Classification of Jackfruit Fruit Damage Using Color Texture Features and Backpropagation Neural Network

Jonah Flor V. Oraño, Elmer A. Maravillas, Chris Jordan G. Aliac


An accessible and cost-effective technology for plant pest and disease diagnosis could be beneficial for the farmers to be equipped with the technical know-how in producing high quality and quantity of crop yields. This study presents an implementation of image processing and machine learning techniques in building a predictive model for a computer-based and a mobile-based classification of jackfruit fruit damages caused by pests (fruit borer and fruit fly) and diseases (rhizopus fruit rot and sclerotium fruit rot). First, captured images of healthy, and infected fruit were split into two datasets: 60% for training and 40% for the testing phase, wherein each set contains five different classes. Then pre-processing methods such as cropping, scaling, and median filtering were applied that would make these images appropriate for information extraction. Next, 13 Haralick texture features were extracted from color co-occurrence matrices generated from Hue, Saturation, and Luminance color components of pre-processed images. Through Pearson’s correlation approach, texture features such as uniformity, variance, sum average, sum entropy, and entropy were selected as significant descriptors for training the classification model using a backpropagation learning algorithm. Lastly, basic evaluation metrics such as accuracy, precision, sensitivity, specificity, and Cohen’s kappa were computed to determine the performance of the model in recognizing the type of fruit damage on an unforeseen dataset. As a result, an overall accuracy rate of 93.42% and a kappa value of 0.9146 were obtained. In addition, the developed application displays suggestions on the proper pest control or disease management of the identified damage on the fruit surface of jackfruit.


jackfruit; backpropagation neural network; color co-occurrence matrix; texture feature.

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