WO2020119067A1 - 一种基于光谱成像技术的豆象危害情况观测技术 - Google Patents

一种基于光谱成像技术的豆象危害情况观测技术 Download PDF

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WO2020119067A1
WO2020119067A1 PCT/CN2019/092863 CN2019092863W WO2020119067A1 WO 2020119067 A1 WO2020119067 A1 WO 2020119067A1 CN 2019092863 W CN2019092863 W CN 2019092863W WO 2020119067 A1 WO2020119067 A1 WO 2020119067A1
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seeds
bean
beans
wavelength
spectral
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PCT/CN2019/092863
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French (fr)
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薛晨晨
张勤雪
陈新
袁星星
宋塔普拉给特
斯乃文披拉沙
陈景斌
张红梅
陈华涛
崔晓艳
刘晓庆
顾和平
闫强
吴然然
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江苏省农业科学院
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Publication of WO2020119067A1 publication Critical patent/WO2020119067A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/6402—Atomic fluorescence; Laser induced fluorescence

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  • the invention belongs to the technical field of plant diseases and insect pests research, and relates to a rapid observation technique of the damage of bean elephants to mung beans.
  • Beans are rich in nutrients and have the same source of medicine and food. They are important crops for the development of modern functional foods and an important source for ordinary people to obtain high-quality protein. Its various processed products are traditional cuisines that our people love. Bean elephant is one of the most harmful insects to mung beans and all other legumes, especially the impact on the storage process. It is extremely devastating, causing a large loss of beans in the warehouse. Bean warehouses generally have a huge storage volume, and bean elephants lay eggs with a diameter of about 1 mm. At this stage, there is no professional instrument to observe and judge the hazards of bean elephants. They can only rely on visual observation. The work is cumbersome and subjectively affected. How can it be done? Effective and quick judgment of the harm of beans to the beans in the warehouse. How to select high-throughput resistant bean varieties is a key issue in bean breeding research and warehousing research.
  • the purpose of the present invention is to provide a technique for observing the damage situation of bean elephants based on spectral imaging technology.
  • the method is convenient to operate and has wide applicability. A large amount of targets can be screened in one super-action, reducing human judgment errors and accurate judgment.
  • a method for observing the damage of bean elephants based on spectral imaging technology includes the following steps:
  • the long-wavelength UV ultraviolet light emitted by the multispectral fluorescence imaging has a wavelength range of 320 nm to 400 nm, which can excite the target to produce fluorescence with 4 characteristic peaks Spectrum, the wavelengths of the four peaks are blue 440nm, green 520nm, red 690nm and far infrared 740nm.
  • a multi-spectral fluorescence imaging system capable of emitting long-wavelength UV ultraviolet light is used to obtain a seed spectral image, and the selected characteristic wavelength is 440 nm or 520 nm.
  • the spectral image of the characteristic wavelength use the corresponding software FluorCam7Software to obtain the image and Numeric Avg data at the wavelength of 440nm or 520nm; according to whether the image shows red at the wavelength of 440nm or 520nm or according to the change of the Numeric Avg data, the result can be used to judge the batch Bean-like damage to bean seeds.
  • the beans are suitable for all beans harmed by beans, and further preferably soybeans, mung beans, red beans, peas, fava beans, kidney beans or cowpea.
  • a method for distinguishing the resistance of different legume varieties to the harm of legumes including the observation of the different legume varieties after being invaded by the bean elephants for a period of time without multi-spectrum fluorescence imaging system capable of emitting long-wave UV ultraviolet light Spectral images of characteristic wavelengths, using the corresponding software FluorCam7Software, obtained images and Numeric Avg data at 440nm or 520nm wavelengths; by comparing the amplitude of the spectral changes of different bean varieties, the results can be used to determine the hazards of different bean varieties to beans Different resistance.
  • the long-wavelength UV ultraviolet light emitted by multispectral fluorescence imaging has a wavelength range of 320nm-400nm, which can excite the target to generate a fluorescence spectrum with 4 characteristic peaks, and the wavelengths of the 4 peaks are blue 440nm, respectively , Green light 520nm, red light 690nm and far infrared 740nm.
