Quantitative Assessment of Lesion Characteristics and Disease Severity Using Digital Image Processing

This work describes a dedicated software which detects and characterizes disease lesions on leaves to provide data on the number and type of lesions and the percentage of leaf area diseased (severity). The software, written in C²⁺, can be used with a standard computer in combination with a colour CCD camera and a frame grabber for image acquisition. The usefulness and adaptability of the software was evaluated using two foliar diseases, Alternaria blight of sunflower and oat leaf rust (Puccinia coronata f.sp. avenae), which differ in symptoms. Using image segmentation and classification techniques, the software discriminated disease symptoms from the healthy leaf area. The number and size of lesions and severity, obtained using the image processing software, were compared with those calculated using a software planimeter or visual assessment. Significant linear relationships between planimeter and the imaging software were obtained for lesion number and severity in oat leaf rust and for severity in sunflower blight. Artefacts, mistakenly classified as blight lesions by the imaging software resulted in an over-estimation of the number of lesions. Future research is aimed at improving accuracy through better illumination during image capture. A dedicated, compact and portable hardware is currently being developed for field use as a self-contained device for disease assessment.     

Digital image analysis of Zostera marina leaf injury

Current methods for assessing leaf injury in Zostera marina (eelgrass) utilize subjective indexes for desiccation injury and wasting disease. Because of the subjective nature of these measures, they are inherently imprecise making them difficult to use in quantifying complex leaf injuries from multiple sources. We have developed a method using color digital photography of eelgrass leaves which are then manipulated using image processing programs and analyzed using geographic digital image analysis. The resulting false color images are then assigned by the user into uninjured and injured groupings which may then be reported as a percentage of leaf area affected. If images are rectified, leaf area (cm²) of injured and uninjured leaf segments may be determined. Although this method is time consuming and still requires some subjective judgments, it does allow for precise analysis of highly complex leaf injuries and has the potential to be a substantial improvement over existing leaf injury indexes.     

大批量测定植物(互花米草)叶面积的一种新方法

叶面积的准确测定是评价生态系统能流、碳流和水分利用的重要基础.目前常用的叶面积测定方法都有其局限性,有些方法快捷、准确,但仅适用于小批量测定;大多数方法很难在较短时间内准确进行大批量测定.本文以互花米草叶面积测定为例,探讨了以扫描仪和叶片干重为基础,结合PHOTOSHOP图像处理技术,大批量、快捷、准确测定植物叶面积的方法--扫描-干重法.结果表明:虚拟正方形叶片边长与叶子像素成幂函数曲线关系,公式为419.85x^1.9693,R≈1.00;用此公式验证扫描-干重法,相对误差仅为3.651%.用扫描-干重法测得的结果与方格法相比无显著差异(P=0.473),表明该方法是可行的.     

新生儿肺炎的临床表现及x线特征的探讨

目的：探讨新生儿肺炎（neonatal pneumonia,NP）的临床表现及x线特征,以提高对NP的临床认识。方法：选择2011年6月至2012年10月在我院确诊为NP的新生儿73例,均经x线检查,回顾性分析患儿的临床资料,总结并分析患儿的临床表现及x线特征。结果：NP患儿的临床症状主要表现为呼吸急促、发绀、咳嗽、吐沫、呛奶和三凹征,部分患儿体温正常;NP的x线影像表现具有多样性,以不同程度的支气管肺炎多见,主要表现为肺纹理增多增密,局部肺野透光度降低或透光度增强。结论：NP患儿的临床症状以呼吸急促、发绀、咳嗽、吐沫、呛奶和三凹征为主,x线检查可为NP的早期诊断提供重要的临床信息,临床医师应根据NP患儿的x线影像的多样性,密切结合患儿的临床症状,做出综合分析。     

Design of an intelligent bean cultivation approach using computer vision,IoT and spatio-temporal deep learning structures

