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基于热红外和RGB图像的番茄苗期高温胁迫检测方法

钟陆杰, 汪小旵, 张晓蕾, 施印炎

钟陆杰, 汪小旵, 张晓蕾, 等. 基于热红外和RGB图像的番茄苗期高温胁迫检测方法[J]. 华南农业大学学报, 2023, 44(1): 110-122. DOI: 10.7671/j.issn.1001-411X.202203039
引用本文: 钟陆杰, 汪小旵, 张晓蕾, 等. 基于热红外和RGB图像的番茄苗期高温胁迫检测方法[J]. 华南农业大学学报, 2023, 44(1): 110-122. DOI: 10.7671/j.issn.1001-411X.202203039
ZHONG Lujie, WANG Xiaochan, ZHANG Xiaolei, et al. Detection method of high temperature stress of tomato at seedling stage based on thermal infrared and RGB images[J]. Journal of South China Agricultural University, 2023, 44(1): 110-122. DOI: 10.7671/j.issn.1001-411X.202203039
Citation: ZHONG Lujie, WANG Xiaochan, ZHANG Xiaolei, et al. Detection method of high temperature stress of tomato at seedling stage based on thermal infrared and RGB images[J]. Journal of South China Agricultural University, 2023, 44(1): 110-122. DOI: 10.7671/j.issn.1001-411X.202203039

基于热红外和RGB图像的番茄苗期高温胁迫检测方法

基金项目: 国家重点研发计划 (2019YFD1001902-11)
详细信息
    作者简介:

    钟陆杰,硕士研究生,主要从事图像处理研究, E-mail: zhonglujie24@163.com

    通讯作者:

    汪小旵,教授,博士,主要从事作物信息化检测研究, E-mail: wangxiaochan@njau.edu.cn

  • 中图分类号: S126; S641.2

Detection method of high temperature stress of tomato at seedling stage based on thermal infrared and RGB images

Article Text (iFLYTEK Translation)
  • 摘要:
    目的 

    针对实际生产场景中番茄苗期生长遇到的高温胁迫问题,提出一种基于热红外和RGB图像的番茄苗期高温胁迫检测方法。

    方法 

    首先,通过番茄苗期热红外图像反演获取番茄冠层温度参数,采用偏最小二乘(Partial least squares, PLS)模型提取冠层温度特征指标;然后,建立采用3种不同主干特征提取网络的Mask-RCNN模型,通过迁移学习的方式将番茄苗期RGB图像输入Mask-RCNN模型,进行高温胁迫症状实例分割,得到番茄苗期胁迫症状特征指标;最后,利用提取的温度和胁迫症状特征指标构建分级数据集,输入高温胁迫分级模型,得到高温胁迫等级。

    结果 

    基于PLS模型提取的冠层温度特征指标累计贡献率达95.45%;基于ResNet101+Mask-RCNN的高温胁迫症状分割网络对番茄苗期轻度和重度胁迫的分割精度最高,均值平均查准率(Mean average precision, mAP)分别为77.3%和73.8%;基于温度和胁迫症状特征指标构建的4种高温胁迫分级模型中,反向传播神经网络(Back propagation neural network, BPNN)获得最好的高温胁迫分级效果,分级准确率达95.6%。

    结论 

    该方法对番茄苗期高温胁迫检测效果较好,可为番茄苗期高温胁迫早期精准检测和快速自动预警提供技术支撑。

    Abstract:
    Objective 

    Aiming at the problem of high temperature stress encountered in the growth of tomato at seedling stage in actual production scenarios, a method for detecting high temperature stress of tomato at seedling stage based on thermal infrared and RGB images was proposed.

    Method 

    Firstly, the tomato canopy temperature parameters were obtained by inversion through the thermal infrared image of tomato plant at seedling stage, and the canopy temperature characteristic indicators were extracted by the partial least squares (PLS) model. Then, a Mask-RCNN model using three different backbone feature extraction networks was established, and the RGB images of tomato seedlings were input into the Mask-RCNN model by means of transfer learning for instance segmentation of high temperature stress symptoms. The characteristic indicators of tomato stress symptoms at seedling stage were obtained. Finally, the extracted temperature and stress symptom characteristic indicators were used to construct a hierarchical data set and fed into the high temperature stress classification model to obtain the high temperature stress level.

