Explainable Neural Network for Classification of Cotton Leaf Diseases

Javeria Amin (), Muhammad Almas Anjum, Muhammad Sharif, Seifedine Kadry and Jungeun Kim ()
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Javeria Amin: Computer Science Department, University of Wah, Wah 47010, Pakistan
Muhammad Almas Anjum: National University of Technology, Islamabad 44000, Pakistan
Muhammad Sharif: Wah Campus, COMSATS University Islamabad, Wah 47040, Pakistan
Seifedine Kadry: Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
Jungeun Kim: Department of Software, Kongju National University, Cheonan 31080, Republic of Korea

Agriculture, 2022, vol. 12, issue 12, 1-11

Abstract: Every nation’s development depends on agriculture. The term “cash crops” refers to cotton and other important crops. Most pathogens that significantly harm crops also impact cotton. Numerous diseases that influence yield via the leaf, such as powdery mildew, leaf curl, leaf spot, target spot, bacterial blight, and nutrient deficiencies, can affect cotton. Early disease detection protects crops from additional harm. Computerized methods perform a vital role in cotton leaf disease detection at an early stage. The method consists of two core steps such as feature extraction and classification. First, in the proposed method, data augmentation is applied to balance the input data. After that, features are extracted from a pre-trained VGG-16 model and passed to 11 fully convolutional layers, which freeze the majority and randomly initialize convolutional features to subsequently generate a score of the anomaly map, which defines the probability of the lesion region. The proposed model is trained on the selected hyperparameters that produce great classification results. The proposed model performance is evaluated on two publicly available Kaggle datasets, Cotton Leaf and Disease. The proposed method provides 99.99% accuracy, which is competent compared to existing methods.

Keywords: VGG-16; heat map; explainable neural network; cotton leaf disease (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: 2022
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