Crop health monitoring and weed removal are two crucial elements dictating efficient, productive and resilient cultivation. Due to frequent attacks by pest and pathogens, the crops become diseased resulting in degradation of the quality and quantity of the production. The process of continuous monitoring of crop health is challenging and requires the involvement of information and communication technologies (ICT). The outcome is precision agriculture where the Internet of Things (IoT) and Artificial Intelligence (AI) techniques are vital ingredients. The design of an integrated approach of precision agriculture based on IoT and AI is discussed here which is tailored for real time crop health monitoring and performs various other operations like weed detection, ambient air sensing, watering the vegetation automatically at regular intervals of time, spraying of pesticides etc. The proposed system is a combination of an IoT formed using sensors and devices, image processing and machine learning (ML)/ deep learning (DL) techniques confined to the cultivation of fifteen varieties of beans found in India. The work involves two intelligent learning models configured to capture spatio-temporal attributes of image samples and sensor inputs and for real time discrimination between healthy and diseased bean leaves, detection of weeds growing around the cultivation land and also for process control. The first approach employs a DL structure named EfficientNetB7 along with a Bidirectional Long Short Term Memory (BiLSTM) while the second method adopts a VGG16 with an integrated attention mechanism. Also experiments have been carried out using benchmark ML classifiers like Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP) and Time Delay Neural Network (TDNN) combined with feature extraction techniques. Segmentation methods have been used to separate out the diseased sections of the leaves which are then used as apriori labels for the classifiers to reinforce the previously known details of the bean varieties. Subsequently, the trained networks are tested with bean leaf samples collected from cultivation farms. Results show that our proposed DL models could accurately predict the health state of the bean leaves with less computation time. With an automated approach of bean leaf health discrimination, weed detection and process control, the cost effectiveness of the overall effort is enhanced. Further, the sensor pack also provides precise thresholds at which water sprinkling could be initiated resulting in water conservation.     

金丝猴(RHINOPITHECUS)肌肉系统某些特征的比较研究

本文对金丝猴与其他灵长类之间和三种金丝猴之间特征不同的56条肌作了比较研究。结果表明:三种金丝猴的肌肉系统存在一些差异。纠正了Patterson(1942)对一例川金丝猴的一些错误观察记录。在金丝猴属的肌学方面除与猴超科共有的大部分特征外,还具有与类人猿和人相似的特征。在猴类中,金丝猴的肌肉系统更多地与叶猴相似,其中一些特征较叶猴更为进化。从进化观点看,金丝猴的肌学特征似介于叶猴与类人猿之间,故它们比其他猴超科动物更为进化。     

A Novel Method of Automatic Plant Species Identification Using Sparse Representation of Leaf Tooth Features

Automatic species identification has many advantages over traditional species identification. Currently, most plant automatic identification methods focus on the features of leaf shape, venation and texture, which are promising for the identification of some plant species. However, leaf tooth, a feature commonly used in traditional species identification, is ignored. In this paper, a novel automatic species identification method using sparse representation of leaf tooth features is proposed. In this method, image corners are detected first, and the abnormal image corner is removed by the PauTa criteria. Next, the top and bottom leaf tooth edges are discriminated to effectively correspond to the extracted image corners; then, four leaf tooth features (Leaf-num, Leaf-rate, Leaf-sharpness and Leaf-obliqueness) are extracted and concatenated into a feature vector. Finally, a sparse representation-based classifier is used to identify a plant species sample. Tests on a real-world leaf image dataset show that our proposed method is feasible for species identification.     

CT image segmentation using FEM with optimized boundary condition

The authors propose a CT image segmentation method using structural analysis that is useful for objects with structural dynamic characteristics. Motivation of our research is from the area of genetic activity. In order to reveal the roles of genes, it is necessary to create mutant mice and measure differences among them by scanning their skeletons with an X-ray CT scanner. The CT image needs to be manually segmented into pieces of the bones. It is a very time consuming to manually segment many mutant mouse models in order to reveal the roles of genes. It is desirable to make this segmentation procedure automatic. Although numerous papers in the past have proposed segmentation techniques, no general segmentation method for skeletons of living creatures has been established. Against this background, the authors propose a segmentation method based on the concept of destruction analogy. To realize this concept, structural analysis is performed using the finite element method (FEM), as structurally weak areas can be expected to break under conditions of stress. The contribution of the method is its novelty, as no studies have so far used structural analysis for image segmentation. The method's implementation involves three steps. First, finite elements are created directly from the pixels of a CT image, and then candidates are also selected in areas where segmentation is thought to be appropriate. The second step involves destruction analogy to find a single candidate with high strain chosen as the segmentation target. The boundary conditions for FEM are also set automatically. Then, destruction analogy is implemented by replacing pixels with high strain as background ones, and this process is iterated until object is decomposed into two parts. Here, CT image segmentation is demonstrated using various types of CT imagery.     