    Result 

    The cumulative contribution rate of the canopy temperature characteristic indicators extracted based on the PLS model reached 95.45%. The high temperature stress symptom segmentation network based on ResNet101+Mask-RCNN had the highest segmentation accuracy for mild and severe stress of tomato at seedling stage, with mean average precision (mAP) of 77.3% and 73.8% respectively. Among the four high temperature stress grading models constructed based on temperature and stress symptom characteristic indicators, the back propagation neural network (BPNN) showed the best high temperature stress grading performance with the grading accuracy rate of 95.6%.

    Conclusion 

    The proposed method in this study achieves better detection performance for high temperature stress of tomato at seedling stage, and provides a technical support for early detection and rapid automatic warning of high temperature stress of tomato at seedling stage.

  • 美丽崖豆藤Millettia speciosa Champ.,又名牛大力、大力薯、山莲藕,为豆科崖豆藤属植物,主要分布在广东、福建、湖南、广西、海南、云南、贵州等省区的山谷、路旁、灌木林丛或疏林中[]。其根部可以入药,用来治疗腰肌劳损、风湿性关节炎、肺结核等疾病,是多种中成药的主要原料;在美丽崖豆藤生产地,其根部常用于制作药膳、药酒,其茎、芽、叶片被开发成茶包或饲料添加剂,产品附加值大大提升[]。目前美丽崖豆藤野生资源已濒临枯竭,规模化的人工种植基地面积正逐渐扩大。但是,鲜见关于美丽崖豆藤的病害研究[]

    炭疽病是世界范围内的一种重要病害,炭疽病菌Colletotrichum spp.能够侵染为害3200余种单子叶和双子叶植物,包括园艺观赏植物、水果、蔬菜、中药材等[]。在我国的豆科植物上已报道了多种炭疽病菌,如C. spaethianum引起的菜豆炭疽病[]C. gloeosporioides引起的葛藤和大豆炭疽病[]C. capsici引起的豇豆炭疽病[]C. chlorophyti 引起的大豆炭疽病[]C. siamense引起的紫荆炭疽病[],但其鉴定所采用的标准不尽一致。即使是形态学特征和分子系统发育分析相结合,也有单个内转录间隔区(Internal transcribed spacer,ITS)片段、肌动蛋白(Actin,ACT)、3−磷酸甘油醛脱氢酶(Glyceraldehyde-3- phosphate dehydrogenase,GAPDH)、几丁质合成酶基因(Chitin synthase,CHS-1)、β−微管蛋白(β-tubulin,TUB2)和钙调蛋白(Calmodulin,CAL)等多个基因联合分析的区别,后者已成为炭疽病菌精准鉴定的重要手段[-]。美丽崖豆藤炭疽病菌主要侵染叶片,在叶尖或叶缘形成大量病斑,发病后期引起大量叶片脱落,田间发病植株叶片黄化,长势衰退。为明确广东省德庆县美丽崖豆藤叶片炭疽病的病原菌种类及筛选有效杀菌剂,本研究采集了发病的美丽崖豆藤植株样品,采用组织分离法获得分离物,单孢纯化后通过柯赫氏法则验证其致病性;结合病原菌形态学特征和多基因系统学分析确定病原菌分类地位;同时,采用菌丝生长速率法测定病原菌对4种常用杀菌剂的敏感性,旨在为美丽崖豆藤炭疽病的诊断和有效防控提供理论依据。

    2018年11月在广东省肇庆市德庆县药材种植示范基地发现感染炭疽病的美丽崖豆藤,观察并记录病害症状;采集具有典型症状的叶片。

    在采集的具有典型症状的美丽崖豆藤叶片的病健交界处切取5 mm×5 mm的叶片组织块,于φ为75%的乙醇溶液中浸润10 s进行表面消毒,随后用φ为2%的次氯酸钠消毒2~3 min,再用无菌蒸馏水冲洗3次,最后于无菌滤纸上自然晾干水分。用无菌镊子将消毒后的叶片组织块移至倒好的PDA培养基上,于25 ℃黑暗条件下培养至组织块周围长出菌丝。挑取新鲜的菌丝至新PDA平板中央进行疑似病原菌的培养。待新转移的菌落产生橘红色黏孢团后,挑取分生孢子配制孢子悬浮液,涂布于琼脂培养基上,于显微镜下切取单孢子的琼脂块转移至PDA平板上,获得单孢纯化菌株。观察各纯化菌株的菌落形态,并将各菌株接种至PDA斜面试管中在4 ℃条件下保存备用。

