荧光成像技术在植物病害检测的应用研究进展

植物病害阻碍植物正常生长,对蔬菜产品品质安全造成威胁,病害检测可以有效控制植物病害发展,是提高果蔬品质安全和可持续生产能力的一个重要途径。所有的绿色植物在受到紫外光或可见光激发时,都会发出可见光波段的荧光。基于植物荧光的成像技术,是利用计算机模拟人眼的视觉功能,在不破坏植株外观且不影响其生长的情况下,获取植物的荧光图像,并通过图像处理算法,对植物的健康状况做出判断。本文对荧光成像技术及其在植物病害检测中的研究进行了综述,介绍了荧光成像的原理,分别从叶绿素荧光和其他色素荧光两方面总结了国内外研究现状。与可见光成像、高光谱成像、多光谱成像以及热成像技术相比,该技术具有可获取植物内部信息、受温湿度影响小、不局限于晴天采集、价格合理等优点。     

太赫兹波谱无损检测技术研究进展

太赫兹由于具有透视性、安全性和波谱分辨能力的特点,近年来得到了较快的发展和应用,成为无损检测的新技术,并被应用于国防、工业、半导体、通信、生物医学、制药、农产品及食品等多个领域.首先介绍了太赫兹波谱技术和成像技术的原理和特点,阐述了太赫兹技术在非金属材料特性和缺陷检测中的应用,并重点分析了近年来太赫兹技术在农产品及食品领域的应用新进展.最后,分析了太赫兹波谱无损检测技术的技术难点及问题,并对其发展趋势进行了展望.     

Agreement of Serum Spec cPL with the 1,2‐o‐Dilauryl‐Rac‐Glycero Glutaric Acid‐(6′‐methylresorufin) Ester (DGGR) Lipase Assay and with Pancreatic Ultrasonography in Dogs with Suspected Pancreatitis

Background

Spec cPL is the most sensitive and specific test for diagnosing pancreatitis in dogs. Its results have not been compared to those of the 1,2‐o‐dilauryl‐rac‐glycero‐3‐glutaric acid‐(6′‐methylresorufin) ester (DGGR) lipase assay or those of abdominal ultrasonography.

Objectives

To investigate agreement of Spec cPL with DGGR lipase activity and pancreatic ultrasonography in dogs with suspected pancreatitis.

Animals

One hundred and forty‐two dogs.

Methods

DGGR lipase activity (reference range, 24–108 U/L) and Spec cPL were measured using the same sample. The time interval between ultrasonography and lipase determinations was <24 hours. The agreement of the 2 lipase assays at different cutoffs and the agreement between pancreatic ultrasonography and the 2 tests were assessed using Cohen''s kappa coefficient (κ).

Results

DGGR lipase (>108, >216 U/L) and Spec cPL (>200 μg/L) had κ values of 0.79 (95% confidence interval [CI], 0.69–0.9) and 0.70 (CI, 0.58–0.82). DGGR lipase (>108, >216 U/L) and Spec cPL (>400 μg/L) had κ values of 0.55 (CI, 0.43–0.67) and κ of 0.80 (CI, 0.71–0.9). An ultrasonographic diagnosis of pancreatitis and DGGR lipase (>108, >216 U/L) had κ values of 0.29 (CI, 0.14–0.44) and 0.35 (CI, 0.18–0.52). Ultrasonographically diagnosed pancreatitis and Spec cPL (>200, >400 μg/L) had κ values of 0.25 (CI, 0.08–0.41) and 0.27 (CI, 0.09–0.45).

Conclusions and Clinical Importance

Although both lipase assays showed high agreement, agreement between ultrasonography and lipase assays results was only fair. Because lipase results are deemed more accurate, ultrasonography results should be interpreted carefully.     

Accuracy of a machine-vision pellet detection system

Video cameras are employed on net pen fish farms for monitoring food pellet levels near the cage bottom. Herein, the accuracy of a new machine-vision system for the identification of a feed-wastage event and its response time are reported. Research involved novel tests and definitions, video footage recorded under different stocking and environmental conditions, and numerical filters to reduce the effects of misclassifications and patchiness of the pellet wastage record on overall system accuracy. Single-frame food pellet detection accuracy was based on the difference between computer-generated and actual frame counts of pellets greater than 30 pixels in size. A pellet wastage event was defined as a median of three or more visible pellets per frame. During tests on still video frames from different feeding events, the system missed 0.1±0.1 to 1.3±0.4 pellets frame⁻¹, and miss-classified as pellets 0±0.1 to 2.3±0.5 objects frame⁻¹ (n=264). Most missed pellet images were on top of fish images. Waste matter accounted for 65% of the misclassifications, while particles associated with poor camera maintenance accounted for approximately 24%. The average response time to a pellet wastage event was 5.1 and 11.4 s for sampling rates of 2 frames s⁻¹ and 0.5 frames s⁻¹, respectively (n=20 different pellet wastage events). The longest response time (26 s) occurred when fish were amassed against the camera and/or covering pellets. Using appropriate camera lens, camera positioning and/or ‘warning’ software can address fish-interference problems.     