  • the present invention provides a technique for observing the damage of bean elephants based on spectral imaging technology, which can be used to quickly, efficiently and accurately observe the damage of bean elephants in a batch of bean seeds. , The results are not affected by human factors, and get accurate results quickly and efficiently.
  • Figure 1 is an image of seeds of mung bean variety Sulu No. 2 at a wavelength of 440nm. Three of the seeds are obviously red, indicating that the seeds have been harmed by bean elephants, which corresponds to the statistical results of the naked eye.
  • Figure 2 is an image of seeds of mung bean variety Sulu No. 2 at a wavelength of 520nm. Three of the seeds are obviously red, indicating that the seeds are harmed by the bean, and the other two seeds are partially red, indicating that the two seeds are affected. Slight bean hazard; corresponds to naked eye statistics
  • Figure 3 is the primary color map of the seed of mung bean variety Sulu No. 2 which is damaged by bean elephant in red in the above picture
  • Fig. 4 is the Numeric Avg data of the mung bean variety Sulu No. 2 seed under the image processing wavelengths of F440 and F520
  • Figure 5 is the difference in the change of mung bean varieties with different resistance at the F440 wavelength of image processing.
  • the ordinate indicates the Numeric Avg value after infection/Numeric Avg value before infection
  • Figure 6 is an image of seeds of mung bean variety Sulu No. 6 at a wavelength of 440nm. Three of the seeds are obviously red, indicating that the seeds are harmed by bean elephants, which corresponds to the statistical results of the naked eye.
  • Figure 7 is an image of seeds of mung bean variety Sulu No. 6 at a wavelength of 520nm. Three of the seeds clearly show red, indicating that the seeds are harmed by bean elephants, which corresponds to the statistical results of the naked eye.
  • Fig. 8 is the primary color map of the seed of mung bean variety Sulu No. 6 in red shown in Fig. 7
  • Figure 9 is an image of seeds of red bean variety Suhong No. 2 at a wavelength of 440nm. Three of the seeds clearly show red, indicating that the seeds are harmed by bean elephants, which corresponds to the statistical results of the naked eye.
  • Fig. 10 is an image of seeds of red adzuki bean variety Suhong 2 at a wavelength of 520nm. Three of the seeds clearly show red, indicating that the seeds have been harmed by bean elephants; corresponding to the statistical results of the naked eye
  • Fig. 11 is the primary color map of the red adzuki bean variety Suhong No. 2 harmed by the bean elephant in red in Fig. 10
  • Seed screening select mung bean variety Sulu No. 50g (more than 700 seeds), observe the surface of each newly harvested seed that year, select seeds with normal surface and no disease performance, and carry out the next test.
  • FluorCam fluorescence imaging system is used to observe the mung bean seeds placed on the platform.
  • the system's long-wave UV ultraviolet light (320nm-400nm) can stimulate the target to produce a fluorescent spectrum with a characteristic peak.
  • After obtaining the spectral image use Corresponding software FluorCam7Software, available images at F440 and F520 wavelengths ( Figure 1-2) and Numeric Avg data (Table 1). According to the data in Table 1, the performance of each mung bean seed at the wavelengths of F440 and F520 is plotted ( Figure 4). It can be clearly seen that the value of 3 seeds is obviously higher than the whole group, and the value of 2 seeds is slightly higher than the group value.
  • Seed screening select 4 mung bean varieties with anti-bean elephant varieties 1, anti-bean elephant varieties 2, non-resistant bean elephant varieties 1, non-resistant bean elephant varieties 2, conduct surface observation on each newly harvested seed of the year and select 6 seeds with normal surface and no disease performance were tested in the next step.
  • Seed screening select mung bean variety Sulu No. 6 and observe the surface of each newly harvested seed of the year, select 10 seeds with normal surface for the next test.
  • Area 3 500 5148 5235 0 Area 4 604 5752 6173 0 Area 5 658 27989 28465 14 Area 6 453 5140 5088 0 Area 7 541 4626 5109 0 Area 8 640 23206 21547 11 Area 9 509 5173 6255 0 Area 10 652 28044 27654 17 Area11 623 6913 6945 0 Area12 704 6666 8812 0 Area 13 523 5238 6129 0
  • Seed screening select the red bean variety Suhong 2 and observe the surface of each newly harvested seed of the year, select 11 seeds with normal surface and carry out the next test.