    采用菌饼接种法进行菌株致病性测定。待培养菌株于PDA培养基上培养7 d后,在菌落边缘打取直径为5 mm的菌饼。选取美丽崖豆藤嫩叶,用无菌接种针刺伤后接种菌饼,以接种纯PDA培养基饼作为对照,每个菌株共计接种15张叶片。接种后的叶片置于保鲜盒中喷雾保湿,定期观察叶片的发病情况,方法参照文献[];随机选取发病的叶片进行病原菌的再分离,并与原接种菌株进行形态及分子序列比较,若与原接种菌株相同,则原接种菌株即为致病菌。

    观察病原菌在PDA培养基上于25 ℃条件下的培养性状;待产生橘红色的黏孢团后,用无菌牙签挑取黏孢团制备玻片,用Olympus BX41显微镜观察各菌株形态、测定分生孢子大小。

    将病原菌于PDA培养基上培养5 d,刮取菌丝置于液氮下充分研磨,采用真菌基因组DNA提取试剂盒(Omega生物工程有限公司)提取菌丝DNA。采用引物ITS1/ITS4[]、CHS-79F/CHS-345R[]、GDF/GDR[]、ACT-512F/ACT-783R[]和Bt2a/Bt2b[]进行PCR扩增,各序列扩增引物信息见表1。PCR反应体系总体积为25 μL:DNA模板1 μL,正、反向引物各1 μL(10 μmol/L),2×MasterMix 12.5 μL,加ddH2O补足至25 μL。反应条件为:94 ℃预变性5 min;94 ℃变性30 s (CHS、ACT,58 ℃;GAPDH,60 ℃;ITS、TUB2,55 ℃)退火30 s,72 ℃延伸45 s,共35个循环;最后72 ℃延伸7 min。表2为本研究所用的刺盘孢菌株的序列信息。