Magnetic Resonance Imaging for Rapid Screening for the Nephrotoxic and Hepatotoxic Effects of Microcystins

In vivo visualization of kidney and liver damage by Magnetic Resonance Imaging (MRI) may offer an advantage when there is a need for a simple, non-invasive and rapid method for screening of the effects of potential nephrotoxic and hepatotoxic substances in chronic experiments. Here, we used MRI for monitoring chronic intoxication with microcystins (MCs) in rat. Male adult Wistar rats were treated every other day for eight months, either with MC-LR (10 μg/kg i.p.) or MC-YR (10 μg/kg i.p.). Control groups were treated with vehicle solutions. T₁-weighted MR-images were acquired before and at the end of the eight months experimental period. Kidney injury induced by the MCs presented with the increased intensity of T₁-weighted MR-signal of the kidneys and liver as compared to these organs from the control animals treated for eight months, either with the vehicle solution or with saline. The intensification of the T1-weighted MR-signal correlated with the increased volume density of heavily injured tubuli (R² = 0.77), with heavily damaged glomeruli (R² = 0.84) and with volume density of connective tissue (R² = 0.72). The changes in the MR signal intensity probably reflect the presence of an abundant proteinaceous material within the dilated nephrons and proliferation of the connective tissue. T₁-weighted MRI-is a valuable method for the in vivo screening of kidney and liver damage in rat models of intoxication with hepatotoxic and nephrotoxic agents, such as microcystins.     

Validating a Time Series of Annual Grass Percent Cover in the Sagebrush Ecosystem

We mapped yearly (2000–2016) estimates of annual grass percent cover for much of the sagebrush ecosystem of the western United States using remotely sensed, climate, and geophysical data in regression-tree models. Annual grasses senesce and cure by early summer and then become beds of fine fuel that easily ignite and spread fire through rangeland systems. Our annual maps estimate the extent of these fuels and can serve as a tool to assist land managers and scientists in understanding the ecosystem’s response to weather variations, disturbances, and management. Validating the time series of annual maps is important for determining the usefulness of the data. To validate these maps, we compare Bureau of Land Management Assessment Inventory and Monitoring (AIM) data to mapped estimates and use a leave-one-out spatial assessment technique that is effective for validating maps that cover broad geographical extents. We hypothesize that the time series of annual maps exhibits high spatiotemporal variability because precipitation is highly variable in arid and semiarid environments where sagebrush is native, and invasive annual grasses respond to precipitation. The remotely sensed data that help drive our regression-tree model effectively measures annual grasses’ response to precipitation. The mean absolute error (MAE) rate varied depending on the validation data and technique used for comparison. The AIM plot data and our maps had substantial spatial incongruence, but despite this, the MAE rate for the assessment equaled 12.62%. The leave-one-out accuracy assessment had an MAE of 8.43%. We quantified bias, and bias was more substantial at higher percent cover. These annual maps can help management identify actions that may alleviate the current cycle of invasive grasses because it enables the assessment of the variability of annual grass ? percent cover distribution through space and time, as part of dynamic systems rather than static systems.     

Real-time spectral classification of compression wood inPicea abies

Compression wood is formed by the living tree to compensate for external loads. It creates wood fibers with properties undesirable in sawn products. Automatic detection of compression wood can lead to production advantages. A wood surface was scanned with a spectrometer, and compression wood was detected by analyzing the spectral composition of light reflected from the wood surface within the visible spectrum. Linear prediction models for compression wood in Norway spruce (Picea abies) were produced using multivariate analysis and regression methods. The resulting prediction coefficients were implemented in a scanning system using the MAPP2200 smart image sensor combined with an imaging spectrograph. This scanning system is capable of making a pixelwise classification of a wood surface in real time. Classification of one spruce plank was compared with analysis by scanning electron microscopy, showing that the automatic classification was correct in 11 of 14 cases.