机构地区:[1]新疆农业大学计算机与信息工程学院,新疆乌鲁木齐830052 [2]中国农业科学院农业信息研究所,北京100081 [3]国家农业科学数据中心,北京100081
出 处:《智慧农业(中英文)》2023年第2期93-103,共11页Smart Agriculture
基 金:国家自然科学基金项目(32271880,31860180,32060321)。
摘 要:[目的/意义]荒漠植物的准确识别是其认识和保护过程中不可或缺的任务,是荒漠生态研究与保护的基础。自然条件下野外荒漠植物图像的机器视觉自动分类识别可有效提升植物资源调查效率、降低人为主观因素影响,对荒漠植物的精准分类、多样性保护和资源化利用具有重要意义。[方法]以自然环境下的整株荒漠植物图像为研究对象,构建新疆干旱区荒漠植物图像数据集,以EfficientNet B0—B4网络为基础网络,提出一种融合迁移学习和集成学习的荒漠植物图像识别算法,并在公开数据集Oxford Flowers102上进行对比验证。[结果和讨论]基于EfficientNet B0网络的单一子模型的Top-1准确率最高可达93.35%,最低为92.26%,软投票Ensemble-Soft模型、硬投票Ensemble-Hard模型以及加权投票法集成的Ensemble-Weight模型的准确率分别为93.63%、93.55%和93.67%,F1Score和准确率相当;基于EfficientNet B0—B4网络的单一子模型的Top-1准确率最高可达96.65%,F1Score为96.71%,而Ensemble-Soft模型、Ensemble-Hard模型以及Ensemble-Weight模型的准确率分别为99.07%、98.91%和99.23%,相较于单一子模型,精度进一步提高,F_(1)Score与准确率基本相同,模型性能显著;在公开数据集Oxford Flowers102上进行对比试验,3个集成模型相比5个子模型准确率和F,Score最高提升了4.56%和5.05%,最低也提升了1.94%和2.29%,证明了本研究提出的迁移和集成学习策略能够有效提高模型性能。[结论]本方法可提高荒漠植物的识别准确率,通过云端传输至服务器后,实现荒漠植物的准确识别,为真实野外环境下植物图像识别精度低、模型鲁棒性及泛化性弱等问题提供解决思路。服务于野外调查、教学科普以及科学实验等场景。[Objective] Desert vegetation is an indispensable part of desert ecosystems,and its conservation and restoration are crucial.Accurate identification of desert plants is an indispensable task,and is the basis of desert ecological research and conservation.The complex growth environment caused by light,soil,shadow and other vegetation increases the recognition difficulty,and the generalization ability is poor and the recognition accuracy is not guaranteed.The rapid development of modern technology provides new opportunities for plant identification and classification.By using intelligent identification algorithms,field investigators can be effectively assisted in desert plant identification and classification,thus improve efficiency and accuracy,while reduce the associated human and material costs.[Methods] In this research,the following works were carried out for the recognition of desert plant:Firstly,a training dataset of deep learning model of desert plant images in the arid and semi-arid region of Xinjiang was constructed to provide data resources and basic support for the classification and recognition of desert plant images.The desert plant image data was collected in Changji and Tacheng region from the end of September 2021 and July to August 2022,and named DPlants50.The dataset contains 50 plant species in 13families and 43 genera with a total of 12,507 images,and the number of images for each plant ranges from 183 to 339.Secondly,a migration integration learning-based algorithm for desert plant image recognition was proposed,which could effectively improve the recognition accuracy.Taking the EfficientNet B0-B4 network as the base network,the ImageNet dataset was pre-trained by migration learning,and then an integrated learning strategy was adopted combining Bagging and Stacking,which was divided into two layers.The first layer introduced K-fold cross-validation to divide the dataset and trained K sub-models by borrowing the Stacking method.Considering that the output features of each model were the same i
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