    表  1  病原菌鉴定所用引物
    Table  1.  Primers used for pathogen identification
    基因 Gene 产物名称 Product name 引物名称 Primer name 序列 (5′→3′) Sequence
    ITS 内转录间隔区 Internal transcribed spacer ITS1 TCCGTAGGTGAACCTGCGG
    ITS4 TCCTCCGCTTATTGATATGC
    CHS 几丁质合成酶 Chitin synthase CHS-79F TGGGGCAAGGATGCTTGGAAGAAG
    CHS-345R TGGAAGAACCATCTGTGAGAGTTG
    GAPDH 3−磷酸甘油醛脱氢酶
    Glyceraldehyde-3- phosphate dehydrogenase
    GDF GCCGTCAACGACCCCTTCATTGA
    GDR GGGTGGAGTCGTACTTGAGCATGT
    ACT 肌动蛋白 Actin ACT-512F ATGTGCAAGGCCGGTTTCGC
    ACT-783R TACGAGTCCTTCTGGCCCAT
    TUB2 β−微管蛋白 β-tubulin Bt2a GGTAACCAAATCGGTGCTGCTTTC
    Bt2b ACCCTCAGTGTAGTGACCCTTGGC
    下载: 导出CSV 
    | 显示表格
    表  2  刺盘孢菌株的序列信息
    Table  2.  Sequence information of the Colletotrichum isolates
    类别
    Taxon
    菌株编号1)
    Isolate No.
    宿主
    Host
    来源地
    Original place
    GenBank 登录号 GenBank accession No.
    ITS CHS GAPDH ACT TUB2
    C. aenigma ICMP 18608 Persea americana Israel JX010244 JX009774 JX010044 JX009443 JX010389
    C. aenigma ICMP 18686 Pyrus pyrifolia Japan JX010243 JX009789 JX009913 JX009519 JX010390
    C. alienum ICMP 12071 Malus domestica New Zealand JX010251 JX009882 JX010028 JX009572 JX010411
    C. alienum ICMP 18704 Persea americana New Zealand JX010253 JX009886 JX010045 JX009456
    C. asianum ICMP 18696 Mangiferaindica Australia JX010192 JX009753 JX009915 JX009576 JX010384
    C. asianum ICMP 18580 Coffea arabica Thailand JX010196 JX009867 JX010053 JX009584 JX010406
    C. boninense ICMP 17904 Crinum asiaticum var. sinicum Japan JX010292 JX009827 JX009905 JX009583 JQ005588
    C. boninense LPS0023 Alcantareaimperialis Brazil MK286012 MK286457 MK286456 MK286458
    C. fructicola ICMP 18645 Theobroma cacao Panama JX010172 JX009873 JX009992 JX009543 JX010408
    C. fructicola ICMP 18727 Fragaria × ananassa USA JX010179 JX009812 JX010035 JX009565 JX010394
    C. fructicola ICMP 18120 Dioscoreaalata Nigeria JX010182 JX009844 JX010041 JX009436 JX010401
    C. fructicola ICMP 17921 Ficus habrophylla Germany JX010181 JX009839 JX009923 JX009495 JX010400
    C. gloeosporioides ICMP 17821 Citrus sinensis Italy JX010152 JX009818 JX010056 JX009531 JX010445
    C. gloeosporioides ICMP 18697 Vitis vinifera USA JX010154 JX009780 JX009987 JX009557
    C. horii ICMP 12492 Diospyros kaki New Zealand GQ329687 JX009748 JX010001 JX009533 JX010375
    C. horii ICMP 17968 Diospyros kaki China JX010212 JX009811 JX009939 JX009547 JX010378
    C. kahawae ICMP 17816 Coffea arabica Kenya JX010231 JX009813 JX010012 JX009452 JX010444
    C. kahawae ICMP 17915 Coffea arabica Angola JX010234 JX009829 JX010040 JX009474 JX010435
    C.queenslandicum ICMP 1778 Carica papaya Australia JX010276 JX009899 JX009934 JX009447 JX010414
    C.queenslandicum ICMP 18705 Coffea sp. Fiji JX010185 JX009890 JX010036 JX009490 JX010412
    C. siamense NDL13 Millettia speciosa China MT673674 MT683677 MT683679 MT683675 MT683681
    C. siamense NDL19 Millettia speciosa China MT673675 MT683678 MT683680 MT683676 MT683682
    C. siamense ICMP 17791 Malus domestica USA JX010273 JX009810 JX009933 JX009508
    C. siamense ICMP 12567 Persea americana Australia JX010250 JX009761 JX009940 JX009541 JX010387
    C. siamense ICMP 18642 Hymenocallis mericana China JX010278 JX009875 JX010019 JX009441 JX010410
     1) NDL13和NDL19为本研究所获得的菌株
     1) NDL13 and NDL19 were isolates in the present study
    下载: 导出CSV 
    | 显示表格

    取5 μL PCR扩增产物用10 g/L琼脂糖凝胶电泳进行检测,然后将PCR产物送至北京六合华大基因科技有限公司广州分公司测序。将测得的基因序列与GenBank中的序列进行比对。下载相似性高的序列及其对应复合种的常见模式菌株序列(各菌株均为狭义的生物学种,具体信息见表2),使用MEGA软件剪切后按照ITS-CHS-GAPDH-ACT-TUB2的顺序首尾拼接,然后分析系统发育关系,采用最大似然法(Maximum likelihood,ML)构建系统发育树,以自展法(Bootstrap)进行检测,共循环1000次。

    以经致病性测定的菌株NDL13为材料,供试药剂为苯醚甲环唑(φ为96.3%)、咪鲜胺(φ为97.0%)、吡唑醚菌酯(φ为98.0%)和甲基硫菌灵(φ为97.0%)原药,采用菌丝生长速率法测定供试药剂对病原菌的抑制活性。首先将各药剂溶解于甲醇中得到原液,然后加水稀释得到工作液,取各工作液加入至PDA培养基中充分混匀,配制得到不同终浓度的含药平板,以不加药剂加入等量甲醇的PDA平板作为对照,每个药剂浓度处理设置4个重复。用孔径为5 mm的打孔器在培养7 d的菌落边缘打取菌饼,接种至PDA含药平板上,置于25 ℃条件下黑暗培养7 d,然后用十字交叉法测量各处理的菌落半径,计算各杀菌剂菌丝生长抑制率和抑制中浓度(EC50),绘制毒力回归方程。