Interpreting Plant Responses to Clinostating: I. MECHANICAL STRESSES AND ETHYLENE

The severe epinasty and other symptoms developed by clinostated leafy plants could be responses to gravity compensation and/or the mechanical stresses of leaf flopping. Epinasty in cocklebur (Xanthium strumarium L.), tomato (Lycopersicon esculentum Mill.), and castor bean (Ricinus communis L.) is delayed by inhibitors of ethylene synthesis and action (aminoethoxyvinylglycine and Ag⁺), confirming the role of ethylene in clinostat epinasty. To test the possibility that clinostat mechanical stresses (leaf flopping) cause ethylene production and, thus, epinasty, vertical plants were stressed with constant, gentle, horizontal, or vertical shaking or with a quick, back-and-forth rotation (twisting). Clinostat leaf flopping was closely approximated but with a minimum of gravity compensation, by turning plants so their stems were horizontal, rotating them quickly about the stem axis, and then returning them to the vertical, repeating the treatment every four minutes (clinostat rotation time). None of these mechanical stresses produced significant epinasties, but vigorous hand-shaking (120 seconds per day) generated minor epinasties, as did Ag⁺ applied daily (concentrations high enough to cause leaf browning). Plants gently inverted every 20 minutes developed epinasty at about the same rate and to about the same extent as clinostated plants, but plants inverted every 20 minutes and immediately returned to the upright position did not become epinastic. It is concluded that clinostat epinasty is probably caused by disturbances in the gravity perception mechanism, rather than by leaf flopping.     

First Report of Botryosphaeria dothidea on Mangifera indica L. in Gujarat

A leaf spot disease was observed on Mangifera indica L. located in the region of Rajkot, Gujarat, India. The symptoms included the appearance of small, circular to oval dark brown necrotic sunken spots on the leaves. The fungus formed grey to dark grey on potato dextrose agar and conidia were single‐celled, ovoid or oblong. On the basis of mycological characteristics, pathogenicity test and molecular identification using 28S rDNA sequence and internal transcribed spacer (ITS), the fungus was identified as Botryosphaeria dothidea. To the best of our knowledge, this is the first report of leaf spot on M. indica tree caused by Botryoshaeria dothidea in Gujarat, India.     

Tracking facilitates 3-D motion estimation

The recently emerging paradigm of Active Vision advocates studying visual problems in form of modules that are directly related to a visual task for observers that are active. Along these lines, we are arguing that in many cases when an object is moving in an unrestricted manner (translation and rotation) in the 3D world, we are just interested in the motion's translational components. For a monocular observer, using only the normal flow — the spatio-temporal derivatives of the image intensity function — we solve the problem of computing the direction of translation and the time to collision. We do not use optical flow since its computation is an ill-posed problem, and in the general case it is not the same as the motion field — the projection of 3D motion on the image plane. The basic idea of our motion parameter estimation strategy lies in the employment of fixation and tracking. Fixation simplifies much of the computation by placing the object at the center of the visual field, and the main advantage of tracking is the accumulation of information over time. We show how tracking is accomplished using normal flow measurements and use it for two different tasks in the solution process. First it serves as a tool to compensate for the lack of existence of an optical flow field and thus to estimate the translation parallel to the image plane; and second it gathers information about the motion component perpendicular to the image plane.     

