A Hybrid Framework for Detection and Analysis of Leaf Blight Using Guava Leaves Imaging

Sidrah Mumtaz, Mudassar Raza, Ofonime Dominic Okon, Saeed Ur Rehman (), Adham E. Ragab and Hafiz Tayyab Rauf ()
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Sidrah Mumtaz: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan
Mudassar Raza: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan
Ofonime Dominic Okon: Department of Electrical/Electronics & Computer Engineering, Faculty of Engineering, University of Uyo, Uyo 520103, Nigeria
Saeed Ur Rehman: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan
Adham E. Ragab: Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
Hafiz Tayyab Rauf: Independent Researcher, Bradford BD8 0HS, UK

Agriculture, 2023, vol. 13, issue 3, 1-22

Abstract: Fruit is an essential element of human life and a significant gain for the agriculture sector. Guava is a common fruit found in different countries. It is considered the fourth primary fruit in Pakistan. Several bacterial and fungal diseases found in guava fruit decrease production daily. Leaf Blight is a common disease found in guava fruit that affects the growth and production of fruit. Automatic detection of leaf blight disease in guava fruit can help avoid decreases in its production. In this research, we proposed a CNN-based deep model named SidNet. The proposed model contains thirty-three layers. We used a guava dataset for early recognition of leaf blight, which consists of two classes. Initially, the YCbCr color space was employed as a preprocessing step in detecting leaf blight. As the original dataset was small, data augmentation was performed. DarkNet-53, AlexNet, and the proposed SidNet were used for feature acquisition. The features were fused to get the best-desired results. Binary Gray Wolf Optimization (BGWO) was used on the fused features for feature selection. The optimized features were given to the variants of SVM and KNN classifiers for classification. The experiments were performed on 5- and 10-fold cross validation. The highest achievable outcomes were 98.9% with 5-fold and 99.2% with 10-fold cross validation, confirming the evidence that the identification of Leaf Blight is accurate, successful, and efficient.

Keywords: AlexNet; BGWO; CNN; DarkNet-53; deep learning; entropy; KNN; ROI; SVM; YCbCr (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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