    用SPSS 26软件统计分析试验数据,求得毒力回归方程、EC50和相关系数,并采用Duncan’s新复极差法进行各处理间的差异显著性检验。

    美丽崖豆藤炭疽病发生于叶片,在叶尖或叶缘形成褐色的不规则形病斑,发病后期病斑变为灰白色或灰褐色(图1A1B),边缘具有一条明显的褐色坏死交界线(图1C1D),病斑外具黄色晕圈,病斑正面有明显的黑色小颗粒,为病原菌的分生孢子盘(图1C)。

    图 1 美丽崖豆藤的田间发病症状
    图  1  美丽崖豆藤的田间发病症状
    A 和B: 田间发病症状;C 和D:发病叶片的正、反面
    Figure  1.  Symptoms of the diseased Millettia speciose in the field
    A and B: Typical symptoms in the field; C and D: The adaxial side and abaxial side of diseased leaves, respectively

    经过病组织分离、单孢纯化及单孢菌株的菌落性状比较,保存6个菌株,分别编号为NDL09、NDL13、NDL18、NDL19、NDL23和NDL35。6个菌株的致病性测定结果表明,只有菌株NDL13和NDL19可引起接种叶片发病,接种3 d后叶片开始出现水渍状病斑,接种8 d后病斑呈现为明显的褐色坏死,中央灰白色,边缘有黄色晕圈(图2A~2C),该症状与植株自然发病症状相似;对发病组织进行病原菌的再分离,得到的菌株的菌丝生长特征、菌落颜色、产孢表现、分生孢子显微特征等均与接种菌株表现一致。PDA对照、空白对照及另外4个菌株接种的叶片均不发病,仅在伤口刺伤处有一褐色坏死点(图2D~2H)。

    图 2 美丽崖豆藤叶片接种8 d后的发病症状
    图  2  美丽崖豆藤叶片接种8 d后的发病症状
    A~C:NDL13接种;D~E: 刺伤后接PDA饼;F: 空白对照;G~H:NDL18接种接种
    Figure  2.  Disease symptoms on Millettia speciosa leaves eight days after inoculation
    A−C indicate the anthracnose symptom of leaves inoculated with NDL13; D−E indicate leaves were wounded and inoculated with pure PDA plug; F indicate leaves were only wounded; G−H indicate leaves were inoculated with NDL18

    致病菌株NDL13和NDL19的形态学特征基本一致。在PDA培养基上,菌丝初期为白色,气生菌丝茂盛,呈绒毛状,菌落边缘整齐(图3A3B);培养15 d后菌落上出现橘红色的黏孢团(图3C3D),为病原菌的分生孢子。分生孢子无色,单孢,长椭圆形,两端钝圆,具1~2个油球,13.46~15.45 μm×4.77~5.91 μm (图3E)。分生孢子萌发形成的附着孢浅棕色至黑褐色,近球形、棒形或不规则形,8.22~9.95 μm×5.46~6.29 μm(图3F3G)。根据病原菌的形态特征,结合Weir等[]的描述,初步确定该菌株属于胶孢刺盘孢C. gloeosporioides 复合种。

    图 3 炭疽病菌株的菌落形态和显微形态
    图  3  炭疽病菌株的菌落形态和显微形态
    A和B:培养8 d后正、反面菌落;C和D:培养15 d后正、反面菌落;E: 孢子;F和G:附着孢
    Figure  3.  Colony morphology and microscopic characters of Colletotrichum isolates
    A and B indicate upper and reverse of cultures 4 days after inoculation; C and D indicate upper and reverse of cultures 15 days after inoculation; E: conidia; F and G indicate appressoria

    将菌株NDL13和NDL19的ITSCHSGAPDHACTTUB2这5个基因的序列与从GenBank中下载得到的21个模式菌株或公认菌株相应的序列进行比对,以C. boninense为外类群构建的系统发育树如图4所示,病原菌NDL13和NDL19均与暹罗刺盘孢C. siamense聚在一起,形成一个明显的分支,且各分支间均有较高的支持率,因此确定美丽崖豆藤炭疽病的病原菌为暹罗刺盘孢C. siamense