  • the FluorCam fluorescence imaging system is used to observe the seeds placed on the platform.
  • the system's long-wave UV ultraviolet light (320nm-400nm) can stimulate the target to produce a fluorescent spectrum with a characteristic wave peak.
  • After obtaining the spectral image use the corresponding Software FluorCam7Software, available images and Numeric Avg data at F440 and F520 wavelengths (Table 3). According to the data in the graph and table, it can be clearly seen that the value of the three seeds is significantly higher than that of the entire population. It can be proved from the data of artificial egg counting that these three seeds carry a certain degree of bean weevil eggs. The test proves that the data obtained by this technology can be used to preliminarily judge the number of seeds harmed by red bean beans.
  • Seed screening select the bean variety Su Chaidou No. 6, and observe the surface of each newly harvested seed of the year, select 10 seeds with normal surface, and carry out the next test.
  • the FluorCam fluorescence imaging system is used to observe the seeds placed on the platform.
  • the system's long-wave UV ultraviolet light (320nm-400nm) can stimulate the target to produce a fluorescent spectrum with a characteristic wave peak.
  • After obtaining the spectral image use the corresponding Software FluorCam7Software, available images and Numeric Avg data at F440 and F520 wavelengths (Table 4). According to the data in the graph and table, it can be clearly seen that the value of the three seeds is significantly higher than that of the entire population. It can be proved from the data of artificial egg counting that these three seeds carry a certain degree of bean weevil eggs. The test proves that the data obtained by this technique can be used to preliminarily judge the number of bean-like harmful seeds in kidney bean seeds.
  • Seed screening select soybean variety Sudou No. 10, observe the surface of each newly harvested seed of the year, select 11 seeds with normal surface, and carry out the next test.
  • the FluorCam fluorescence imaging system is used to observe the seeds placed on the platform.
  • the system's long-wave UV ultraviolet light (320nm-400nm) can stimulate the target to produce a fluorescent spectrum with a characteristic wave peak.
  • After obtaining the spectral image use the corresponding Software FluorCam7Software, available images and Numeric Avg data at F440 and F520 wavelengths (Table 5). According to the data in the graph and table, it can be clearly seen that the value of the three seeds is significantly higher than that of the entire population. It can be proved from the data of artificial egg counting that these three seeds carry a certain degree of bean weevil eggs. The experiment proves that the data obtained by this technique can be used to preliminarily judge the number of soybean seeds in soybean seeds.
  • Seed screening select pea variety Supi No. 2 to observe the surface of each newly harvested seed of the year, select 10 seeds with normal surface and carry out the next test.
  • the FluorCam fluorescence imaging system is used to observe the seeds placed on the platform.
  • the system's long-wave UV ultraviolet light (320nm-400nm) can stimulate the target to produce a fluorescent spectrum with a characteristic wave peak.
  • After obtaining the spectral image use the corresponding Software FluorCam7Software, available images and Numeric Avg data at F440 and F520 wavelengths (Table 5). According to the data in the graph and table, it can be clearly seen that the value of the three seeds is significantly higher than that of the entire population. It can be proved from the data of artificial egg counting that these three seeds carry a certain degree of bean weevil eggs. The test proves that the data obtained by this technique can be used to preliminarily judge the number of bean-like harmful seeds in pea seeds.

