A new approach to aflatoxin detection in chili pepper by machine vision

Aflatoxins are the toxic metabolites of Aspergillus molds, especially by Aspergillus flavus and Aspergillus parasiticus. They have been studied extensively because of being associated with various chronic and acute diseases especially immunosuppression and cancer. Aflatoxin occurrence is influenced by certain environmental conditions such as drought seasons and agronomic practices. Chili pepper may also be contaminated by aflatoxins during harvesting, production and storage. Aflatoxin detection based on chemical methods is fairly accurate. However, they are time consuming, expensive and destructive. We use hyperspectral imaging as an alternative for detection of such contaminants in a rapid and nondestructive manner. In order to classify aflatoxin contaminated chili peppers from uncontaminated ones, a compact machine vision system based on hyperspectral imaging and machine learning is proposed. In this study, both UV and Halogen excitations are used. Energy values of individual spectral bands and also difference images of consecutive spectral bands were utilized as feature vectors. Another set of features were extracted from those features by applying quantization on the histogram of the images. Significant features were selected based on proposed method of hierarchical bottleneck backward elimination (HBBE), Guyon’s SVM-RFE, classical Fisher discrimination power and Principal Component Analysis (PCA). Multi layer perceptrons (MLPs) and linear discriminant analysis (LDA) were used as the classifiers. It was observed that with the proposed features and selection methods, robust and higher classification performance was achieved with fewer numbers of spectral bands enabling the design of simpler machine vision systems.     

Detection of common defects on oranges using hyperspectral reflectance imaging

To detect various common defects on oranges, a hyperspectral imaging system has been built for acquiring reflectance images from orange samples in the spectral region between 400 and 1000 nm. Oranges with insect damage, wind scarring, thrips scarring, scale infestation, canker spot, copper burn, phytotoxicity, heterochromatic stripe, and normal surface were studied. Hyperspectral images of samples were evaluated using principal component analysis (PCA) with the goal of selecting several wavelengths that could potentially be used in an in-line multispectral imaging system. The third principal component images using six wavelengths (630, 691, 769, 786, 810 and 875 nm) in the visible spectral (VIS) and near-infrared (NIR) regions, or the second principal component images using two wavelengths (691 and 769 nm) in VIS region gave better identification results under investigation. However, the stem-ends were easily confused with defective areas. In order to solve this problem, representative regions of interest (ROIs) reflectance spectra of samples with different types of skin conditions were visually analyzed. The researches revealed that a two-band ratio (R875/R691) image could be used to differentiate stem-ends from defects effectively. Finally, the detection algorithm of defects was developed based on PCA and band ratio coupled with a simple thresholding method. For the investigated independent test samples, accuracies of 91.5% and 93.7% with no false positives were achieved for both sets of selected wavelengths using proposed method, respectively. The disadvantage of this algorithm is that it could not discriminate between different types of defects.     

气候变暖对宜阳县朝天椒种植的影响研究

通过对朝天椒大田生产的气候、生态环境特征的分析,选取气温、降水、日照和相对湿度这4个对朝天椒生产影响最大的气象因子,分析各因子的变化特点。结果表明:气候变暖和日照增加有利于朝天椒的种植发展,但降雨量减少会影响朝天椒的生产。据此提出相关对策,如温棚育苗、提早播种、加强田间管理、做好人工增雨和蓄水准备,以应对气候变暖的影响。     

Registration of multispectral 3D points for plant inspection

Machine vision technologies have shown advantages for efficient and accurate plant inspection in precision agriculture. Regarding the balance between accuracy of inspection and compactness for infield applications, multispectral imaging systems would be more suitable than RGB colour cameras or hyperspectral imaging systems. Multispectral image registration (MIR) is a key issue for multispectral imaging systems, however, this task is challenging. First of all, in many cases, two images needing registration do not have a one-to-one linear mapping in 2D space and therefore they cannot be aligned in 2D images. Furthermore, the general MIR algorithms are limited to images with uniform intensity and are incapable of registering images with rich features. This study developed a machine vision system (MVS) and a MIR method which replaces 2D-2D image registration by 3D-3D point cloud registration. The system can register 3D point clouds of ultraviolet (UV), blue, green, red and near-infrared (NIR) spectra in 3D space. It was found that the point clouds of general plants created by images of different spectral bands have a complementary property, and therefore a combined point cloud, called multispectral 3D point cloud, is denser than any cloud created by a single spectral band. Intensity information of each spectral band is available in a multispectral 3D point cloud and therefore image fusion and 3D morphological analysis can be conducted in the cloud. The MVS could be used as a sensor of a robotic system to fulfil on-the-go infield plant inspection tasks.     