    图 4 基于最大似然法构建的多基因系统发育树
    图  4  基于最大似然法构建的多基因系统发育树
    NDL19和NDL13为供试菌株
    Figure  4.  Multigene phylogenetic tree based on maximum likelihood analysis
    NDL19 and NDL13 indicate the present study isolates

    表3可知,4种杀菌剂对美丽崖豆藤炭疽病菌均表现出显著的抑制效果,EC50为0.015~0.066 mg/L。其中,咪鲜胺的抑菌效果最佳,EC50最低,为0.015 mg/L,其次为吡唑醚菌酯、苯醚甲环唑和甲基硫菌灵,其EC50分别为0.055、0.060和0.066 mg/L,结果表明这4种测试药剂均可作为防治美丽崖豆藤炭疽病的首选药剂。

    表  3  4种杀菌剂对美丽崖豆藤炭疽病菌的室内毒力测定结果
    Table  3.  In vitro toxicity test of four fungicides against Colletotrichum siamense
    杀菌剂 Fungicide 回归方程1) Regression equation 相关系数 Correlation coefficient EC50/(mg·L−1)
    苯醚甲环唑 Difenoconazole y=16.112x+51.699 0.979 0.060
    咪鲜胺 Prochloraz y=23.217x+71.232 0.989 0.015
    吡唑醚菌酯 Pyraclostrobin y=199.850x+32.768 0.997 0.055
    甲基硫菌灵 Thiophanate-methyl y=329.460x+20.391 0.975 0.066
     1) x:杀菌剂的浓度对数;y:杀菌剂对美丽崖豆藤炭疽病菌的抑制率
     1) x: Logarithm of fungicide concentration; y: Inhibition rate of fungicide against Colletotrichum siamense
    下载: 导出CSV 
    | 显示表格

    美丽崖豆藤具有重要的药用和经济价值,炭疽病的发生极大地影响了其产量和价值,本研究报道了在广东省德庆县药材种植基地发现的美丽崖豆藤炭疽病,并结合致病性接种、形态特征和多基因系统发育树,将病原菌鉴定为暹罗刺盘孢C. siamense。该发现为美丽崖豆藤炭疽病的诊断和防治提供了理论依据。

    炭疽病是一种重要的植物病害,可侵染为害植物叶片[]、花穗[]、果实[]等,多年来很多学者致力于炭疽病的病原菌分类鉴定和病害防控的研究[-]。暹罗刺盘孢C. siamense被归入胶孢刺盘孢C. gloeosporioides复合种,前期研究只用ITS序列对病原菌进行鉴定,而该序列不能很好地区分胶胞刺盘孢复合种里面的近缘种,在后期的胶胞刺盘孢C. gloeosporioides复合种的修订中,多基因(ITSACTGAPDHCHS-1、TUBCAL等)联合应用至胶胞刺盘孢复合种的鉴定中,提升了生物种鉴定的准确性[, ]。暹罗刺盘孢C. siamense能够侵染多种宿主植物,如茜草科Rubiaceae的咖啡Coffea arabica[]、天南星科Araceae的魔芋Amorphophallus konjac[]、无患子科Sapindaceae的荔枝Litchi chinensis []、蔷薇科Rosaceae的苹果Malus domestica []、山龙眼科Proteaceae植物[]、五加科Araliaceae的鹅掌柴Schefflera octophylla []、兰科Orchidaceae植物[]等。美丽崖豆藤隶属于豆科Leguminosae崖豆藤属Millettia,目前鲜见刺盘孢属Colletotrichum病原菌在崖豆藤属植物上的相关报道。本研究结合形态特征并构建多基因(ITSCHSGAPDHACTTUB2)系统发育树,鉴定出美丽崖豆藤炭疽病的病原菌为暹罗刺盘孢C. siamense

