Artificial Neural Network based Model for Fruit Grade Empirical Thresholding and Feature Extraction based Back Propagation
Article Sidebar
Main Article Content
Abstract
This study details a novel attribute retrieval method for use in pre-processing images, and then applies it to the development of an "artificial neural network" system based on back propagation for identifying fruits in photographs. The “Scale Conjugate Gradient” (SCG) technique is used For back propagation. In this paper, there are three stages to the process. First, MATLAB was used to process a variety of external image-based apple properties. Since merely colour is insufficient to judge the quality, size and weight characteristics were also taken into consideration. Second, features extraction was carried out during picture pre-processing to simplify the method by concentrating only on important features. The Support Vector Machine (SVM) algorithm is a favourite for creating classification models that are relatively small in weight. The classification in this work is done using the MATLAB-ANN (Artificial Neural Network) toolkit. A single hidden layer BP-ANN (Back propagation- artificial neural network) was employed with sigmoid activation functions,. The outcome was determined by the appropriate output variables, which is the apple's quality class, which was determined to be Class A, Class B, Class C, and Class D, respectively. The modeling result indicates the tremendous match between the data used in training and assumed output values. It also has shorter calculation time due to the SCG algorithm. It is also possible for apple producers and distributors to classify their fruit using this model and reduce the cost by avoiding manual classification.
Article Details
References
Ramya, S. Menaga, S.Dinesh. 2017, A Computer Vision Based Diseases Detection and Classification in Apple Fruits, IJERT, pp. 161-164.
Frederik Dara, Ariola Devolli, 2016 APPLYING ARTIFICIAL NEURAL NETWORKS (ANN) TECHNIQUES TO AUTOMATED VISUAL APPLE SORTING, Journal of Hygienic Engineering and Design, pp. 55-63.
M. N. Puspita, W. A. Kusuma, A. Kustiyo, r. Heryanto; 2015 “A Classification System for Jamu Efficacy Based on Formula Using Support Vector Machine and K-Means Algorithm as a Feature Selection”. ICACSIS, IEEE pp. 215-220.
Gopal Dutt, Ashutosh Kumar Bhatt, Sunil Kumar; 2015 “Disaster management Information Systtem Framework using Feed Forward Back Propagation Neural Network”. IJARCCE, DOI 10.17148/IJARCCE.2015.43122 pp. 510-514.
Suen J. P., Eheart J. W. (2003). Evaluation of neural networks for modelling nitrate concentration in rivers. J. Water Resour. Plan Manag., 129, (6), pp. 505-510.
Devrim Unay, Bernard Gosselin, 2014, Artificial neural network-based segmentation and apple grading by machine vision, DOI: 10.1109/ICIP.2005.1530134.
Vanakovarayan, S., S. Prasanna, S. Thulasidass, and V. Mathavan. "Non-Destructive Classification of Fruits by Using Machine Learning Techniques." In 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1-5. IEEE, 2021.
Varghese, Z.; Y.; Morrow, C.T.; Heinemann, P.H.; Sommer III, J.H.; Tao, Y.; Crassweller, R.M.; 1991. “Automated inspection of golden delicious apples using color computer vision”. ASAE Paper No. 91-7002.
A.K.Bhatt Et. All, 2015, “An Analysis of the Performance of Artificial Neural Network Technique for Apple Classification”, AI & Society: Journal of knowledge, culture and communication, imprint by Springer, ISSN 0951-5666.
Sarkar, N.; Wolfe, R.R.; 1985. “Feature extraction techniques for sorting tomatoes by computer vision”. TASAE Vol. 28(3): 970-974.
Guyer, D.E.; Miles, G.E.; Gaultney, L.D.; Schereiber, M.M.; 1993. “Application of machine vision to shape analysis in leaf and plant identification”. 1993. TASAE Vol. 36(1): 163-171.
Dickson, M.A.; Bausch, W.C.; Howarth, M.S.; 1994. “Classification of a broadleaf weed, a grassy weed, and corn using image processing techniques”. SPIE Vol. 2345: 297-305.
Ruiz, L.A.; Moltó, E.; Juste, F.; Aleixos, N.; 1995. “Aplicación de métodos ópticos para la inspección automática de productos hortofrutícolas”. VI Congreso de la Sociedad Española de Ciencias Hortícolas, 25-28 de Abril de 1995, Barcelona.
Harvey Jake G. Opeña1, John Paul T. Yusiong2; 2017 “Automated Tomato Maturity Grading Using ABC-Trained- Artificial Neural Networks”. Malaysian Journal of Computer Science. Vol. 30(1), 2017: 12-26.
Growe, T.G.; Delwiche, M.J.; 1996. “A system for fruit defect detection in real-time”. AGENG 96, Paper No. 96F-023.
Kumari, Neeraj, A. K. Bhatt, Rakesh Kr Dwivedi, and Rajendra Belwal. "Performance analysis of support vecor machine in defective and non defective mangoes classification." International Journal of Engineering and Advanced Technology (IJEAT) 8, no. 4 (2019): 1563-1572.
Moltó, E.; Aleixos, N.; Ruiz, L.A.; Vázquez, J.; Juste, F.; 1996. “An artificial vision system for fruit quality assessment”. AGENG 96, Madrid, Paper No. 96F-078.
McCulloch, W., and W. Pitts (1943), “A Logical Calculus of the Ideas Immanent in Nervous Activity”, Bulletin of Mathematical Biophysics, Vol. 5, pp.115–133.
Le, Tien-Thinh, Athanasia D. Skentou, Anna Mamou, and Panagiotis G. Asteris. "Correlating the Unconfined Compressive Strength of Rock with the Compressional Wave Velocity Effective Porosity and Schmidt Hammer Rebound Number Using Artificial Neural Networks." Rock Mechanics and Rock Engineering (2022): 1-36.
C.M.Bishop. Neural Networks for Pattern Recognition. Chapter 7, pp.253-294. Oxford University Press. 1995.
Tripathi, Praveen, Rajendra Belwal, and Ashutosh Kumar Bhatt. "Empirical Thresholding and Feature Extraction based Back Propagation-Artificial Neural Network Model for Fruit Grade." In International Conference on Advances in Engineering Science Management & Technology (ICAESMT)-2019, Uttaranchal University, Dehradun, India. 2019.