Multi-Layer Perceptron Neural Network Model Development for Chili Pepper Disease Diagnosis Using Filter and Wrapper Feature Selection Methods
Received: 4 August 2021 | Revised: 5 September 2021 and 21 September 2021 | Accepted: 27 September 2021 | Online: 2 October 2021
Corresponding author: S. Nuanmeesri
Abstract
The goal of the current study is to develop a diagnosis model for chili pepper disease diagnosis by applying filter and wrapper feature selection methods as well as a Multi-Layer Perceptron Neural Network (MLPNN). The data used for developing the model include 1) types, 2) causative agents, 3) areas of infection, 4) growth stages of infection, 5) conditions, 6) symptoms, and 7) 14 types of chili pepper diseases. These datasets were applied to the 3 feature selection techniques, including information gain, gain ratio, and wrapper. After selecting the key features, the selected datasets were utilized to develop the diagnosis model towards the application of MLPNN. According to the model’s effectiveness evaluation results, estimated by 10-fold cross-validation, it can be seen that the diagnosis model developed by applying the wrapper method along with MLPNN provided the highest level of effectiveness, with an accuracy of 98.91%, precision of 98.92%, and recall of 98.89%. The findings showed that the developed model is applicable.
Keywords:
chili pepper diseases, feature selection, multi-layer perceptron neural network, wrapperDownloads
References
K. Lertrat, "Production, planting, processing, marketing, and chili pepper products in Thailand," Research Community, vol. 73, pp. 15-20, May 2007.
S. Potghan, R. Rajamenakshi, and A. Bhise, "Multi-Layer Perceptron Based Lung Tumor Classification," in 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, Mar. 2018, pp. 499-502. https://doi.org/10.1109/ICECA.2018.8474864
K. Subhadra and B. Vikas, "Neural Network Based Intelligent System for Predicting Heart Disease," International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 5, pp. 484-487, 2019.
K. Sutha and J. J. Tamilselvi, "A review of feature selection algorithms for data mining techniques" International Journal on Computer Science and Engineering, vol. 7, no. 6, pp. 63-67, Jun. 2015.
P.-N. Tan, M. Steinbach, A. Karpatne, and V. Kumar, Introduction to Data Mining, 2nd ed. New York, USA: Pearson Education, 2019.
P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, 1st ed. Boston, Massachusetts, USA: Addison-Wesley, 2005.
Y. B. Wah, N. Ibrahim, H. A. Hamid, S. Abdul-Rahman, and S. Fong, "Feature selection methods: Case of filter and wrapper approaches for maximising classification accuracy," Pertanika Journal of Science & Technology, vol. 26, no. 1, pp. 329-340, Jan. 2018.
B. Karlik and A. V. Olgac, "Performance analysis of various activation functions in generalized MLP architectures of neural networks," International Journal of Artificial Intelligence and Expert Systems, vol. 1, no. 1, pp. 111-122, 2011.
S. Nuanmeesri, S. Chopvitayakun, P. Kadmateekarun, and L. Poomhiran, "Marigold flower disease prediction through deep neural network with multimodal image," International Journal of Engineering Trends and Technology, vol. 69, no. 7, pp. 174-180, Jul. 2021. https://doi.org/10.14445/22315381/IJETT-V69I7P224
S. Nuanmeesri, L. Poomhiran, and K. Ploydanai, "Improving the prediction of rotten fruit using convolutional neural network," International Journal of Engineering Trends and Technology, vol. 69, no. 7, pp. 51-55, Jul. 2021. https://doi.org/10.14445/22315381/IJETT-V69I7P207
A. N. Saeed, "A Machine Learning based Approach for Segmenting Retinal Nerve Images using Artificial Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 5986-5991, Aug. 2020. https://doi.org/10.48084/etasr.3666
M. B. Ayed, "Balanced Communication-Avoiding Support Vector Machine when Detecting Epilepsy based on EEG Signals," Engineering, Technology & Applied Science Research, vol. 10, no. 6, pp. 6462-6468, Dec. 2020. https://doi.org/10.48084/etasr.3878
A. Mustaqeem, S. M. Anwar, M. Majid, and A. R. Khan, "Wrapper method for feature selection to classify cardiac arrhythmia," in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Jeju, South Korea, 2017, pp. 3656-3659. https://doi.org/10.1109/EMBC.2017.8037650
S. Nuanmeesri and W. Sriurai, "Thai Water Buffalo Disease Analysis with the Application of Feature Selection Technique and Multi-Layer Perceptron Neural Network," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 6907-6911, Apr. 2021. https://doi.org/10.48084/etasr.4049
S. Sudhi-Aromna et al., A guide to chili pepper pests, Nonthaburi, Thailand: The Agricultural Co-operative Federation of Thailand, Ltd., 2014.
D. P. Patil, S. R. Kurkute, P. S. Sonar, and S. I. Antonov, "An advanced method for chilli plant disease detection using image processing," in 52nd International Scientific Conference On Information, Communication and Energy Systems and Technologies, Niš, Serbia, 2017, pp. 309-313.
M. Ataş, Y. Yardimci, and A. Temizel, "A new approach to aflatoxin detection in chili pepper by machine vision," Computers and Electronics in Agriculture, vol. 87, pp. 129-141, 2012. https://doi.org/10.1016/j.compag.2012.06.001
S. Jana, A. R. Begum, and S. Selvaganesan, "Design and analysis of pepper leaf disease detection using Deep Belief Network," European Journal of Molecular & Clinical Medicine, vol. 7, no. 9, pp. 1724-1731, 2020.
N. N. Ahmad Loti, M. R. Mohd Noor, and S.-W. Chang, "Integrated analysis of machine learning and deep learning in chili pest and disease identification," Journal of the Science of Food and Agriculture, vol. 101, no. 9, pp. 3582-3594, 2021. https://doi.org/10.1002/jsfa.10987
S. Das Chagas Silva Araujo, V. S. Malemath, and K. M. Sundaram, "Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant," Frontiers in Robotics and AI, vol. 8, 2021, Art. no. 650134. https://doi.org/10.3389/frobt.2021.650134
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