English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

international journal on advanced science, engineering and information technology/international journal of advanced science, engineering and information technology

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