    化学防治虽具有造成环境污染和诱使病原菌产生抗药性等缺陷,但其具有高效、快速、不受地域限制、便于规模化农事操作等优点,依然是目前防治炭疽病的主要措施。药效试验结果表明,美丽崖豆藤炭疽病的病原菌NDL13对咪鲜胺和苯醚甲环唑敏感,该结果与Cao等[]的试验结果基本一致。Hu等[]对桃子Prunus persica和蓝莓Vaccinium spp.上分离得到的暹罗刺盘孢C. siamense进行研究,结果发现100 mg/L的甲基硫菌灵对其抑制率才能超过50%,远高于本研究中甲基硫菌灵对美丽崖豆藤炭疽病的病原菌暹罗刺盘孢C. siamense的EC50(0.066 mg/L),这或与该病害为新病害,尚无进行长期的药剂防控,病原菌对供试药剂敏感有关,因此实际生产中应多种药剂轮换使用以降低病原菌的抗药性。本研究为美丽崖豆藤炭疽病的准确识别和科学防治奠定了理论基础、提供了科学的指导建议。

  • 图  1   试验整体流程图

    PCA:主成分分析;PLS:偏最小二乘;NB:朴素贝叶斯;SVM:支持向量机;kNN:k−最近邻;BPNN:反向传播神经网络

    Figure  1.   Overall flow chart of the test

    PCA: Principal component analysis; PLS: Partial least squares; NB: Naive Bayesian; SVM: Support vector machine; kNN: k-nearest neighbor; BPNN: Back propagation neural network

    图  2   番茄幼苗热红外图像温度分布直方图

    Figure  2.   Histogram of temperature distribution of thermal infrared image of tomato seedling

    图  3   番茄幼苗冠层能量分布三维曲面图

    Figure  3.   3D surface map of energy distribution in canopy of tomato seedlings

    图  4   不同高温胁迫症状的番茄幼苗叶片RGB图像

    Figure  4.   RGB images of tomato seedling leaves with different symptoms of high temperature stress

    图  5   Mask-RCNN架构

    FPN:特征金字塔网络;RPN:区域生成网络;RoI:感兴趣区域

    Figure  5.   Mask-RCNN architecture

    FPN: Feature pyramid network; RPN: Region proposal network; RoI: Region of interest

    图  6   番茄幼苗叶片热红外图像

    Figure  6.   Thermal infrared images of tomato seedling leaves

    图  7   3种不同主干特征提取网络不同程度胁迫预测结果

    Figure  7.   Prediction results of different stresses on three different backbone feature extraction networks

    图  8   Mask-RCNN+ResNet101模型损失函数值变化曲线

    Figure  8.   Change curve of loss function value of Mask-RCNN+ResNet101 model

    图  9   4种不同模型测试集分类结果混淆矩阵

    绿色方格代表分类正确样本,其中数字为样本数量和所占总体样本的比例;红色方格代表分类错误样本;浅灰色方格中数字代表测试集样本每一行和列的真阳性率(绿色)和假阴性率(红色);深灰色方格中数字代表测试集样本的总体准确率(绿色)和总体错误率(红色)

    Figure  9.   Confusion matrices of classification results of test sets of four different models

    Green squares represent correctly classified samples, where the numbers are the number of samples and the proportion of the total sample; Red squares represent misclassified samples; The numbers in the light gray squares represent the true positive rate (green) and false negative rate (red) for each row and column of the test set sample; The numbers in the dark gray squares represent the overall accuracy (green) and overall error (red) of the test set samples

    表  1   番茄幼苗冠层热红外温度参数变化表

    Table  1   Variation table of thermal infrared temperature parameters of tomato seedling canopy

    试验时刻1)Experiment time 最大温度 Maximum temperature 最小温度 Minimum temperature 最大温差 Maximum temperature difference 平均温度 Average temperature 温度标准差 Temperature standard deviation
    D1 12:00 30.3 21.7 8.6 24.6230 1.5155
    D1 16:00 31.6 21.7 9.9 25.1910 1.8274
    D2 12:00 32.4 22.9 9.5 25.1721 1.3691
    D2 16:00 31.2 22.7 8.5 25.9238 1.4817
    D3 12:00 31.1 23.1 8.0 26.8236 1.7791
    D3 16:00 32.0 23.1 8.9 27.3416 1.7418
    D4 12:00 31.8 24.3 7.5 27.0165 1.3433
    D4 16:00 32.9 24.1 8.8 27.5235 1.5168
    D5 12:00 33.1 24.6 8.5 29.0355 1.7989
    D5 16:00 34.2 25.2 9.0 30.0699 1.8027
    D6 12:00 33.5 24.8 8.7 29.7350 1.7264
    D6 16:00 34.4 25.2 9.2 30.1302 1.8432
     1) D1~D6分别表示第1~6天  1) D1-D6 indicate day 1-6, respectively
    下载: 导出CSV