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Abstract

一种基于光谱成像技术的豆象危害情况观测技术，属于植物病虫害研究技术领域，该方法可以随机选取豆类种子，将种子静置与平面上，利用光谱成像系统获取种子光谱图像，使用软件FluorCam7 Software，获得的特征波长F440或F520下的图像和Numeric Avg，结合图像和数据分析判断该批豆类种子中受到豆象危害的情况。该方法操作方便，适用性广，可大批量超作，减少人为判断误差，判断精确。

Description

一种基于光谱成像技术的豆象危害情况观测技术 技术领域
本发明属于植物病虫害研究技术领域，涉及豆象对绿豆危害情况的快速观测技术。
背景技术
豆类营养丰富，医食同源，是现代功能性食品开发的重要作物，也是普通民众获取高质量蛋白质的重要来源，其多种加工品是我国人民喜爱的传统美食。豆象是对绿豆以及其他所有豆类危害最严重的昆虫之一，特别是对仓储过程中的影响，毁灭性极强，造成仓库中豆类大量损失。豆类仓库一般存储数量巨大，而豆象产卵直径约1毫米，现阶段还未有专业的仪器去观测判断豆象危害情况，只能依靠肉眼观测，工作繁琐且受到主观影响大，如何能有效快速的判断豆象对仓库中豆类的危害情况，如何能高通量的筛选出抗豆象品种，是豆类育种研究和仓储研究的关键性问题。
因此，如何快速、高效的判断绿豆和其他豆类的豆象危害情况，是现阶段研究的关键。但目前相关报告并不多见。
发明内容
本发明的目的是提供一种基于光谱成像技术的豆象危害情况观测技术，该方法操作方便，适用性广，一次超作可筛选大批量目标，减少人为判断误差，判断精确。
本发明采用以下技术方案：
一种基于光谱成像技术的豆象危害情况观测方法，包括以下步骤：
(1)随机选择豆类种子一批；
(2)将种子放置于能够发射长波段UV紫外光的多光谱荧光成像平面上，豆类在被长波段UV紫外光激发产生荧光光谱，选择波长440nm或520nm下观测的荧光光谱特征图像，通过是否显示红色光谱判断绿豆种子表面是否有豆象产卵，以此结果判断该批次豆类种子的豆象损害情况。
作为本发明所述的基于光谱成像技术的豆象危害情况观测方法的优选，多光谱荧光成像发射的长波段UV紫外光波长范围为320nm－400nm，可以激发目标产生具有4个特征性波峰的荧光光谱，4个波峰的波长分别为蓝光440nm、绿光520nm、红光690nm和远红外740nm。
作为本发明所述的基于光谱成像技术的豆象危害情况观测方法的优选，利用能够发射长波段UV紫外光的多光谱荧光成像系统获取种子光谱图像，所选用的特征波长是440nm或520nm，获得特征波长的光谱图像后，利用相应软件FluorCam7 Software，获得的440nm或520nm波长下图像和Numeric Avg数据；根据440nm或520nm波长下图像是否显示红色或者根据Numeric Avg数据的变化以此结果判断该批次豆类种子的豆象损害情况。
所述的豆类适用于所有豆象危害的豆类，进一步优选大豆、绿豆、红小豆、豌豆、蚕豆、芸豆或豇豆。
一种区分不同豆类品种对豆象危害抗性的方法，包括用能够发射长波段UV紫外光的多光谱荧光成像系统观测不同豆类品种在没有豆象和放置一段时间受豆象侵害后的特征波长的光谱图像，利用相应软件FluorCam7 Software，获得的440nm或520nm波长下图像和Numeric Avg数据；通过比较不同豆类品种光谱变化的幅度，以此结果可以判断不同豆类品种对豆象危害的不同抗性。
作为本发明所述的方法，：多光谱荧光成像发射的长波段UV紫外光波长范围为320nm－400nm，可以激发目标产生具有4个特征性波峰的荧光光谱，4个波峰的波长分别为蓝光440nm、绿光520nm、红光690nm和远红外740nm。
有益效果：
现阶段豆类在仓储过程中受到豆象危害严重，但由于豆象卵过于微小，难于观查，受人为因素影响大，至今也未见对其进行相关检测的技术报道。本发明提供了一种基于光谱成像技术的豆象危害情况观测技术，可用于快速、高效、准确的观测一批豆类种子中豆象的危害情况，该技术可用于豆象危害的各类种子，结果不受人为因素影响，高效快速得到准确的结果。通过该技术的利用，能初步的分析出豆类材料对豆象的抗性，极大的加快豆类抗豆象品种的选育进程，促进豆类抗豆象品种的应用，减少化学杀豆象药剂的使用，对食品健康安全和环境保护有着重要的意义。
附图说明
图1是波长440nm下绿豆品种苏绿2号种子成像图，其中有三颗种子明显显示红色，表明种子受到了豆象的危害，与肉眼统计结果对应
图2是波长520nm下绿豆品种苏绿2号种子成像图，其中三颗种子明显显示红色，表明种子受 到了豆象的危害，另外还有两颗种子部分区域显红色，表明这两颗种子受轻微的豆象危害；与肉眼统计结果对应
图3是上图中红色显示被豆象危害的绿豆品种苏绿2号种子原色图
