Fractal dimension applied to plant identification

This article discusses methods to identify plants by analysing leaf complexity based on estimating their fractal dimension. Leaves were analyzed according to the complexity of their internal and external shapes. A computational program was developed to process, analyze and extract the features of leaf images, thereby allowing for automatic plant identification. Results are presented from two experiments, the first to identify plant species from the Brazilian Atlantic forest and Brazilian Cerrado scrublands, using fifty leaf samples from ten different species, and the second to identify four different species from genus Passiflora, using twenty leaf samples for each class. A comparison is made of two methods to estimate fractal dimension (box-counting and multiscale Minkowski). The results are discussed to determine the best approach to analyze shape complexity based on the performance of the technique, when estimating fractal dimension and identifying plants.     

Automatic plant identification from photographs

We present a plant identification system for automatically identifying the plant in a given image. In addition to common difficulties faced in object recognition, such as light, pose and orientation variations, there are further difficulties particular to this problem, such as changing leaf shapes according to plant age and changes in the overall shape due to leaf composition. Our system uses a rich variety of shape, texture and color features, some being specific to the plant domain. The system has achieved the best overall score in the ImageCLEF’12 plant identification campaign in both the automatic and human-assisted categories. We report the results of this system on the publicly available ImageCLEF’12 plant dataset, as well as the effectiveness of individual features. The results show 61 and 81 % accuracies in classifying the 126 different plant species in the top-1 and top-5 choices.     

A robust knowledge-based plant searching strategy

This paper presents a knowledge-based plant information retrieval system that is robust to inaccurate and erroneous user queries. First, a knowledge-based genetic algorithm (GA) corrects the erroneous input vectors before these are fed into a back-propagation neural network (BPNN) that performs the actual query. Experimental results show that the strategy achieves a 75% recall rate and 25% precision rate with a cutoff level of 10 under the misjudgment of shapes. Moreover, a fully trained BPNN dynamically adapts to changes in the environment. Due to its robust and simple user interface and portability, the strategy is particularly applicable to educational settings such as outdoor fieldwork in courses on ecology.     

A patch-based approach to 3D plant shoot phenotyping

The emerging discipline of plant phenomics aims to measure key plant characteristics, or traits, though as yet the set of plant traits that should be measured by automated systems is not well defined. Methods capable of recovering generic representations of the 3D structure of plant shoots from images would provide a key technology underpinning quantification of a wide range of current and future physiological and morphological traits. We present a fully automatic approach to image-based 3D plant reconstruction which represents plants as series of small planar sections that together model the complex architecture of leaf surfaces. The initial boundary of each leaf patch is refined using a level set method, optimising the model based on image information, curvature constraints and the position of neighbouring surfaces. The reconstruction process makes few assumptions about the nature of the plant material being reconstructed. As such it is applicable to a wide variety of plant species and topologies, and can be extended to canopy-scale imaging. We demonstrate the effectiveness of our approach on real images of wheat and rice plants, an artificial plant with challenging architecture, as well as a novel virtual dataset that allows us to compute distance measures of reconstruction accuracy. We also illustrate the method’s potential to support the identification of individual leaves, and so the phenotyping of plant shoots, using a spectral clustering approach.     

Validation of plant part measurements using a 3D reconstruction method suitable for high-throughput seedling phenotyping

In plant phenotyping, there is a demand for high-throughput, non-destructive systems that can accurately analyse various plant traits by measuring features such as plant volume, leaf area, and stem length. Existing vision-based systems either focus on speed using 2D imaging, which is consequently inaccurate, or on accuracy using time-consuming 3D methods. In this paper, we present a computer-vision system for seedling phenotyping that combines best of both approaches by utilizing a fast three-dimensional (3D) reconstruction method. We developed image processing methods for the identification and segmentation of plant organs (stem and leaf) from the 3D plant model. Various measurements of plant features such as plant volume, leaf area, and stem length are estimated based on these plant segments. We evaluate the accuracy of our system by comparing the measurements of our methods with ground truth measurements obtained destructively by hand. The results indicate that the proposed system is very promising.     

Identification of grape diseases using image analysis and BP neural networks

Prevention and treatment of diseases are critical to improve grape yield and quality. Automatic identification of grape diseases is important to prevent insect pests timely and effectively. This study proposed an automatic detection method for grape leaf diseases based on image analysis and back–propagation neural network (BPNN). The Wiener filtering method based on wavelet transform was applied to denoise the disease images. The grape leaf disease regions were segmented by Otsu method, and morphological algorithms were used to improve the lesion shape. Prewitt operator was utilized to extract the complete edge of lesion region. Five effective characteristic parameters, namely, perimeter, area, circularity, rectangularity, and shape complexity, were extracted. The proposed recognition model for grape leaf diseases based on BPNN could efficiently inspect and recognize five grape leaf diseases: leaf spot, Sphaceloma ampelinum de Bary, anthracnose, round spot, and downy mildew. Results indicated that the proposed detection system for grape leaf diseases could be used to inspect grape diseases with high classification accuracy.