Image analysis as a tool for quantitative phycology: a computational approach to cyanobacterial taxa identification

In the following work we discuss the application of image processing and pattern recognition to the field of quantitative phycology. We overview the area of image processing and review previously published literature pertaining to the image analysis of phycological images and, in particular, cyanobacterial image processing. We then discuss the main operations used to process images and quantify data contained within them. To demonstrate the utility of image processing to cyanobacteria classification, we present details of an image analysis system for automatically detecting and classifying several cyanobacterial taxa of Lake Biwa, Japan. Specifically, we initially target the genus Microcystis for detection and classification from among several species of Anabaena. We subsequently extend the system to classify a total of six cyanobacteria species. High-resolution microscope images containing a mix of the above species and other nontargeted objects are analyzed, and any detected objects are removed from the image for further analysis. Following image enhancement, we measure object properties and compare them to a previously compiled database of species characteristics. Classification of an object as belonging to a particular class membership (e.g., “Microcystis,”“A. smithii,”“Other,” etc.) is performed using parametric statistical methods. Leave-one-out classification results suggest a system error rate of approximately 3%. Received: September 6, 1999 / Accepted: February 6, 2000     

grasviq: an image analysis framework for automatically quantifying vein number and morphology in grass leaves

Beyond facilitating transport and providing mechanical support to the leaf, veins have important roles in the performance and productivity of plants and the ecosystem. In recent decades, computational image analysis has accelerated the extraction and quantification of vein traits, benefiting fields of research from agriculture to climatology. However, most of the existing leaf vein image analysis programs have been developed for the reticulate venation found in dicots. Despite the agroeconomic importance of cereal grass crops, like Oryza sativa (rice) and Zea mays (maize), a dedicated image analysis program for the parallel venation found in monocots has yet to be developed. To address the need for an image-based vein phenotyping tool for model and agronomic grass species, we developed the grass vein image quantification (grasviq ) framework. Designed specifically for parallel venation, this framework automatically segments and quantifies vein patterns from images of cleared leaf pieces using classical computer vision techniques. Using image data sets from maize inbred lines and auxin biosynthesis and transport mutants in maize, we demonstrate the utility of grasviq for quantifying important vein traits, including vein density, vein width and interveinal distance. Furthermore, we show that the framework can resolve quantitative differences and identify vein patterning defects, which is advantageous for genetic experiments and mutant screens. We report that grasviq can perform high-throughput vein quantification, with precision on a par with that of manual quantification. Therefore, we envision that grasviq will be adopted for vein phenomics in maize and other grass species.     

高寒草甸高原早熟禾个体性状对放牧与围封的响应

植物对放牧的响应是植物在响应过程中为生存和繁殖所形成的适应策略,研究植物表型反应对揭示草原生态系统的放牧响应机制具有重要意义。以青藏高原高寒草甸主要植物高原早熟禾(Poa alpigena)为研究对象,通过放牧和围封试验,研究其个体性状对放牧与围封的响应,旨在为草原生态系统放牧机制提供理论依据。结果表明:1)在长期放牧干扰下,除叶片数和根重外,其他性状株高、分枝数、叶长、叶宽、叶面积、茎粗、茎长、根长、根粗、穗长、总叶质量、单叶质量、茎重、穗重、全株重均出现显著变小的特征(P0.05);而短期围封对于高原早熟禾叶长、叶宽、总叶质量、单叶质量、茎重、穗重、根重等功能性状的恢复效果并不显著(P0.05),表明放牧退化草原植物性状具有保守性;2)通过构建高原早熟禾性状可塑性变化谱,发现穗重、茎重、全株重、分蘖数、茎长、株高等可塑性幅度较大,为放牧响应的敏感指标,叶片数、叶宽、茎粗、根粗的可塑性变化幅度较小,为惰性性状。     

Microvessel diameter estimation: error bias correction of serial measurements

The assessment of vessel patency can be substantially improved by serial microvessel diameter measurements taken successively along an extensive length of the vessel. It is possible to avoid making the a priori assumptions about the existence or location of local constriction sites implicit in single diameter measurements. The problem then becomes one of making sense of tens or hundreds of measurements for each vessel. Equivalent diameter is defined here as as the diameter of a uniform circular cylinder of the same length as the original vessel, and having the same total resistance. Direct computation of the equivalent diameter, without taking measurement errors into account, leads to an underestimation of the true equivalent diameter even if the individual diameter measurements were not biased. We have developed a method for effectively eliminating this bias. It has been applied to serial microvessel diameter measurements of the guinea pig cochlea, automatically measured using an image analysis system. In this report, the results were developed for diameter estimates with an approximate gaussian distribution; however the method is readily extended to other error distributions. Convergence of the bias compensation was rapid. Use of the new method is advisable with as few as three diameter estimates per vessel.     

