Automatic Plant Identification: Is Shape the Key Feature

This research investigates the application of texture features for leaf recognition for herbal plant identification. Malaysia is rich with herbal plants but not many people can identify them and know about their uses. Preservation of the knowledge of these herb plants is important since it enables the general public to gain useful knowledge which they can apply whenever necessary. Leaf image is chosen for plant recognition since it is available and visible all the time. Unlike flowers that are not always available or roots that are not visible and not easy to obtain, leaf is the most abundant type of data available in botanical reference collections. A comparative study has been conducted among three popular texture features that are Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Speeded-Up Robust Features (SURF) with multiclass Support Vector Machine (SVM) classifier. A new leaf dataset has been constructed from ten different herb plants. Experimental results using the new constructed dataset and Flavia, an existing dataset, indicate that HOG and LBP produce similar leaf recognition performance and they are better than SURF.

ISPEC Journal of Agricultural Sciences, 2021

Plants that are of great importance for humans and other living things are an integral part of our ecosystem. In today's world, where many plant species are at risk of disappearance, the identification of plants helps to protect and survive all natural life. There are many studies presented in the literature for plant identification. The most popular of these identification methods is leaf based classification. The reason for choosing leaves in this classification is that they are easier to obtain than other biometric components such as flowers available for a short period of time. Various biometric properties of the leaf must be determined for leaf classifications. In traditionally it is time consuming and expensive to perform this process visually by experts. In this article, various leaf biometric features obtained by digital image processing methods are used as the feature extraction step for automatic leaf classification. As the classification algorithms, Naive Bayes, Linea...

Journal of Computer Science, 2013

Plant classification based on leaf identification is becoming a popular trend. Each leaf carries substantial information that can be used to identify and classify the origin or the type of plant. In medical perspective, images have been used by doctors to diagnose diseases and this method has been proven reliable for years. Using the same method as doctors, researchers try to simulate the same principle to recognise a plant using high quality leaf images and complex mathematical formulae for computers to decide the origin and type of plants. The experiments have yielded many success stories in the lab, but some approaches have failed miserably when tested in the real world. This happens because researchers may have ignored the facts that the real world sampling may not have the luxury and complacency as what they may have in the lab. What this study intends to deliver is the ideal case approach in plant classification and recognition that not only applicable in the real world, but also acceptable in the lab. The consequence from this study is to introducing more external factors for consideration when experimenting real world sampling for leaf recognition and classification does this.

2010

The most extracted measures from plants are traditionally performed manually. This and the great biodiversity of species makes the taxonomical classification of plants a very complex and time-consuming task. In order to contribute to the plant species characterization, a novel approach based on color texture is proposed. Each color channel in a leaf texture is modeled as a surface, so the complexity in each channel, as well as in the interaction among these three color channels, can be measured using the Multi-scale fractal dimension. Results show the potential of this approach, which overcomes traditional color texture analysis methods, such as Chromaticity Moments and Gabor EEE descriptors.

The International journal of Multimedia & Its Applications, 2015

Because of huge diversity of plants existing on earth and large inter-species similarity, the manual process of identification of plants is difficult and at times, the results generated may be confusing. So, it is necessary to automate the process of identification of plants to generate faster and accurate results. Among various parts of plant, leaf is easily available in all seasons and can be easily scanned for understanding the shape details. So, in this paper, we have worked on plant identification using leaf image. In the process of identification of plants, the key challenge identified is to keep the size of feature vector reasonable and still achieve accurate and effective results. As the results are to be interpreted by the user, it was necessary to understand the theories behind human perception to reduce the semantic gap. We have designed and developed a tool 'SbLRS: Shape based Leaf Recognition System' which is a three-staged system that performs identification of plant using leaf shape. Our system can be helpful for following users-common layman person, as replacement for biologists in remote locations, as teaching aid for teachers and students, for a farmer to identify the plant species and to assist biologists in identification process. The parameters important for identifying leaf image according to human perception are identified and defined at three different levels. On the basis of results generated, the effectiveness of SbLRS is compared with existing contour based methods in terms of recall, precision. It was observed that our tool SbLRS showed satisfactory results for identification of plant using leaf at small feature vector size and simpler computations.

Several methods to identify plants have been proposed by several researchers. Commonly, the methods did not capture color information, because color was not recognized as an important aspect to the identification. In this research, shape and vein, color, and texture features were incorporated to classify a leaf. In this case, a neural network called Probabilistic Neural network (PNN) was used as a classifier. The experimental result shows that the method for classification gives average accuracy of 93.75% when it was tested on Flavia dataset, that contains 32 kinds of plant leaves. It means that the method gives better performance compared to the original work.

Expert Systems with Applications, 2021

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2011

Several methods to identify plants have been proposed by several researchers. Commonly, the methods did not capture color information, because color was not recognized as an important aspect to the identification. In this research, shape and vein, color, and texture features were incorporated to classify a leaf. In this case, a neural network called Probabilistic Neural network (PNN) was used as a classifier. The experimental result shows that the method for classification gives average accuracy of 93.75% when it was tested on Flavia dataset, that contains 32 kinds of plant leaves. It means that the method gives better performance compared to the original work.

2013

A number of shape features for automatic plant recognition based on digital image processing have been proposed by Pauwels et al. in 2009. A database with 15 classes and 171 leaf samples was considered for the evaluation of these measures using linear discriminant analysis and hierarchical clustering. The results obtained match the human visual shape perception with an overall accuracy of 87%.

Plant classification has a broad application prospective in agriculture and medicine, and is especially significant to the biology diversity research. As plants are vitally important for environmental protection, it is more important to identify and classify them accurately. Plant leaf classification is a technique where leaf is classified based on its different morphological features. The goal of this paper is to provide an overview of different aspects of texture based plant leaf classification and related things. At last we will be concluding about the efficient method i.e. the method that gives better performance compared to the other methods.