Method for distinguishing tree species based on leaf pictures
A picture and leaf technology, applied in the field of pattern recognition, can solve the problems of low recognition accuracy, complex algorithm, and large amount of calculation, and achieve the effect of improving accuracy, enhancing robustness, and improving convergence speed.
Pending Publication Date: 2022-05-31
秦延宁
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[0008] The purpose of the present invention is to provide a method for distinguishing tree species based on leaf pictures, to solve the problems of complex algorithm, large amount of calculation, and low recognition accuracy, and improve the robustness of the algorithm
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Embodiment 1
[0077] Such as Figure 11 As shown, a method for distinguishing tree species based on leaf pictures includes the following steps:
[0078] Step 1, collect leaf pictures, divide training set and test set;
[0079] Collect pictures of different tree species. The tree species include: ginkgo, maple, willow, pomegranate, and birch. The number of leaves for each type is 100, totaling 500. The size of the picture is 300*300 pixels. The collected pictures are used as a data set, and the data set is divided into For the training set and the test set, the ratio of the training set to the test set is 8:2.
[0080] Step 2, image preprocessing;
[0081] Preprocess the collected pictures, preprocessing includes: denoising, grayscale, binarization, edge detection, erosion, expansion, filling;
[0082] The denoising methods include Butterworth low-pass filter denoising, FIR low-pass filter denoising, moving average filter denoising, median filter denoising, Wiener filter denoising, adapti...
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The invention relates to the field of pattern recognition, in particular to a method for distinguishing tree species based on leaf pictures. The problems that the algorithm is complex, the calculation amount is large, and the recognition accuracy is not high are solved. The method comprises the steps of collecting leaf pictures, and dividing a training set and a test set; preprocessing the picture; extracting features of leaves of the training set; the extracted shape features are integrated to serve as feature vectors, the feature vectors are normalized firstly, then the normalized feature vectors are input into a BP neural network to be trained, and a tree species distinguishing model is obtained; and extracting features of the leaves in the test set, inputting the features into the tree species identification model, and outputting an identification result. According to the method, the image is preprocessed, so that subsequent extraction and recognition of leaf features are facilitated; normalization processing is carried out on the extracted feature vectors, statistical distribution of unified samples is summarized, and the convergence speed of the model is improved; the BP neural network is used for training, a tree species distinguishing model is obtained, the robustness of the algorithm is enhanced, and the method can be applied to more scenes.
Description
technical field [0001] The invention relates to the field of pattern recognition, in particular to a method for distinguishing tree species based on leaf pictures. Background technique [0002] Pattern recognition was born in the 1920s. With the emergence of computers in the 1940s and the rise of artificial intelligence in the 1950s, pattern recognition rapidly developed into a discipline in the early 1960s. Pattern recognition is the process of performing various analysis and judgments according to the alignment of the input raw data, so as to obtain its category attributes and feature judgments. The function and purpose of pattern recognition is to correctly classify a specific thing into a certain category. [0003] With the continuous development and progress of science and technology, pattern recognition is used more and more widely. Pattern recognition can classify things more accurately and quickly, saving people a lot of time, manpower and material resources. [00...
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Patent Timeline
Login to View More IPC IPC(8): G06T5/00G06T5/30G06T7/13G06T7/62G06N3/08G06K9/62G06V10/764G06V10/774G06V10/74G06V10/82
CPCG06T5/30G06N3/084G06T7/13G06T7/62G06F18/22G06F18/214G06F18/24G06T5/70
Inventor 秦延宁
Owner 秦延宁