图4是图像处理F440和F520波长下绿豆品种苏绿2号种子Numeric Avg数据
图5是图像处理F440波长下不同抗性的绿豆品种变化差异,纵坐标表示侵染后Numeric Avg数值/侵染前Numeric Avg数值
图6是波长440nm下绿豆品种苏绿6号种子成像图，其中有三颗种子明显显示红色，表明种子受到了豆象的危害，与肉眼统计结果对应
图7是波长520nm下绿豆品种苏绿6号种子成像图，其中三颗种子明显显示红色，表明种子受到了豆象的危害，与肉眼统计结果对应
图8是图7中红色显示被豆象危害的绿豆品种苏绿6号种子原色图
图9是波长440nm下红小豆品种苏红2号种子成像图，其中有三颗种子明显显示红色，表明种子受到了豆象的危害，与肉眼统计结果对应
图10是波长520nm下红小豆品种苏红2号种子成像图，其中三颗种子明显显示红色，表明种子受到了豆象的危害；与肉眼统计结果对应
图11是图10中红色显示被豆象危害的红小豆品种苏红2号种子原色图
具体实施方式
实施例1
1)种子筛选：选择绿豆品种苏绿2号50g(700粒以上)，对当年新采收的每个种子进行表面观察，挑选表面正常，无病害表现的种子，进行下一步试验。
2)混入受到豆象危害的绿豆种子：选择已受到豆象危害的绿豆种子5g左右(73粒)，混入上述50g正常的种子中，随机混匀后，随机抓取一把种子(52粒)，原色图见图3,放入观测平台上。
3)观测分析：运用FluorCam荧光成像系统观测放置于平台上的绿豆种子，该系统长波段UV紫外光(320nm－400nm)，可以激发目标产生具有特征性波峰的荧光光谱，获得光谱图像后，利用相应软件FluorCam7 Software，可获得的F440和F520波长下图像(图1-2)和Numeric Avg数据(表1)。根据表1数据绘制出在F440和F520波长下各个绿豆种子的表现情况(图4)。可以明显看到有3个种子的数值明显高于整个群体，另外有2个种子的数值略高于群体值，可 以从人工数卵的数据结果证明，这5个种子上都带有一定程度的豆象虫卵。试验证明可以通过该技术获得的数据，初步判断绿豆种子中豆象危害种子的数量。
表1图像处理F440和F520波长下Numeric Avg数据
  Size[pixels] F440 F520 实际产卵数
Area 1 208 21912 28486 0
Area 2 172 18467 23071 0
Area 3 277 22310 33078 0
Area 4 275 21268 29175 0
Area 5 225 22419 27744 0
Area 6 168 18293 23215 0
Area 7 212 28238 40735 0
Area 8 580 23902 35241 0
Area 9 183 24585 32544 0
Area 10 220 21044 28488 0
Area 11 128 18810 24175 0
Area 12 322 25571 41165 0
Area 13 183 17950 22307 0
Area 14 286 23076 35221 0
Area 15 224 21913 27688 0
Area 16 174 19096 24008 0
Area 17 163 19100 23638 0
Area 18 279 22826 35502 0
Area 19 317 23642 31436 0
Area 20 240 19603 22358 0
Area 21 27 21926 24253 0
Area 22 247 22754 27451 0
Area 23 97 21165 32424 0
Area 24 250 25569 33504 0
Area 25 379 27902 51467 0
Area 26 274 23078 26839 0
Area 27 287 25360 41102 0
Area 28 350 25856 32511 0
Area 29 250 23012 38567 0
Area 30 311 65386 91173 27
Area 31 96 17958 21746 0
Area 32 34 19195 18036 0
Area 33 231 24910 33064 0
Area 34 211 26543 33877 0
Area 35 229 24007 37613 0
Area 36 441 17379 28062 0
Area 37 239 29455 38008 0
Area 38 600 27026 35046 0
Area 39 115 15841 18191 0
Area 40 231 21571 25248 0
Area 41 195 22653 33546 0
Area 42 354 44401 72998 3