Detection of Fusarium damaged kernels in Canada Western Red Spring wheat using visible/near-infrared hyperspectral imaging and principal component analysis

Fusarium damage in wheat reduces the quality and safety of food and feed products. In this study, the use of hyperspectral imaging was investigated to detect fusarium damaged kernels (FDK) in Canadian wheat samples. Eight hundred kernels of Canada Western Red Spring wheat were segregated into three classes of kernels: sound, mildly damaged and severely damaged. Singulated kernels were scanned with a hyperspectral imaging system in the visible-NIR (400-1000 nm) wavelength range. Principal component analysis (PCA) was performed on the images and the distribution of PCA scores within individual kernels measured to develop linear discriminant analysis (LDA) models for predicting the extent of fusarium damage. An LDA model classified the wheat kernels into sound and FDK categories with an overall accuracy of 92% or better. Classification based on six selected wavelengths was comparable to that based on the full-spectrum data.     

榛子粉溶解性和膨胀势与其主要物质组成的关系

【目的】比较4个品种榛子粉溶解性和膨胀势及其去皮处理后榛子粉溶解性和膨胀势变化的差异,分析榛子粉主要物质组成与其溶解性和膨胀势的关系,为榛子粉加工产品的研制提供理论依据。【方法】采用碱液浸泡法去除榛子种皮,索氏提取法脱去油脂制取榛子粉;采用离心法测定榛子粉溶解性和膨胀势,凯氏定氮法测定蛋白质含量,考马斯亮蓝比色法测定可溶性蛋白质含量,蒽酮比色法测定淀粉和可溶性糖含量,重量法测定粗纤维和灰分含量,并分析物质组成与溶解性和膨胀势的相关性。【结果】不同品种榛子粉溶解性和膨胀势均存在显著差异,溶解性从大到小依次为‘巴塞罗那’、‘辽榛3#’、‘平榛1#’和‘达维’,膨胀势从大到小依次为‘巴塞罗那’、‘平榛1#’、‘达维’和‘辽榛3#’。溶解性随温度的升高呈先增加后降低的趋势变化,不同温度下溶解度差异显著,70℃ 时达到峰值;膨胀势在低于70℃ 时随温度的升高显著增加,高于70℃时随温度的升高变化不明显。品种间榛子粉主要物质组成含量差异显著,溶解性与可溶性蛋白质、可溶性糖和淀粉含量显著正相关,与灰分含量显著负相关;膨胀势与淀粉含量显著正相关,与蛋白质含量显著负相关。去皮处理对溶解性和膨胀势的影响不显著。【结论】各品种榛子粉的溶解性和膨胀势随温度变化的趋势基本一致,温度对溶解性影响差异显著,高于70℃ 时膨胀势没有明显变化;可溶性蛋白质、可溶性糖和淀粉是影响榛子粉溶解性的主要因子,而膨胀势与淀粉关系密切;去皮处理后溶解性和膨胀势变化不明显,并且溶解性和膨胀势之间相互影响。     

Hyperspectral waveband selection for internal defect detection of pickling cucumbers and whole pickles

Hyperspectral imaging under transmittance mode has shown potential for detecting internal defect, however, the technique still cannot meet the online speed requirement because of the need to acquire and analyze a large amount of image data. This study was carried out to select important wavebands for further development of an online inspection system to detect internal defect in pickling cucumbers and whole pickles. Hyperspectral transmittance/reflectance images were acquired from normal and defective cucumbers and whole pickles using a prototype hyperspectral reflectance (400-740 nm)/transmittance (740-1000 nm) imaging system. Up to four-waveband subsets were determined by a branch and bound algorithm combined with the k-nearest neighbor classifier. Different waveband binning operations were also compared to determine the bandwidth requirement for each waveband combination. The highest classification accuracies of 94.7 and 82.9% were achieved using the optimal four-waveband sets of 745, 805, 965, and 985 nm at 20 nm spectral resolution for cucumbers and of 745, 765, 885, and 965 nm at 40 nm spectral resolution for whole pickles, respectively. The selected waveband sets will be useful for online quality detection of pickling cucumbers and pickles.     

