| 融合多尺度特征和注意力机制的油菜倒伏分类 |
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| 引用本文： | 郑权,乔江伟,李婕,秦涛,涂静敏,李礼,蒋琦.融合多尺度特征和注意力机制的油菜倒伏分类[J].农业工程学报,2024,40(11):186-194. |
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| 作者姓名： | 郑权 乔江伟 李婕 秦涛 涂静敏 李礼 蒋琦 |
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| 作者单位： | 湖北工业大学电气与电子工程学院，武汉，430068;中国农科院油料作物研究所，武汉，430060;武汉大学遥感信息工程学院，武汉，430062;湖南省第二测绘院，长沙，430103 |
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| 基金项目： | 湖北省重点研发计划项目（2023BBB030），国家自然科学基金项目（42301515, 42101440），湖南省自然科学基金（2023JJ60564） |
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| 摘 要： | 倒伏是限制油料作物高产、稳产、优产的主要因素，对油菜倒伏类型的实时监测与评估对于油菜预产和品种选育至关重要。该研究提出一种无人机可见光影像下融合多尺度特征和注意力机制的油菜倒伏分类方法，对绿熟期和黄熟期的倒伏级别进行分类鉴定。首先，设计一种图像分类模型NGnet（nam-ghost network），用于对角果期的油菜倒伏程度进行分类。该网络采用改进的GhostBottleNeck模块，融入利用权重因子来体现重要特征的注意力机制模块NAM（normalization-based attention module），再将不同尺度的注意力特征进行融合，以降低模型参数量、提高准确率；其次，构建使用无人机高空遥感正射影像的油菜倒伏数据集（rape lodging dataset, RLD），该数据集由5789张分辨率为3×255×255且人工标注倒伏级别的小区影像构成；最后，将本文NGnet模型在RLD数据集上的进行验证，准确率达到85.10%，比T2T-VIT、SwinTransformerV2、MobileNetV3、Res2Net、RepVGG 和 RepLKNet分别高出15.6、11.92、7.01、6.22、6.08、2.37个百分点。试验结果表明，NGnet模型对油菜倒伏分类任务是有效的，可为基于无人机RGB影像的油菜倒伏鉴定和良种选育等提供参考。
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| 关 键 词： | 遥感 农作物 分类 无人机遥感影像 倒伏识别 深度学习 |
| 收稿时间： | 2024/1/24 0:00:00 |
| 修稿时间： | 2024/4/24 0:00:00 |
Integration of Multiscale Characterization and Attention Mechanisms for Oilseed Rape Lodging Classification Methodology Study |
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| ZHENG Quan,QIAO Jiangwei,LI Jie,QIN Tao,TU Jingmin,LI Li,JIANG Qi.Integration of Multiscale Characterization and Attention Mechanisms for Oilseed Rape Lodging Classification Methodology Study[J].Transactions of the Chinese Society of Agricultural Engineering,2024,40(11):186-194. |
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| Authors: | ZHENG Quan QIAO Jiangwei LI Jie QIN Tao TU Jingmin LI Li JIANG Qi |
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| Institution: | School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, Hubei 430068, China;Institute of Oilseed Crops, Chinese Academy of Agricultural Sciences, Wuhan, Hubei 430060, China;School of Remote Sensing Information Engineering, Wuhan University, Wuhan, Hubei 430062, China; The Second Surveying and Mapping Institute of Hunan Province, Changsha 430103, China |
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| Abstract: | Oilseed rape has been one of the largest cash crops in the national security of edible oil supply. The yields of oilseed rape can be improved under large-scale production in recent years. The lodging has restricted the high yield, stability, and quality of oilseed crops. Real-time monitoring and evaluation of lodging types are of great significance to the pre-production and variety selection. Unmanned aerial vehicles (UAVs) combined with deep learning can be expected to identify the lodging with the rapid development of artificial intelligence. Among them, the image classification is superior to the oilseed rape lodging, due to the small model size and high accuracy. In this study, an oilseed rape lodging classification was proposed to incorporate multi-scale features and attention mechanisms. The lodging level of oilseed rape was determined at the green and yellow maturity stage. Firstly, an image classification network (NGnet) was designed to identify the lodging of oilseed rape during the pod period. An improved Ghost Bottle Neck structure was then used instead of the traditional convolution. The number of network parameters and computations was reduced greatly because the redundant operation was reduced to maintain the feature expressiveness; Normalization-based attention (NAM) was integrated with the weight factor to represent the important features. The new network was then focused on the interest lodging areas at different lodging levels, leading to suppression of the irrelevant information; A multi-scale fusion strategy was used to fuse attention features at different scales. Multi-scale feature representations were obtained to appropriately weight the fusion of features at different scales, according to the weights of the attention mechanism; The UAV high-altitude remote sensing orthophoto was utilized to construct the rape lodging dataset (RLD). The data was collected from the Yangluo base of the Oilseed Research Institute of the Chinese Academy of Agricultural Sciences in Wuhan City, Hubei Province, China, and the oilseed rape planting area of the Jingzhou Academy of Agricultural Sciences in Shazhou City, Jingzhou City, Hubei Province, China; The dataset consisted of 5789 images with a resolution of 3 × 255 × 255, of which 4 648 were in the training set, and 1 141 were in the testing set; Lastly, the NGnet network model on the RLD was achieved in recognition accuracy of 85.10%, which was 15.6, 11.92, 7.01, 6.22, 6.08, and 2.37 percentage points higher than that of T2T-VIT, SwinTransformerV2, MobileNetV3, Res2Net, RepVGG, and RepLKNet, respectively. The recognition of the improved model was further evaluated in the task of oilseed rape lodging. The experimental fields of oilseed rape were selected as the validation set at Yangluo and Jingzhou base of the Oilseed Research Institute of the Chinese Academy of Agricultural Sciences in 2021. The average accuracy of the model reached 86.36% and 85.71% in the Yangluo and Jingzhou bases. The improved model was achieved in the high accuracy, small model size, and generalization performance, which fully met the classification of oilseed rape lodging degree in the oilseed rape breeding. In addition, the lodging situation of different plots was also explored in the same field. The lodging resistance of different varieties was better distinguished to visualize. The finding can also provide a strong reference for the UAV RGB images to identify the lodging disaster of rapeseed in the selective breeding of good varieties. |
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| Keywords: | remote sensing crops classification unmanned aerial remote sensing imagery fall recognition deep learning |
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