Area 43 309 29374 49006 0
Area 44 260 65243 91418 30
Area 45 237 24037 35985 0
Area 46 333 28934 51825 0
Area 47 215 21321 33723 0
Area 48 238 26460 36223 0
Area 49 264 65513 92227 22
Area 50 152 15415 25091 0
Area 51 393 43803 69038 2
Area 52 175 19541 32467 0
实施例2(图5)
1)种子筛选：选择4个绿豆品种抗豆象品种1、抗豆象品种2、非抗豆象品种1、非抗豆象品种2，对当年新采收的每个种子进行表面观察，挑选表面正常，无病害表现的种子6粒，进行下一步试验。
2)混入受到豆象危害的绿豆种子：选择同样的4个绿豆品种若干粒，单独放置在独立容器中，添加同样数量和大小的绿豆象10只，共培养7天后，分别取6粒种子，与步骤1中种子并排放入观测平台上。
3)观测分析：运用FluorCam荧光成像系统观测放置于平台上的绿豆种子，该系统长波段UV紫外光(320nm－400nm)，可以激发目标产生具有特征性波峰的荧光光谱，获得光谱图像后，利用相应软件FluorCam7 Software，可获得的F440和F520波长下图像和Numeric Avg数据，利用F440的数据值，将受到豆象侵染后的种子的数值除以侵染前的数值，观察获得的比值可以发现，抗性品种的值远低于非抗性品种(图5)，可以用该方法初步判断绿豆品种对豆象的抗性大小。
实施例3(图6～8，表2)
1)种子筛选：选择绿豆品种苏绿6号，对当年新采收的每个种子进行表面观察，挑选表面正常的种子10粒，进行下一步试验。
2)混入受到豆象危害的绿豆种子：选择已受到豆象危害的绿豆种子3粒，混入上述正常的种子中，随机混匀后，随机放入观测平台上。
3)观测分析：运用FluorCam荧光成像系统观测放置于平台上的绿豆种子，该系统长波段UV紫外光(320nm－400nm)，可以激发目标产生具有特征性波峰的荧光光谱，获得光谱图像后，利用相应软件FluorCam7 Software，可获得的F440和F520波长下图像和Numeric Avg数据(表2)。根据图和表数据可以明显看到有3个种子的数值明显高于整个群体，可以从人工数卵的数据结果证明，这3个种子上都带有一定程度的豆象虫卵。试验证明可以通过该技术获得的数据，初步判断绿豆种子中豆象危害种子的数量。
表2图像处理F440和F520波长下Numeric Avg数据
  Size[pixels] F440 F520 实际产卵数
Area 1 579 5173 5436 0
Area 2 633 6084 8069 0
Area 3 500 5148 5235 0
Area 4 604 5752 6173 0
Area 5 658 27989 28465 14
Area 6 453 5140 5088 0
Area 7 541 4626 5109 0
Area 8 640 23206 21547 11
Area 9 509 5173 6255 0
Area 10 652 28044 27654 17
Area 11 623 6913 6945 0
Area 12 704 6666 8812 0
Area 13 523 5238 6129 0
实施例4(图9～11，表3)
1)种子筛选：选择红小豆品种苏红2号，对当年新采收的每个种子进行表面观察，挑选表面正常的种子11粒，进行下一步试验。
2)混入受到豆象危害的种子：选择已受到豆象危害的红小豆种子3粒，混入上述正常的种子中，随机混匀后，随机放入观测平台上。
3)观测分析：运用FluorCam荧光成像系统观测放置于平台上的种子，该系统长波段UV紫外光(320nm－400nm)，可以激发目标产生具有特征性波峰的荧光光谱，获得光谱图像后，利用相应软件FluorCam7 Software，可获得的F440和F520波长下图像和Numeric Avg数据(表3)。根据图和表数据可以明显看到有3个种子的数值明显高于整个群体，可以从人工数卵的数据结果证明，这3个种子上都带有一定程度的豆象虫卵。试验证明可以通过该技术获得的数据，初步判断红小豆种子中豆象危害种子的数量。
表3图像处理F440和F520波长下Numeric Avg数据
Figure PCTCN2019092863-appb-000001
Figure PCTCN2019092863-appb-000002
实施例5(表4)
1)种子筛选：选择菜豆品种苏菜豆6号，对当年新采收的每个种子进行表面观察，挑选表面正常的种子10粒，进行下一步试验。
2)混入受到豆象危害的种子：选择已受到豆象危害的菜豆种子2粒，混入上述正常的种子中，随机混匀后，随机放入观测平台上。
