PFDINN: Comparison between Three Back-propagation Algorithms for Pear Fruit Disease Identification

  • Samar Amil Qassir Department of Computer Science, College of Science, Mustansiriyah University, Baghdad, Iraq
Keywords: Pear fruit diseases, k-means clustering, First and second order statistical features, Scaled conjugate gradient backpropagation SCG-BP, Resilient backpropagation R-BP, Bayesian regularization backpropagation BR-BP

Abstract

     The diseases presence in various species of fruits are the crucial parameter of economic composition and degradation of the cultivation industry around the world. The proposed pear fruit disease identification neural network (PFDINN) frame-work to identify three types of pear diseases was presented in this work. The major phases of the presented frame-work were as the following: (1) the infected area in the pear fruit was detected by using the algorithm of K-means clustering. (2) hybrid statistical features were computed over the segmented pear image and combined to form one descriptor. (3) Feed forward neural network (FFNN), which depends on three learning algorithms of back propagation (BP) training, namely Scaled conjugate gradient (SCG-BP), Resilient (R-BP) and Bayesian regularization (BR-BP), was used in the identification process. Pear fruit was taken as the experiment case during this work with three classifications of diseases, namely fire blight, pear scab, and sooty blotch, as compared to healthy pears. PFDINN framework was trained and tested using 2D pear fruit images collected from the Fruit Crops Diseases Database (FCDD). The presented framework achieved 94.6%, 97.3%, and 96.3% efficiency for SCG-BP, R-BP, and BR-BP, respectively. An accuracy value of 100% was achieved when the R-BP learning algorithm was trained for identification.

Published
2021-09-30
How to Cite
Qassir, S. A. (2021). PFDINN: Comparison between Three Back-propagation Algorithms for Pear Fruit Disease Identification. Iraqi Journal of Science, 62(9), 3128-3137. https://doi.org/10.24996/ijs.2021.62.9.28
Section
Computer Science