     

Modified locally linear discriminant embedding for plant leaf recognition

Based on locally linear embedding (LLE) and modified maximizing margin criterion (MMMC), a modified locally linear discriminant embedding (MLLDE) algorithm is proposed for plant leaf recognition in this paper. By MLLDE, the plant leaf images are mapped into a leaf subspace for analysis, which can detect the essential leaf manifold structure. Furthermore, the unwanted variations resulting from changes in period, location, and illumination can be eliminated or reduced. Different from principal component analysis (PCA) and linear discriminant analysis (LDA), which can only deal with flat Euclidean structures of plant leaf space, MLLDE not only inherits the advantages of locally linear embedding (LLE), but makes full use of class information to improve discriminant power by introducing translation and rescaling models. The experimental results on real plant leaf database show that the MLLDE is effective for plant leaf recognition.     

基于HSV彩色空间与直方图信息的植物叶脉FFCM算法提取*

为满足植物分类和识别对植物叶片叶脉信息的需要,提出了基于HSV彩色空间与直方图信息FFCM聚类算法相结合的植物叶片叶脉提取方法。该算法首先将植物叶片图像由RGB转换到HSV彩色空间,然后使用FFCM算法实现叶片的自动分类和叶脉信息的提取。实验结果表明,该方法既能有效处理和区分绿色和枯黄的叶片图像,也能很好的处理和区分受光均匀和受光不均匀的叶片图像,可以应用于植物的分类与识别。     

Multiclass Twin Support Vector Machine for plant species identification

Multimedia Tools and Applications - Automatic plant species identification is one of the recent and fascinating research area as plants are crucial element of ecosystem. Several plant species exist...     

基于形状特征的叶片图像识别算法比较研究

植物是生命的主要形态之一,其种类已达40多万种,对其进行分类识别在生物多样性保护,生态农业,生物安全中有着重要的意义。不同的种类的植物一般有着不同的叶片形状,因此叶片的形状特征在植物分类中扮演着重要的角色。作为计算机视觉的一个重要应用的植物叶片图像识别,近些年来受到了学者们的关注,产生了大量的研究成果。但由于植物种类巨大,叶片图像存在的类内差异大、类间差异小和叶片的自遮挡等问题等诸多问题,使得叶片图像的识别仍然是目前计算机视觉应用研究的一个热点。对近些年来的基于形状特征的叶片图像识别算法进行了综述和比较,对现有的算法进行了分类,对目前各类最先进的识别算法进行了分析和比较。此外,还介绍了常用的叶片图像测试集和性能评估方法,并将各类算法进行了实验结果的比较研究。研究工作既为现有的植物叶片识别算法的实际应用提供了指导,又为今后进一步研究新的高性能的识别算法提出了努力的方向。     

Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species

The main focus of recent studies relating vegetation leaf chemistry with remotely sensed data is the prediction of chlorophyll and nitrogen content using indices based on a combination of bands from the red and infrared wavelengths. The use of high spectral resolution data offers the opportunity to select the optimal wavebands for predicting plant chemical properties. In order to test the optimal band combinations for predicting nitrogen content, normalized ratio indices were calculated for all wavebands between 350 and 2200 nm for five different species. The correlation between these indices and the nitrogen content of the samples was calculated and compared between species. The results show a strong correlation between individual normalized ratio indices and the nitrogen content for different species. The spectral regions that are most effective for predicting nitrogen content are, for each individual species, different from the normalized difference vegetation index (NDVI) spectral region. By combining the areas of maximum correlation it was possible to determine the optimal spectral regions for predicting leaf nitrogen content across species. In a cross‐species situation, normalized ratio indices using the combination of reflectance at 1770 nm and at 693 nm may give the best relation to nitrogen content for individual species.     

Remote estimation of chlorophyll content in higher plant leaves

Indices for the non-destructive estimation of chlorophyll content were formulated using various instruments to measure reflectance and absorption spectra in visible and near-infrared ranges, as well as chlorophyll contents from several non-related species from different climatic regions. The proposed new algorithms are simple ratios between percentage reflectance at spectral regions that are highly sensitive (540 to 630nm and around 700nm) and insensitive (nearinfrared) to variations in chlorophyll content: R NIR / R 700 and R NIR / R 550. The developed algorithms predicting leaf chemistry from the leaf optics were validated for nine plant species in the range of chlorophyll content from 0.27 to 62.9mug cm -2. An error of less than 4.2 mugcm -2 in chlorophyll prediction was achieved. The use of green and red (near 700nm) channels increases the sensitivity of NDVI to chlorophyll content by about five-fold.     