3)观测分析：运用FluorCam荧光成像系统观测放置于平台上的种子，该系统长波段UV紫外光(320nm－400nm)，可以激发目标产生具有特征性波峰的荧光光谱，获得光谱图像后，利用相应软件FluorCam7 Software，可获得的F440和F520波长下图像和Numeric Avg数据(表4)。根据图和表数据可以明显看到有3个种子的数值明显高于整个群体，可以从人工数卵的数据结果证明，这3个种子上都带有一定程度的豆象虫卵。试验证明可以通过该技术获得的数据，初步判断菜豆种子中豆象危害种子的数量。
表4图像处理F440和F520波长下Numeric Avg数据
Figure PCTCN2019092863-appb-000003
Figure PCTCN2019092863-appb-000004
实施例6(表5)
1)种子筛选：选择大豆品种苏豆10号，对当年新采收的每个种子进行表面观察，挑选表面正常的种子11粒，进行下一步试验。
2)混入受到豆象危害的种子：选择已受到豆象危害的大豆种子2粒，混入上述正常的种子中，随机混匀后，随机放入观测平台上。
3)观测分析：运用FluorCam荧光成像系统观测放置于平台上的种子，该系统长波段UV紫外光(320nm－400nm)，可以激发目标产生具有特征性波峰的荧光光谱，获得光谱图像后，利用相应软件FluorCam7 Software，可获得的F440和F520波长下图像和Numeric Avg数据(表5)。根据图和表数据可以明显看到有3个种子的数值明显高于整个群体，可以从人工数卵的数据结果证明，这3个种子上都带有一定程度的豆象虫卵。试验证明可以通过该技术获得的数据，初步判断大豆种子中豆象危害种子的数量。
表5图像处理F440和F520波长下Numeric Avg数据
Figure PCTCN2019092863-appb-000005
Figure PCTCN2019092863-appb-000006
实施例7(表6)
1)种子筛选：选择豌豆品种苏豌2号，对当年新采收的每个种子进行表面观察，挑选表面正常的种子10粒，进行下一步试验。
2)混入受到豆象危害的种子：选择已受到豆象危害的豌豆种子2粒，混入上述正常的种子中，随机混匀后，随机放入观测平台上。
3)观测分析：运用FluorCam荧光成像系统观测放置于平台上的种子，该系统长波段UV紫外光(320nm－400nm)，可以激发目标产生具有特征性波峰的荧光光谱，获得光谱图像后，利用相应软件FluorCam7 Software，可获得的F440和F520波长下图像和Numeric Avg数据(表5)。根据图和表数据可以明显看到有3个种子的数值明显高于整个群体，可以从人工数卵的数据结果证明，这3个种子上都带有一定程度的豆象虫卵。试验证明可以通过该技术获得的数据，初步判断豌豆种子中豆象危害种子的数量。
表6图像处理F440和F520波长下Numeric Avg数据
Figure PCTCN2019092863-appb-000007
Figure PCTCN2019092863-appb-000008

Claims (6)

  1. 一种基于光谱成像技术的豆象危害情况观测方法，其特征在于包括以下步骤：
    (1)随机选择豆类种子一批；
    (2)将种子放置于能够发射长波段UV紫外光的多光谱荧光成像平面上，豆类在被长波段UV紫外光激发产生荧光光谱，选择波长440nm或520nm下观测的荧光光谱特征图像，通过是否显示红色光谱判断绿豆种子表面是否有豆象产卵，以此结果判断该批次豆类种子的豆象损害情况。
  2. 根据权利要求1所述的基于光谱成像技术的豆象危害情况观测方法，其特征在于：多光谱荧光成像发射的长波段UV紫外光波长范围为320nm－400nm，可以激发目标产生具有4个特征性波峰的荧光光谱，4个波峰的波长分别为蓝光440nm、绿光520nm、红光690nm和远红外740nm。
  3. 根据权利要求1所述的基于光谱成像技术的豆象危害情况观测方法，其特征在于：利用能够发射长波段UV紫外光的多光谱荧光成像系统获取种子光谱图像，所选用的特征波长是440nm或520nm，获得特征波长的光谱图像后，利用相应软件FluorCam7 Software，获得的440nm或520nm波长下图像和Numeric Avg数据；根据440nm或520nm波长下图像是否显示红色或者根据Numeric Avg数据的变化以此结果判断该批次豆类种子的豆象损害情况。
  4. 根据权利要求1所述的基于光谱成像技术的豆象危害情况观测方法，其特征在于：所述的豆类适用于所有豆象危害的豆类，进一步优选大豆、绿豆、红小豆、豌豆、蚕豆、芸豆或豇豆。
