Convolutional Neural Network Applied to Tree Species Identification Based on Leaf     Images

In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).

Download Full-text

INDIVIDUAL TREE SPECIES CLASSIFICATION BASED ON TERRESTRIAL LASER SCANNING USING CURVATURE ESTIMATION AND CONVOLUTIONAL NEURAL NETWORK

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-1077-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 1077-1082

Author(s):

T. Mizoguchi ◽

A. Ishii ◽

H. Nakamura

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Tree Species ◽

Point Cloud ◽

Superior Performance ◽

Depth Image ◽

Species Classification ◽

Individual Tree ◽

Curvature Estimation ◽

Bark Texture

<p><strong>Abstract.</strong> In this paper, we propose a new method for specifying individual tree species based on depth and curvature image creation from point cloud captured by terrestrial laser scanner and Convolutional Neural Network (CNN). Given a point cloud of an individual tree, the proposed method first extracts the subset of points corresponding to a trunk at breast-height. Then branches and leaves are removed from the extracted points by RANSAC -based circle fitting, and the depth image is created by globally fitting a cubic polynomial surface to the remaining trunk points. Furthermore, principal curvatures are estimated at each scanned point by locally fitting a quadratic surface to its neighbouring points. Depth images clearly capture the bark texture involved by its split and tear-off, but its computation is unstable and may fail to acquire bark shape in the resulting images. In contrast, curvature estimation enables stable computation of surface concavity and convexity, and thus it can well represent local geometry of bark texture in the curvature images. In comparison to the depth image, the curvature image enables accurate classification for slanted trees with many branches and leaves. We also evaluated the effectiveness of a multi-modal approach for species classification in which depth and curvature images are analysed together using CNN and support vector machine. We verified the superior performance of our proposed method for point cloud of Japanese cedar and cypress trees.</p>

Download Full-text

Butterfly Species Identification Using Convolutional Neural Network (CNN)

2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) ◽

10.1109/i2cacis.2019.8825031 ◽

2019 ◽

Author(s):

Nur Nabila Kamaron Arzar ◽

Nurbaity Sabri ◽

Nur Farahin Mohd Johari ◽

Anis Amilah Shari ◽

Mohd Rahmat Mohd Noordin ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Species Identification ◽

Butterfly Species

Download Full-text

Plant Species Identification Using Convolutional Neural Network

Intelligent Data Communication Technologies and Internet of Things - Lecture Notes on Data Engineering and Communications Technologies ◽

10.1007/978-981-15-9509-7_50 ◽

2021 ◽

pp. 619-630

Author(s):

Harsha H. Ashturkar ◽

A. S. Bhalchandra

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Species Identification ◽

Plant Species ◽

Plant Species Identification

Download Full-text

Image based Bird Species Identification using Convolutional Neural Network

International Journal of Engineering Research and ◽

10.17577/ijertv9is060279 ◽

2020 ◽

Vol V9 (06) ◽

Author(s):

Satyam Raj ◽

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Species Identification ◽

Bird Species

Download Full-text

Performance Comparison of Convolutional Neural Network-based model using Gradient Descent Optimization algorithms for the Classification of Low Quality Underwater Images

Issue 4 - Journal of Science and Technology ◽

10.46243/jst.2020.v5.i5.pp227-236 ◽

2020 ◽

pp. 227-236

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Species Identification ◽

Fish Species ◽

Gradient Descent ◽

Stock Assessment ◽

Performance Comparison ◽

Stochastic Gradient Descent ◽

Fisheries Science ◽

Fish Species Identification

Underwater imagery and analysis plays a major role in fisheries management and fisheries science helping developing efficient and automated tools for cumbersome tasks such as fish species identification, stock assessment and abundance estimation. Majority of the existing tools for analysis still leverage conventional statistical algorithms and handcrafted image processing techniques which demand human interventions and are inefficient and prone to human errors. Computer vision based automated algorithms need a better generalisation capability and should be made efficient to address the ambiguities present in the underwater scenarios, and can be achieved through learning based algorithms based on artificial neural networks. This paper research about utilising the Convolutional Neural Network (CNN) based models for under water image classification for fish species identification. This paper also analyses and evaluates the performance of the proposed CNN models with different optimizers such as the Stochastic Gradient Descent (SGD),Adagrad, RMSprop, Adadelta, Adam and Nadam on classifying ten classes of images from the Fish4Knowledge(F4K) database.

Download Full-text

Wood Species Identification using Convolutional Neural Network (CNN) Architectures on Macroscopic Images

Journal of Information Technology and Computer Science ◽

10.25126/jitecs.201943155 ◽

2019 ◽

Vol 4 (3) ◽

pp. 274

Author(s):

Anindita Safna Oktaria ◽

Esa Prakasa ◽

Efri Suhartono

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Wood Species ◽

Tree Species ◽

Classification System ◽

Visual Observation ◽

Identification System ◽

Wood Identification ◽

Species Classification ◽

Identification Process

Indonesia is a country that is very rich in tree species that grow in forests. Wood growth in Indonesia consists of around 4000 species that have different names and characteristics. These differences can determine the quality and exact use of each type of wood. The procedure of standard identification is currently still carried out through visual observation by the wood anatomist. The wood identification process is very in need of the availability of wood anatomists, with a limited amount of wood anatomist will affect the result and the length of time to make an identification. This thesis uses an identification system that can classify wood based on species names with a macroscopic image of wood and the implementation of the Convolutional Neural Network (CNN) method as a classification algorithm. Supporting architecture used is AlexNet, ResNet, and GoogLeNet. Architecture is then compared to a simple CNN architecture that is made namely Kayu30Net. Kayu30Net architecture has a precision performance value reaching 84.6%, recall 83.9%, F1 score 83.1% and an accuracy of 71.6%. In the wood species classification system using CNN, it is obtained that AlexNet as the best architecture that refers to a precision value of 98.4%, recall 98.4%, F1 score 98.3% and an accuracy of 96.7%.

Download Full-text