A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

Wu, Shangshang; Bao, Forrest Sheng; Xu, E.Y.; Wang, Yu-Xuan; Chang, Yi‐Fan; Xiang, Qiao-Liang

doi:10.1109/isspit.2007.4458016

Cited by 698 publications

(465 citation statements)

References 19 publications

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“…The Flavia dataset was collected by Wu et al [21], it contains 1907 RGB scans images of leaf. It is composed of 32 species; where, each species has 40 to 60 sample leaves.…”

Section: Flavia Datasetmentioning

confidence: 99%

Variant-Order Statistics based Model for RealTime Plant Species Recognition

Eid¹,

Darwish²

2017

IJITCS

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Abstract-There are an urgent need of categorizing plant by its species, to help botanist setting up a plant species database. However, plant recognition model is still very challenging task in computer vision and can be onerous and time consuming because of inefficient representation approaches. This paper, proposes a recognition model for classifying botanical species from leaf images, using combination of variant-order statistics based measures. Hence, the spatial coordinates values of gray pixels defines the qualities of texture, for the proposed model a gray-scale approach is adopted for analyzing the local patterns of leaves images using second and higher order statistical measures. While, first order statistical measures are used to extract color descriptors from leaves images.Evaluation of the proposed model shows the importance of combining variant-order statistics measures for enhancing the plant leaf recognition accuracy. Several experiments on Flavia dataset and swedish dataset are conducted. Experimental results indicates that; the proposed model yields to improve the recognition rate up to 97.1% and 94.7% for both Flavia and Swedish dataset respectively; while taking less execution time.

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“…The Flavia dataset was collected by Wu et al [21], it contains 1907 RGB scans images of leaf. It is composed of 32 species; where, each species has 40 to 60 sample leaves.…”

Section: Flavia Datasetmentioning

confidence: 99%

Variant-Order Statistics based Model for RealTime Plant Species Recognition

Eid¹,

Darwish²

2017

IJITCS

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“…The source of images of leaves used in this study is images of leaves found in the Flavia dataset which is publicly available (Wu et al, 2007). The Flavia dataset is a constrained set of leaf images taken against a white background and without any stem present.…”

Section: Data Setmentioning

confidence: 99%

A computer-based vision systems for automatic identification of plant species using kNN and genetic PCA

Babatunde

Armstrong

Leng

et al. 2015

JAI

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A B S T R A C TPrecision farming involves integration of different areas of disciplines to lower production costs and improve productivity. One major arm of precision farming or agriculture is the development of computer-based vision systems for automatic identification of plant species. This work involves application of k Nearest Neighbour (kNN) and genetic principal component analysis (GA-PCA) for the development of computer-based vision systems for automatic identification of plant species. As the first contribution, several image descriptors were extracted from the images of plants found in the Flavia dataset. Lots of these image features are affine maps and amalgamation of such massive features in one study is a novel idea. These descriptors are Zernike Moments (ZM), Fourier Descriptors (FDs), Lengendre Moments (LM) Hu 7 Moments, Texture, Geometrical properties and colour features. The GA-PCA (1907 x 41) feature space improved the classification accuracy of kNN from 84.98% to 88.75%.

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“…Works using such biosonar sensors for natural landmark classification were also proposed in [9] and [10]. Another method for plant classification was proposed by Wu et al [11]. They are using a computer vision leaf recognition algorithm to determine features describing the leaf's shape and size, and a probabilistic neural network (PNN) to learn the relation.…”

Section: Related Workmentioning

confidence: 99%

Plant Species Classification Using a 3D LIDAR Sensor and Machine Learning

Weiß

Biber

Laible

et al. 2010

2010 Ninth International Conference on Machine Learning and Applications

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Abstract-In the domain of agricultural robotics, one major application is crop scouting, e.g., for the task of weed control. For this task a key enabler is a robust detection and classification of the plant and species. Automatically distinguishing between plant species is a challenging task, because some species look very similar. It is also difficult to translate the symbolic high level description of the appearances and the differences between the plants used by humans, into a formal, computer understandable form. Also it is not possible to reliably detect structures, like leaves and branches in 3D data provided by our sensor. One approach to solve this problem is to learn how to classify the species by using a set of example plants and machine learning methods.In this paper we are introducing a method for distinguishing plant species using a 3D LIDAR sensor and supervised learning. For that we have developed a set of size and rotation invariant features and evaluated experimentally which are the most descriptive ones. Besides these features we have also compared different learning methods using the toolbox Weka. It turned out that the best methods for our application are simple logistic regression functions, support vector machines and neural networks. In our experiments we used six different plant species, typically available at common nurseries, and about 20 examples of each species. In the laboratory we were able to identify over 98% of these plants correctly.

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A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

Cited by 698 publications

References 19 publications

Variant-Order Statistics based Model for RealTime Plant Species Recognition

Variant-Order Statistics based Model for RealTime Plant Species Recognition

A computer-based vision systems for automatic identification of plant species using kNN and genetic PCA

Plant Species Classification Using a 3D LIDAR Sensor and Machine Learning

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