PseqIP: a nonredundant and exhaustive protein sequence data bank generated from 4 major existing collections

Four major protein sequence data collections (NBRF-PIR, PSD-Kyoto, PGtrans, and NEWAT) have been merged into a single nonredundant data bank called PseqIP. The data bank entries were automatically matched by a heuristic computer program relying on the fast computation of the number of tetrapeptides shared by two sequences. PseqIP 1.0 includes 6,068 different protein sequences for a total of 1,357,067 residues, representing most of the available sequence information to date. During the course of this work, we found about 600 occurrences of a protein sequence recorded with a one-amino-acid variation in at least two different data banks. A flat file (ASCII computer-readable format) version of PseqIP 1.0, well-suited for exhaustive homology searches and statistical sequence analysis, is available from our laboratory.     

Quantitative analysis of venation patterns of Arabidopsis leaves by supervised image analysis

The study of transgenic Arabidopsis lines with altered vascular patterns has revealed key players in the venation process, but details of the vascularization process are still unclear, partly because most lines have only been assessed qualitatively. Therefore, quantitative analyses are required to identify subtle perturbations in the pattern and to test dynamic modeling hypotheses using biological measurements. We developed an online framework, designated Leaf Image Analysis Interface (LIMANI), in which venation patterns are automatically segmented and measured on dark-field images. Image segmentation may be manually corrected through use of an interactive interface, allowing supervision and rectification steps in the automated image analysis pipeline and ensuring high-fidelity analysis. This online approach is advantageous for the user in terms of installation, software updates, computer load and data storage. The framework was used to study vascular differentiation during leaf development and to analyze the venation pattern in transgenic lines with contrasting cellular and leaf size traits. The results show the evolution of vascular traits during leaf development, suggest a self-organizing mechanism for leaf venation patterning, and reveal a tight balance between the number of end-points and branching points within the leaf vascular network that does not depend on the leaf developmental stage and cellular content, but on the leaf position on the rosette. These findings indicate that development of LIMANI improves understanding of the interaction between vascular patterning and leaf growth.     

Carbon/Nitrogen Imbalance Associated with Drought-Induced Leaf Senescence in Sorghum bicolor

Drought stress triggers mature leaf senescence, which supports plant survival and remobilization of nutrients; yet leaf senescence also critically decreases post-drought crop yield. Drought generally results in carbon/nitrogen imbalance, which is reflected in the increased carbon:nitrogen (C:N) ratio in mature leaves, and which has been shown to be involved in inducing leaf senescence under normal growth conditions. Yet the involvement of the carbon/nitrogen balance in regulation of drought-induced leaf senescence is unclear. To investigate the role of carbon/nitrogen balance in drought-induced senescence, sorghum seedlings were subjected to a gradual soil drought treatment. Leaf senescence symptoms and the C:N ratio, which was indicated by the ratio of non-structural carbohydrate to total N content, were monitored during drought progression. In this study, leaf senescence developed about 12 days after the start of drought treatment, as indicated by various senescence symptoms including decreasing photosynthesis, photosystem II photochemistry efficiency (Fv/Fm) and chlorophyll content, and by the differential expression of senescence marker genes. The C:N ratio was significantly enhanced 10 to 12 days into drought treatment. Leaf senescence occurred in the older (lower) leaves, which had higher C:N ratios, but not in the younger (upper) leaves, which had lower C:N ratios. In addition, a detached leaf assay was conducted to investigate the effect of carbon/nitrogen availability on drought-induced senescence. Exogenous application of excess sugar combined with limited nitrogen promoted drought-induced leaf senescence. Thus our results suggest that the carbon/nitrogen balance may be involved in the regulation of drought-induced leaf senescence.