Starch fossils and the domestication and dispersal of chili peppers (Capsicum spp. L.) in the Americas

Chili peppers (Capsicum spp.) are widely cultivated food plants that arose in the Americas and are now incorporated into cuisines worldwide. Here, we report a genus-specific starch morphotype that provides a means to identify chili peppers from archaeological contexts and trace both their domestication and dispersal. These starch microfossils have been found at seven sites dating from 6000 years before present to European contact and ranging from the Bahamas to southern Peru. The starch grain assemblages demonstrate that maize and chilies occurred together as an ancient and widespread Neotropical plant food complex that predates pottery in some regions.     

Line-scan hyperspectral imaging system for real-time inspection of poultry carcasses with fecal material and ingesta

In poultry processing plants, fecal material and ingesta are the primary source of carcass contamination with microbial pathogens. The current practice of the poultry inspection in the United States is primarily human visual observations. Since the visual inspection is becoming more challenging in poultry processing plants adopting high-speed lines, a rapid sorting system could significantly improve the detection and monitoring of carcasses with surface fecal material and ingesta. As a result, we developed a prototype line-scan hyperspectral imaging system configured as a real-time multispectral imaging subsystem for online detection of surface fecal material and ingesta. Specifically, we integrated a commercially available off-the-shelf hyperspectral image camera into the system with two line lights and a custom software program for real-time multispectral imaging. The bottleneck of the imaging system was the data acquisition. For that reason, a multithreaded software architecture was designed and implemented not only to meet the application requirements such as speed and detection accuracy, but also to be customizable to different imaging applications such as systemic disease detection in the future. The image acquisition and processing speed tests confirmed the system could operate to scan poultry carcasses in commercial poultry processing plants. The fecal detection algorithm was based on the previous research using different hyperspectral imaging systems. A new carcass detection and image formation algorithm was developed to allow existing image processing and detection algorithms reusable without any modifications. Sixteen chicken carcasses and four different types of fecal and ingesta samples were used in a study to test the imaging system at two different speeds (140 birds per minute and 180 birds per minute) in a pilot-scale poultry processing facility. The study found that the system could grab and process three waveband images of carcasses moving up to 180 birds per minute (a line-scan rate 286 Hz) and detect fecal material and ingesta on their surfaces. The detection accuracy of the system varied between 89% and 98% with minimum false positive errors (less than 1%), depending on tested detection algorithms. Therefore, these findings provide the basis of not only a commercially viable imaging platform for fecal detection but also a single poultry inspection system for multiple tasks such as systemic disease detection and quality sorting.     

平欧杂种榛果实生长动态与品质分析

【目的】研究平欧杂种榛果实发育动态,为大果榛子在新疆的栽培提供理论依据。【方法】以平欧杂种榛达维、玉坠、平欧110号、平欧15号、辽榛3号5个品种为材料,对其果实发育过程中的三径、单果质量(鲜果重和单果重)、果仁三径、果仁质量、果壳厚度、果腔系数进行测定,评价不同品种果实的发育特点及品质差异。【结果】榛子单果三径、干果质量和果仁三径表现为逐渐增加趋势,8月19日果实成熟,果实鲜重6月10日前增长缓慢,6月10日~7月10日快速增长,之后变慢。达维和平欧110号的单果大,与其他品种有明显差异。平欧110号的果腔系数为0.70,果壳厚为1.71,达维和玉坠果腔系数分别为0.78和0.81,果壳厚分为1.62和1.48,果仁饱满,壳薄。【结论】果实的三径和干果质量呈单“S”形生长曲线,鲜果质量则呈“慢-快-慢”的生长趋势。5个品种以 平欧110号单果最大,达维其次,达维果仁较饱满,果壳较薄,品质最好。     

Multispectral imaging system with interchangeable filter design

The design and calibration of a three-band image acquisition system was reported in this paper. The prototype system developed in this research was a three-band spectral imaging system that acquired two visible-band images and one NIR image simultaneously. This was accomplished by using a three-port imaging system that consisted of three identical monochrome cameras, an optical system, and three interchangeable optical filters. Spectral reflectance from an object was collimated by a front lens, and split in three ways by a cold mirror and beamsplitter: a cold mirror reflects 90% visible light and transmits 80% NIR light. The visible light was again split identically into two directions by an additional beamsplitter. Focusing lenses then projected each image onto its corresponding sensor. By incorporating an interchangeable filter design, the imaging system can measure any two visible spectral bands that range between 400 nm and 700 nm, and one NIR band that ranges between 700 nm and 1000 nm without any complicated manufacturing process. In order to co-register the three images, a system-specific calibration algorithm was developed that compensates for lens-sensor geometric misalignments.The prototype imaging system and the system calibration algorithm were tested and evaluated for image alignment accuracy. The imaging system acquired three-band images of 3D objects with 0.39 pixel misalignment error on average.     

