无人机低空遥感结合YOLOv7快速评估水稻穗颈瘟抗性  

Rapid evaluation of rice neck blast resistance using low altitude remote sensing of UAV combined with YOLOv7

作　　者：翁海勇[1,2] 姚越 黄德耀 张玉婷 程组锌 叶大鹏[1,2] 吴仁烨 WENG Haiyong;YAO Yue;HUANG Deyao;ZHANG Yuting;CHENG Zuxin;YE Dapeng;WU Renye(College of Mechanical and Electrical Engineering,Fujian Agriculture and Forestry University,Fuzhou 350002,China;School of Future Technology,Fujian Agriculture and Forestry University,Fuzhou 350002,China;College of Agriculture,Fujian Agriculture and Forestry University,Fuzhou 350002,China)

机构地区：[1]福建农林大学机电工程学院,福州350002 [2]福建农林大学未来技术学院,福州350002 [3]福建农林大学农学院,福州350002

出　　处：《农业工程学报》2024年第21期110-118,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：福建省自然科学基金资助项目(2022J01611);福建省科技厅引导性项目(2023N0005);福建农林大学科技创新专项基金项目(CXZX2019030G,CXZX2019031)。

摘　　要：为解决传统水稻稻瘟病抗性评估手段单一、效率低的问题,该研究提出一种无人机低空遥感技术结合YOLOv7模型的水稻穗颈瘟抗性鉴定方法。首先,将标注区域分割成小尺寸图像(≤1 240×1 240像素),将小尺寸图像进行旋转、缩放、平移、剪切和改变对比度处理。经数据清洗,去除分辨率过低的图像,扩充样本数量,以提高数据多样性。然后,将压缩注意力机制(squeeze-excitation attention)和可变形卷积(deformable convolution)引入YOLOv7模型,自适应调整感受野,以提升捕捉穗颈瘟病斑细粒度特征的能力。最后,构建穗颈瘟检测的YOLOv7_Neckblast模型。研究结果表明,YOLOv7_Neckblast能够有效检测穗颈瘟,计算出15个品种的穗颈瘟发病率和病害等级(1、3、5、7和9级的水稻品种分别有4、4、3、2和2个)。在交并比阈值为0.5时,YOLOv7_Neckblast平均精度均值相较于YOLOv7、FCOS和RetinaNet分别提升了4.0、6.4和5.8个百分点,召回率和F1值分别提高了至少4.0和4.0个百分点,且浮点计算数和参数量最低。与育种专家判定的实际抗性水平相比,YOLOv7_Neckblast模型对15个水稻品种的穗颈瘟抗性水平的平均评估准确率为86.67%,能较好地实现不同水稻品种穗颈瘟抗性的区分。无人机低空遥感融合人工智能技术可用于水稻黄熟期育种中穗颈瘟抗性的评估,也可为其他作物优势品种的选育提供参考。Blast disease has been one of the most serious threats to rice production.However,the conventional evaluation of rice blast resistance cannot fully meet the large-scale production in recent years.In this study,an efficient evaluation was proposed for rice neck blast resistance via combining unmanned aerial vehicle(UAV)low-altitude remote sensing with the YOLOv7 model.2565 images of rice neck blast were collected using UAV and then divided into small-sized images(≤1240x1240 pixels)in the annotated area.The small-sized images were further subjected to a random combination of five operations,including rotation,scaling,translation,cropping,and changing contrast.The images with low resolution were removed after data cleaning.Finally,the dataset size was expanded with better diversity.The squeeze extinction attention and deformable convolution were introduced into the YOLOv7 model,in order to capture the fine-grain features of the rice neck blast disease spot.YOLOv7_Neckblast model was established for the rice neck blast detection.The number of affected ears was obtained for 15 rice varieties.The incidence rate was calculated for the disease grade of rice neck blast.Among them,4,4,3,5,7,and 9 rice varieties of grades 1,3,5,and 7,as well as 9,2,and 2 were assessed by YOLOv7_Neckblast,respectively.At the intersection over the union(loU)threshold of 0.5,the mean average precision(mAP)of YOLOv7_Neckblast for rice neck blast was 66.4%,which was 4.0,6.4,and 5.8 percentage points higher than that of the original YOLOv7,FCOS(fully convolutional one-stage object detection),and RetinaNet models,respectively.The recall rate was 75%,which was 4.0,8.0,and 22.0 percentage points higher than those of the three models,respectively.The F1 score was 71%,which was 4.0,5.0,and 12.0 percentage points higher than those of the three models,respectively.The training YOLOv7 Neckblast was relatively stable with the low floating-point operations per second(FLOP)under the same training conditions.The loss values remained stable with about 0.019

关 键 词：水稻 无人机 低空遥感 穗颈瘟 YOLOv7_Neckblast模型 抗性评估 

分 类 号：TP751.2[自动化与计算机技术—检测技术与自动化装置]