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Plant leaf identification method based on deep learning

A technology of plant leaves, recognition method, applied in the field of digital image processing

Inactive Publication Date: 2018-06-08
NORTHEAST FORESTRY UNIVERSITY
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Problems solved by technology

However, it is often difficult to achieve better recognition results in complex backgrounds with different blade angles.

Method used

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  • Plant leaf identification method based on deep learning
  • Plant leaf identification method based on deep learning
  • Plant leaf identification method based on deep learning

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Embodiment Construction

[0031] In this paper, the leaves of five different species were used for identification, namely Chongyangmu, Fufangteng, Pittosporum, Hongye Prunus and Weeping Willow. Among them, the first three kinds of leaves are similar in shape and appear green. The fourth leaf is similar to the first three forms, but is purple in color. The last leaves are green but have a distinct shape difference.

[0032] Images of the blades were captured by a 12-megapixel camera. Compared with using an optical scanner to obtain a two-dimensional structure, obtaining leaf morphology at different angles through a camera can help improve the recognition performance of leaf images taken in the field. In order to obtain good image quality and easier segmentation of leaves from natural backgrounds, training samples are collected with a whiteboard as the background. In addition, the leaves in the training images need to have complete contours, and the less damage on the leaves, the better. Each categor...

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Abstract

Plant species can be distinguished mainly on the basis of the identification of plant leaf characteristics. Nevertheless, most identification systems show poor performance when small targets, including plant leaves and the like, are detected under a complex background. In order to improve the identification ability of plant leaves in a complex environment. The invention puts forward a plant leaf identification method based on deep learning. Inception V2 with BN (Batch Normalization) is used for replacing a convolutional neural layer in a Faster RCNN (Region Convolutional Neural Network) to provide multi-scale image features for an RPN (Region Proposal Network). In addition, an original image is firstly segmented into an appointed size according to a grid, and the segmented images are loaded to a network which is put forward in sequence. Through the accurate classification of Softmax and bounding box regressor, and the segmented imaged with identification tags are spliced to obtain a final image. An experiment result identifies leaves under the complex background. The method has higher identification accuracy than the Faster RCNN.

Description

technical field [0001] The technical field of the present invention is digital image processing. Using the deep convolutional network FasterRCNN in deep learning combined with batch normalization Inception V2, a fast and accurate identification method for plant leaves under complex backgrounds is proposed. Background technique [0002] As an important resource, plants can not only provide human food and medicinal materials, but also play an active role in maintaining ecological balance. Therefore, it is very important to identify and protect plants. As we all know, plant leaves contain a large amount of species information such as different textures, colors and morphological structures, which play a vital role in distinguishing plant species. Unlike flowers or fruits, plant leaves are present in almost every season and vary dramatically from season to season. Because these advantages of leaves can be easily obtained, classifying and identifying plants through leaves has be...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04
CPCG06V20/35G06N3/045
Inventor 任洪娥朱晓龙朱朦王金聪
Owner NORTHEAST FORESTRY UNIVERSITY
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