  5. 一种区分不同豆类品种对豆象危害抗性的方法，其特征在于：包括用能够发射长波段UV紫外光的多光谱荧光成像系统观测不同豆类品种在没有豆象和放置一段时间受豆象侵害后的特征波长的光谱图像，利用相应软件FluorCam7 Software，获得的440nm或520nm波长下图像和Numeric Avg数据；通过比较不同豆类品种光谱变化的幅度，以此结果可以判断不同豆类品种对豆象危害的不同抗性。
  6. 根据权利要求5所述的方法，其特征在于：多光谱荧光成像发射的长波段UV紫外光波长范围为320nm－400nm，可以激发目标产生具有4个特征性波峰的荧光光谱，4个波峰的波长分别为蓝光440nm、绿光520nm、红光690nm和远红外740nm。
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102257379A (zh) * 2008-10-21 2011-11-23 克莫麦特公司 分析荧光颗粒的方法及装置
CN102348976A (zh) * 2009-01-30 2012-02-08 乔治亚大学研究基金公司 用于探测植物中的昆虫诱导的损害的非侵入性方法和设备
CN102621118A (zh) * 2012-03-18 2012-08-01 吉林大学 温室蔬菜病虫害的预警方法
US20120311744A1 (en) * 2011-06-06 2012-12-06 Erich E. Sirkowski Marked Cannabis For Indicating Medical Marijuana
WO2015054434A1 (en) * 2013-10-08 2015-04-16 Rutgers, The State University Of New Jersey Process for providing luminescence in or from a food product
CN104990888A (zh) * 2015-06-24 2015-10-21 河南工业大学 利用太赫兹成像技术检测储备粮食粒内虫害的方法
CN106896077A (zh) * 2017-04-28 2017-06-27 浙江大学 基于叶绿素荧光成像的转基因玉米草甘膦耐受性表型的检测方法
CN109387498A (zh) * 2018-12-12 2019-02-26 江苏省农业科学院 一种基于光谱成像技术的豆象危害情况观测技术

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4390787A (en) * 1980-10-29 1983-06-28 The United States Of America As Represented By The Secretary Of Agriculture Method and apparatus for automatic egg mass counting
CN105532585A (zh) * 2016-02-02 2016-05-04 江苏省农业科学院 一种抗豆象种质资源筛选装置

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102257379A (zh) * 2008-10-21 2011-11-23 克莫麦特公司 分析荧光颗粒的方法及装置
CN102348976A (zh) * 2009-01-30 2012-02-08 乔治亚大学研究基金公司 用于探测植物中的昆虫诱导的损害的非侵入性方法和设备
US20120311744A1 (en) * 2011-06-06 2012-12-06 Erich E. Sirkowski Marked Cannabis For Indicating Medical Marijuana
CN102621118A (zh) * 2012-03-18 2012-08-01 吉林大学 温室蔬菜病虫害的预警方法
WO2015054434A1 (en) * 2013-10-08 2015-04-16 Rutgers, The State University Of New Jersey Process for providing luminescence in or from a food product
CN104990888A (zh) * 2015-06-24 2015-10-21 河南工业大学 利用太赫兹成像技术检测储备粮食粒内虫害的方法
CN106896077A (zh) * 2017-04-28 2017-06-27 浙江大学 基于叶绿素荧光成像的转基因玉米草甘膦耐受性表型的检测方法
CN109387498A (zh) * 2018-12-12 2019-02-26 江苏省农业科学院 一种基于光谱成像技术的豆象危害情况观测技术

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