    表  2   主干特征提取网络性能参数

    Table  2   Performance parameters of backbone feature extraction network

    网络 Network 深度 Depth 大小/MB Size 参数量 Millions parameter quantity 图像输 入大小 Image input size 特征提取层 Feature extraction layer 池化层输出大小 Output size of pooling layer
    ResNet50 50 96 25.6 224×224 Block_13_expand_relu 14×14
    ResNet101 101 167 44.6 224×224 Mixed7 17×17
    MobileNet 54 13 3.5 224×224 Res4b_22_relu 14×14
    下载: 导出CSV

    表  3   温度特征指标的相关系数和主成分贡献率

    Table  3   Correlation coefficient and principal component contribution rate of temperature characteristic indicators

    特征指标 Characteristic indicator 相关系数 Correlation coefficient 主成分贡献率/% Principal component contribution rate
    最大值( $ {T}_{1} $) Maximum 0.1451 3.24
    最小值( $ {T}_{2} $) Minimum −0.0385 0.07
    平均值( $ {T}_{3} $) Average −0.1328 0.23
    标准差( $ {T}_{4} $) Standard deviation −0.0437 0.01
    规范标准化值( $ {T}_{5} $) Canonical normalized −0.1466 4.17
    规范替换值( $ {T}_{6} $) Canonical replacement −0.1657 6.44
    变异系数( $ {T}_{7} $) Coefficient of variation 0.2749 34.37
    冠气温差( $ {T}_{8} $) Crown temperature difference 0.1563 2.28
    信息熵( $ {T}_{9} $) Information entropy 0.2134 16.62
    20~23 ℃温度频率( $ {T}_{10} $) Temperature frequency −0.1360 0.25
    23~26 ℃温度频率( $ {T}_{11} $) Temperature frequency −0.2041 9.76
    26~29 ℃温度频率( $ {T}_{12} $) Temperature frequency 0.0826 0.03
    29~32 ℃温度频率( $ {T}_{13} $) Temperature frequency 0.2279 21.85
    32~35 ℃温度频率( $ {T}_{14} $) Temperature frequency 0.1491 0.68
    下载: 导出CSV

    表  4   ${\rm{IoU}}=0.5$ 时3种不同主干特征提取网络Mask-RCNN模型分割精度

    Table  4   Segmentation accuracy of Mask-RCNN model of three different backbone feature extraction networks when IoU=0.5

    主干特征提取网络 Backbone feature extraction network 胁迫等级 Image stress level 查准率/% Precision 查全率/% Recall $ {F}_{1} $/% $ \mathrm{m}\mathrm{A}\mathrm{P} $/%
    ResNet50 中度 Mild 90.4 50.5 64.8 75.7
    重度 Severe 83.2 45.9 59.2 71.6
    ResNet101 中度 Mild 91.3 52.4 66.6 77.3
    重度 Severe 85.7 47.2 60.9 73.8
    MobileNet 中度 Mild 86.0 46.8 60.6 69.9
    重度 Severe 80.5 43.4 56.4 66.5
    下载: 导出CSV

    表  5   不同分类网络模型的苗期番茄高温胁迫分级精度

    Table  5   Classification accuracy of high temperature stress of tomato at seedling stage with different classification network model

    模型类别 Model category 准确率/% Accuracy 查准率/% Precision 查全率/% Recall $ {F}_{1} $/% ${{\rm{MCC}}}$/%
    朴素贝叶斯 Naive Bayesian (NB) 90.6 92.7 89.9 91.3 81.0
    支持向量机 Support vector machine (SVM) 93.3 96.8 91.0 93.8 86.8
    k−最近邻 k-nearest neighbor (kNN) 91.7 92.7 91.8 92.2 83.3
    BP神经网络 BP neural network (BPNN) 95.6 96.9 94.9 95.9 91.1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-20
  • 网络出版日期:  2023-05-17
  • 刊出日期:  2023-01-09

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