Evaluation of plant seedling water stress using dynamic fluorescence index with blue LED-based fluorescence imaging

A dynamic fluorescence image index system capable of non-destructive assessment of water stress in cabbage seedlings was developed. The quenching curves of chlorophyll fluorescence characteristic to the plant's water stress status under reduced excitation energy were acquired via a multispectral imaging system. The dynamic fluorescence index (DFI), a fluorescence index derived from quantitative modeling of the quenching curves in this study, was found superior to conventional indices such as the fluorescence decrease ratio (Rfd) in evaluating water stress status in cabbage seedlings, with respect to due mainly to its significantly reduced measuring time and enhanced precision of the chlorophyll fluorescence responses. The best results of the quantitative model to predict water stress status of seedlings using the dynamic fluorescence index were r = 0.944 and SEE = 0.286 MPa using the 720 nm channel. The blue (460 nm) LED-excited fluorescence multispectral imaging system (FMSIS) coupled with DFI provides a powerful research tool for plant physiology studies as well as for improving greenhouse and agriculture management practices where quantitative assessment of water potential in leafy plants is critical.     

Development of software for spectral imaging data acquisition using LabVIEW

Developing data acquisition software is a major challenge in integrating a spectral imaging system. This paper presents the design and implementation of a data acquisition program using LabVIEW for a liquid crystal tunable filter based spectral imaging system (900–1700 nm). The module-based program was designed in a three-tier structure. The image acquisition process, modelled by a finite state machine, was implemented in LabVIEW to control the spectral imaging system to collect hyperspectral or multispectral images. The collected spectral images were encoded in general format and could be further processed by other common spectral image analysis tools. In addition, the program could be used to observe band ratio images of the test object in real-time, collect spectral images after ensemble averaging, and select region of interest for spectral image acquisitions. This program is a useful data acquisition tool for the filter-based spectral imaging system. The design and implementation techniques described in this article could also be used to develop similar spectral image acquisition programs.     

Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging

Healthy wheat kernels and wheat kernels damaged by the feeding of the insects: rice weevil (Sitophilus oryzae), lesser grain borer (Rhyzopertha dominica), rusty grain beetle (Cryptolestes ferrugineus), and red flour beetle (Tribolium castaneum) were scanned using a near-infrared (NIR) hyperspecrtal imaging system (700-1100 nm wavelength range) and a colour imaging system. Dimensionality of hyperspectral data was reduced and statistical and histogram features were extracted from NIR images of significant wavelengths and given as input to three statistical discriminant classifiers (linear, quadratic, and Mahalanobis) and a back propagation neural network (BPNN) classifier. A total of 230 features (colour, textural, and morphological) were extracted from the colour images and the most contributing features were selected and used as input to the statistical and BPNN classifiers. The quadratic discriminant analysis (QDA) classifier gave the highest accuracy and correctly identified 96.4% healthy and 91.0-100.0% insect-damaged wheat kernels using the top 10 features from 230 colour image features combined with hyperspectral image features.     

Evaluating high resolution SPOT 5 satellite imagery for crop identification

High resolution satellite imagery offers new opportunities for crop monitoring and assessment. A SPOT 5 image acquired in May 2006 with four spectral bands (green, red, near-infrared, and short-wave infrared) and 10-m pixel size covering intensively cropped areas in south Texas was evaluated for crop identification. Two images with pixel sizes of 20 m and 30 m were also generated from the original image to simulate coarser resolution satellite imagery. Two subset images covering a variety of crops with different growth stages were extracted from the satellite image and five supervised classification techniques, including minimum distance, Mahalanobis distance, maximum likelihood, spectral angle mapper (SAM), and support vector machine (SVM), were applied to the 10-m subset images and the two coarser resolution images to identify crop types. The effects of the short-wave infrared band and pixel size on classification results were also examined. Kappa analysis showed that maximum likelihood and SVM performed better than the other three classifiers, though there were no statistical differences between the two best classifiers. Accuracy assessment showed that the 10-m, four-band images based on maximum likelihood resulted in the best overall accuracy values of 91% and 87% for the two respective sites. The inclusion of the short-wave infrared band statistically significantly increased the overall accuracy from 82% to 91% for site 1 and from 75% to 87% for site 2. The increase in pixel size from 10 m to 20 m or 30 m did not significantly affect the classification accuracy for crop identification. These results indicate that SPOT 5 multispectral imagery in conjunction with maximum likelihood and SVM classification techniques can be used for identifying crop types and estimating crop areas.     