Multi-modality imagery database for plant phenotyping

Among many applications of machine vision, plant image analysis has recently began to gain more attention due to its potential impact on plant visual phenotyping, particularly in understanding plant growth, assessing the quality/performance of crop plants, and improving crop yield. Despite its importance, the lack of publicly available research databases containing plant imagery has substantially hindered the advancement of plant image analysis. To alleviate this issue, this paper presents a new multi-modality plant imagery database named “MSU-PID,” with two distinct properties. First, MSU-PID is captured using four types of imaging sensors, fluorescence, infrared, RGB color, and depth. Second, the imaging setup and the variety of manual labels allow MSU-PID to be suitable for a diverse set of plant image analysis applications, such as leaf segmentation, leaf counting, leaf alignment, and leaf tracking. We provide detailed information on the plants, imaging sensors, calibration, labeling, and baseline performances of this new database.     

松材线虫病害高光谱时序与敏感特征研究

采用高光谱仪ASD FieldSpec Pro FR,连续观测了健康和发病马尾松针叶的时序高光谱,分析了松材线虫病害光谱的时序特征、敏感时期和敏感特征。结果表明:与健康马尾松相比,病害马尾松时序光谱差异较大;病害首先造成红边区域内光谱反射率减低,然后再出现红边蓝移的2阶段光谱变化规律;感染松材线虫的马尾松9月初已经出现了病态植被典型的光谱特征;近红外平台内最大的一阶微分值、红边内一阶微分的总和(SDr)与蓝边内一阶微分的总和(SDb)的比值等是指示病害发生的显著性高光谱特征。     

基于叶裂的植物外观特征提取

计算机辅助植物识别(CAPI)是当前植物分类学的热点课题,叶片外观特征的自动提取是植物识别与分类的重要组成部分.首先对叶片图像进行预处理并提取出叶片的轮廓;然后通过对轮廓进行分析,采用训练的方式,确定了6+个阈值对叶裂进行精确分类;最后提取出叶裂排列方式、叶裂数和叶裂程度三大特征.实验通过对93种共343张叶片样本进行特征提取,获得了良好的实验结果.     

Classification of abnormal plant operation using multiple process variable trends

This paper illustrates two strategies for the detection and classification of abnormal process operating conditions in which multiple process variable trends are available. The first strategy uses a hidden Markov model (HMM) for overall process classification while the second method uses a back-propagation neural network (BPNN) to determine the overall process classification. The methods are compared in terms of their ability to detect and correctly diagnose a variety of abnormal operating conditions for a non-isothermal CSTR simulation. For the case study problem, the BPNN method resulted in better classification accuracy with a moderate increase in training time compared with the HMM approach.     

Review of Plant Identification Based on Image Processing

Plant recognition is closely related to people’s life. The operation of the traditional plant identification method is complicated, and is unfavorable for popularization. The rapid development of computer image processing and pattern recognition technology makes it possible for computer’s automatic recognition of plant species based on image processing. There are more and more researchers drawing their attention on the computer’s automatic identification technology based on plant images in recent years. Based on this, we have carried on a wide range of research and analysis on the plant identification method based on image processing in recent years. First of all, the research significance and history of plant recognition technologies are introduced in this paper; secondly, the main technologies and steps of plant recognition are reviewed; thirdly, more than 30 leaf features (including 16 shape features, 11 texture features, four color features), and then SVM was used to evaluate these features and their fusion features, and 8 commonly used classifiers are introduced in detail. Finally, the paper is ended with a conclusion of the insufficient of plant identification technologies and a prediction of future development.     

基于形状特征的植物叶片在线识别方法

针对传统植物识别方法工作任务量大,效率低下以及难以保证数据客观性的问题,提出了一种基于形状特征的植物叶片识别算法,并开发了一款C/S模式的植物叶片在线识别Android应用。叶片图像经预处理后,提取叶片的轮廓凸包顶点比、轮廓曲率方差等形状特征,采用KNN-SVM对叶片进行分类识别。实验结果表明,相比于一些已有识别算法,该算法可以达到更高的识别率;该Android应用稳定可靠,可以满足用户的需求。     

Semi-supervised orthogonal discriminant projection for plant leaf classification

Plant classification based on the leaf images is an important and tough task. For leaf classification problem, in this paper, a new weight measure is presented, and then a dimensional reduction algorithm, named semi-supervised orthogonal discriminant projection (SSODP), is proposed. SSODP makes full use of both the labeled and unlabeled data to construct the weight by incorporating the reliability information, the local neighborhood structure and the class information of the data. The experimental results on the two public plant leaf databases demonstrate that SSODP is more effective in terms of plant leaf classification rate.