorse detection and classification of the leaf-dwelling pests, offering enhanced efficiency and reduced storage requirements. Moreover, to deal with some mislabeled data, a threshold variable is introduced to adjust a fault-tolerant mechanism into HM, to generate a novel threshold-based hierarchical mask (TbHM). Consequently, the hierarchical mask R-CNN (HM-R-CNN) model and the threshold-based hierarchical mask R-CNN (TbHM-R-CNN) model are established to detect various types of healthy and pest-invasive crop leaves to select the regional image features that are rich in pest information. Then simple linear iterative clustering (SLIC) method is incorporation to finish the image segmentation for the classification of pest invasion. The models are tuned and optimized, then validated. The most optimized modeling results are from the TbHM-R-CNN model, with the classification accuracy of 96.2%, the recall of 97.5% and the F1 score of 0.982. Additionally, the HM-R-CNN model observed appreciable results second only to the best model. These results indicate that the proposed methodologies are well-suited for training and testing a dataset of plant diseases, offering heightened accuracy in pest classification. This study revealed that the proposed methods significantly outperform the existing techniques, marking a substantial improvement over current methods.
虫害检测对作物种植至关重要。作物叶片是虫害入侵的主要部位。当前用于检测作物虫害的技术存在效率低、存储需求大以及精度有限等局限性。图像分割是一种快速高效的计算机辅助检测技术。高分辨率图像采集为从图像中识别虫害提供了有力支持。对分析方法的研究有助于解析图像中的信息。在本文中，设计了一种区域卷积神经网络（R-CNN）架构，并结合径向双对称散度（RBD）方法，以提高图像分割的效率。作为 RBD 的特殊应用，生成了层次掩模（HM），以支持叶栖害虫的检测和分类，提高了效率并降低了存储需求。此外，为了处理一些错误标记的数据，引入了一个阈值变量，将容错机制引入 HM 中，生成一种新的基于阈值的层次掩模（TbHM）。因此，建立了层次掩模 R-CNN（HM-R-CNN）模型和基于阈值的层次掩模 R-CNN（TbHM-R-CNN）模型，用于检测各种健康和受虫害侵袭的作物叶片，以选择富含虫害信息的区域图像特征。然后采用简单线性迭代聚类（SLIC）方法完成图像分割，实现对虫害入侵的分类。对模型进行调整和优化，然后进行验证。最优化的建模结果来自 TbHM-R-CNN 模型，其分类准确率为 96.2%，召回率为 97.5%，F1 得分为 0.982。此外，HM-R-CNN 模型的结果也相当可观，仅次于最佳模型。这些结果表明，所提出的方法非常适合训练和测试植物疾病数据集，在害虫分类方面具有更高的准确性。本研究表明，所提出的方法显著优于现有技术，比当前方法有了实质性的改进。
