基于旋转检测器和 Swin 分类器预测小麦赤霉病程度

IF 4.4 1区 农林科学 Q1 AGRICULTURAL ENGINEERING
Dongyan Zhang , Zhipeng Chen , Hansen Luo , Gensheng Hu , Xin-Gen Zhou , Chunyan Gu , Liping Li , Wei Guo
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引用次数: 0

摘要

小麦赤霉病是一种破坏性很强的病害,对小麦作物的整个生长周期都有不利影响。及时评估田间小麦赤霉病的程度以防止其蔓延至关重要。然而,人工观察效率低且耗时。最近的研究表明,基于计算机视觉的方法可以提高这方面的效率。为了最大限度地减少背景干扰,该研究采用了旋转小麦检测器(RWD)网络来检测小麦头。RWD 网络采用卡尔曼滤波器交叉联合(KFIoU)来预测角度,从而提高了准确性。斯温小麦分类器(SWC)网络用于对健康麦头和病害麦头进行分类。SWC 网络得益于移位窗口自注意模块(SW-MSA),该模块通过与其他窗口建立连接来增强特征提取。利用 3 年来收集的麦田图像对所提出的方法进行了评估。结果表明,该方法性能良好,预测小麦赤霉病水平的准确率达到 96%。此外,病麦计数的 R2 和 RMSE 值分别为 97.62% 和 3.61。该方法通过分析麦田图像,为预测小麦赤霉病程度提供了一种准确的方法。此外,旋转检测器的引入也为小麦赤霉病检测研究做出了新的贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting wheat scab levels based on rotation detector and Swin classifier
Wheat scab is a highly destructive disease that adversely impact wheat crops throughout their growth cycle. It is crucial to promptly evaluate the levels of wheat scab in the field to prevent its spread. Manual observation, however, is inefficient and time-consuming. Recent research has indicated that computer vision-based methods can enhance efficiency in this regard. This study proposed a method for predicting wheat scab levels using a rotation detector and Swin classifier.
To minimise background interference, the study incorporated the rotation wheat detector (RWD) network for detecting wheat heads. The RWD network employed the Kalman filter Intersection over Union (KFIoU) to predict the angle, thereby improving accuracy. The Swin wheat classifier (SWC) network was employed to classify healthy and diseased wheat heads. The SWC network benefited from the shifted window self-attention module (SW-MSA), which enhanced feature extraction by establishing connections with other windows. The proposed method was evaluated using wheat field images collected over 3 years. The results demonstrate promising performance, achieving a 96% accuracy in predicting wheat scab levels. Furthermore, the R2 and RMSE values for diseased wheat count were 97.62% and 3.61, respectively. This method offers an accurate means of predicting wheat scab levels through the analysis of wheat field images. Additionally, the introduction of the rotation detector presents a novel contribution to research on wheat scab detection.
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来源期刊
Biosystems Engineering
Biosystems Engineering 农林科学-农业工程
CiteScore
10.60
自引率
7.80%
发文量
239
审稿时长
53 days
期刊介绍: Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.
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