Hardware-based image processing for high-speed inspection of grains

A high-speed, low-cost, image-based sorting device was developed to detect and separate grains having slight color differences or small defects. The device directly combines a complementary metal–oxide–semiconductor (CMOS) color image sensor with a field-programmable gate array (FPGA) which was programmed to execute image processing in real-time, without the need of an external computer. Spatial resolution of the imaging system is approximately 16 pixels/mm. The system includes three image sensor/FPGA combinations placed around the perimeter of a single-file stream of kernels, so that most of the surface of each kernel is be inspected. A vibratory feeder feeds kernels onto an inclined chute that kernels slide down in a single-file manner. Kernels are imaged immediately after dropping off the end of the chute and are diverted by activating an air valve. The system has a throughput rate of approximately 75 kernels/s per channel which is much higher than previously developed image inspection systems. This throughput rate corresponds to an inspection rate of approximately 8 kg/h of wheat and 40 kg/h of popcorn. The system was initially developed to separate white wheat from red wheat, and to remove popcorn having blue-eye damage, which is indicated by a small blue discoloration in the germ of a popcorn kernel. Testing of the system resulted in accuracies of 88% for red wheat and 91% for white wheat. For popcorn, the system achieved 74% accuracy when removing popcorn with blue-eye damage and 91% accuracy at recognizing good popcorn. The sorter should find uses for removing other defects found in grain, such as insect-damaged grain, scab-damaged wheat, and bunted wheat. Parts for the system cost less than $2000, suggesting that it may be economical to run several systems in parallel to keep up with processing plant rates.     

响应面法优化微波辅助提取辣椒红色素的工艺研究

以红辣椒为原料,丙酮为提取剂,利用微波辅助有机溶剂法提取辣椒红色素,进而通过单因素试验和响应面试验分析微波功率、微波温度、微波时间等工艺参数对提取效率的影响,并优化提取工艺。结果表明,应用微波辅助有机溶剂法提取辣椒红色素的工艺路线是正确可行的,所得产品的光谱特性及特征吸收峰均与辣椒红色素标准图谱基本吻合;微波辅助有机溶剂法提取辣椒红色素的最优工艺条件为:以丙酮为提取剂,微波功率为105 W,微波温度为42℃,微波时间为2 min。在此最优工艺条件下,所得辣椒红色素的吸光度值为0.631。     

QuEChERS-气相色谱质谱联用检测辣椒中

构建QuEChERS-气相色谱质谱联用检测辣椒当中33 种农药残留的快速检测方式。超声 提取乙腈，N-正丙基乙二胺（primary secondary amine，PSA）与C18 净化，通过测定HP-5MS 色谱柱的 分离。在浓度测定范围0.4μg～20.0μg/mL 内，具有良好的线性关系，r＞0.995；浓度添加是25、80、 150μg/kg 的时候，平均的回收率在74.31%～119.72%之间，相对的标准偏差是2.0%～15.3%（n=6），检 出限是0.02～4.6μg/kg，定量的下限是0     

朝天椒辣椒素生物合成途径基因对茉莉酸的响应

为了探索茉莉酸(JA)对辣椒素生物合成途径基因表达的调控作用,该研究利用100μmol/L的JA处理朝天椒果实0、4、12、24和48h后,利用荧光定量PCR检测辣椒素合成途径基因mRNA表达变化,并用高效液相色谱测定处理0、1、3、6、10和15 d后果实辣椒素含量的变化.结果表明,JA可以不同程度地促进朝天椒中pal、C4h、Comt、4Cl、Hct、Pamt、Bcat、FatA和pun1基因的mRNA表达,进而促进辣椒素的合成.     

森林火灾早期预警的多光谱检测技术

传统的红外检测方法难以区分森林火灾初期起火点的红外图像,不利于火灾初始控制,为此对利用多光谱图像探测林火的滤波算法进行了研究.首先采用主成分分析法进行森林背景及干扰信息提取,再根据最小二乘准则,得到适当的权值向量,对多谱图像序列进行处理.理论分析表明,该方法可以有效地探测起火点等红外小目标.计算机模拟结果表明,该方法是实时而有效的.