A neuro-genetic hybrid approach to automatic identification of plant leaves

Author

Oluleye Hezekiah Babatunde, Edith Cowan UniversityFollow

Date of Award

2015

Document Type

Thesis

Publisher

Edith Cowan University

Degree Name

Doctor of Philosophy

School

School of Computer and Security Science

Faculty

Faculty of Health, Engineering and Science

First Supervisor

Dr Leisa Armstrong

Second Supervisor

Dr Jinsong Leng

Third Supervisor

Professor Dean Diepeveen

Abstract

Plants are essential for the existence of most living things on this planet. Plants are used for providing food, shelter, and medicine. The ability to identify plants is very important for several applications, including conservation of endangered plant species, rehabilitation of lands after mining activities and differentiating crop plants from weeds.

In recent times, many researchers have made attempts to develop automated plant species recognition systems. However, the current computer-based plants recognition systems have limitations as some plants are naturally complex, thus it is difficult to extract and represent their features. Further, natural differences of features within the same plant and similarities between plants of different species cause problems in classification.

This thesis developed a novel hybrid intelligent system based on a neuro-genetic model for automatic recognition of plants using leaf image analysis based on novel approach of combining several image descriptors with Cellular Neural Networks (CNN), Genetic Algorithm (GA), and Probabilistic Neural Networks (PNN) to address classification challenges in plant computer-based plant species identification using the images of plant leaves.

A GA-based feature selection module was developed to select the best of these leaf features. Particle Swam Optimization (PSO) and Principal Component Analysis (PCA) were also used sideways for comparison and to provide rigorous feature selection and analysis. Statistical analysis using ANOVA and correlation techniques confirmed the effectiveness of the GA-based and PSO-based techniques as there were no redundant features, since the subset of features selected by both techniques correlated well. The number of principal components (PC) from the past were selected by conventional method associated with PCA. However, in this study, GA was used to select a minimum number of PC from the original PC space. This reduced computational cost with respect to time and increased the accuracy of the classifier used. The algebraic nature of the GA’s fitness function ensures good performance of the GA. Furthermore, GA was also used to optimize the parameters of a CNN (CNN for image segmentation) and then uniquely combined with PNN to improve and stabilize the performance of the classification system. The CNN (being an ordinary differential equation (ODE)) was solved using Runge-Kutta 4th order algorithm in order to minimize descritisation errors associated with edge detection.

This study involved the extraction of 112 features from the images of plant species found in the Flavia dataset (publically available) using MATLAB programming environment. These features include Zernike Moments (20 ZMs), Fourier Descriptors (21 FDs), Legendre Moments (20 LMs), Hu 7 Moments (7 Hu7Ms), Texture Properties (22 TP) , Geometrical Properties (10 GP), and Colour features (12 CF). With the use of GA, only 14 features were finally selected for optimal accuracy. The PNN was genetically optimized to ensure optimal accuracy since it is not the best practise to fix the tunning parameters for the PNN arbitrarily. Two separate GA algorithms were implemented to optimize the PNN, that is, the GA provided by MATLAB Optimization Toolbox (GA1) and a separately implemented GA (GA2). The best chromosome (PNN spread) for GA1 was 0.035 with associated classification accuracy of 91.3740% while a spread value of 0.06 was obtained from GA2 giving rise to improved classification accuracy of 92.62%. The PNN-based classifier used in this study was benchmarked against other classifiers such as Multi-layer perceptron (MLP), K Nearest Neigbhour (kNN), Naive Bayes Classifier (NBC), Radial Basis Function (RBF), Ensemble classifiers (Adaboost). The best candidate among these classifiers was the genetically optimized PNN. Some computational theoretic properties on PNN are also presented.

Related Publications

Babatunde, O., Armstrong, L., Leng, J., & Diepeveen, D. (2014). Zernike Moments and Genetic Algorithm: Tutorial and Application. British Journal of Mathematics and Computer Science, 4(15 ), 2217-2236. Article available here.

Babatunde, O., Armstrong, L., Leng, J., & Diepeveen, D. (2014). (2014 ) On the application of genetic probabilistic neural network and cellular neural networks in precision agriculture. Asian Journal of Computer and Information Systems, 2(4), 90-101. Link to article available here.

Babatunde, O., Armstrong, L., Leng, J., & Diepeveen, D. (2014). A Genetic Algorithm-Based Feature Selection. International Journal of Electronics Communication and Computer Engineering, 5(4), 899-905. Link to article available here.

Babatunde, 0., Armstrong, L., Leng, J., & Diepeveen, D. (2015). A survey of computer-based vision systems for automatic identification of plant species. Journal of Agricultural Informatics; 6(1), 61-71. Link to article available here.

Babatunde, 0., Armstrong, L., Leng, J., & Diepeveen, D. (2015). Comparative analysis of Genetic Algorithm and Particle Swam Optimization: An application in precision agriculture. Asian Journal of Computer and Information Systems, 3(1), 1-12. Link to article available here.

Babatunde, 0., Armstrong, L., Leng, J., & Diepeveen, D. (2015). A computer-based vision systems for automatic identification of plant species using kNN and genetic PCA. Journal of Agricultural Informatics. 6(2) ; 32-44. Link to article available here.

Babatunde, 0., Armstrong, L., Leng, J., & Diepeveen, D. (2015). A Neuronal Classification System for Plant Leaves using Genetic Image Segmentation. British Journal of Mathematics & Computer Science. 9(3), 261-278. doi:10.9734/BJMCS/2015/14611; Link to article available here.

Babatunde, 0., Armstrong, L., Leng, J., Diepeveen, D. (2014a). Application of cellular neural networks and naivebayes classifier in agriculture. Paper presented at the Proceedings of AFITA 2014, 9th Conference of the Asian Federation for Information Technology in Agriculture, Australia, Perth, 29 September to 2nd October 2014, 63-72. Link to article available here.

Babatunde, 0., Armstrong, L.,- Leng, J., & Diepeveen, D. (2014). A computational approachto plant leaves identification. Paper presented at the Proceedings of AFITA 2014, 9th Conference of the AsianFederation for Information Technology in Agriculture, Australia, Perth, 29 September to 2nd 0ctober 2014.

Recommended Citation

Babatunde, O. H. (2015). A neuro-genetic hybrid approach to automatic identification of plant leaves. Edith Cowan University. Retrieved from https://ro.ecu.edu.au/theses/1733