Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection

Muhammad Attique Khan, Abdullah Alqahtani, Aimal Khan, Shtwai Alsubai, Adel Binbusayyis, M. Munawwar Iqbal Ch, Hwan Seung Yong, Jaehyuk Cha

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Agriculture has becomes an immense area of research and is ascertained as a key element in the area of computer vision. In the agriculture field, image processing acts as a primary part. Cucumber is an important vegetable and its production in Pakistan is higher as compared to the other vegetables because of its use in salads. However, the diseases of cucumber such as Angular leaf spot, Anthracnose, blight, Downy mildew, and powdery mildew widely decrease the quality and quantity. Lately, numerous methods have been proposed for the identification and classification of diseases. Early detection and then treatment of the diseases in plants is important to prevent the crop from a disastrous decrease in yields. Many classification techniques have been proposed but still, they are facing some challenges such as noise, redundant features, and extraction of relevant features. In this work, an automated framework is proposed using deep learning and best feature selection for cucumber leaf diseases classification. In the proposed framework, initially, an augmentation technique is applied to the original images by creating more training data from existing samples and handling the problem of the imbalanced dataset. Then two different phases are utilized. In the first phase, fine-tuned four pre-trained models and select the best of them based on the accuracy. Features are extracted from the selected fine-tuned model and refined through the Entropy-ELM technique. In the second phase, fused the features of all four fine-tuned models and apply the Entropy-ELM technique, and finally fused with phase 1 selected feature. Finally, the fused features are recognized using machine learning classifiers for the final classification. The experimental process is conducted on five different datasets. On these datasets, the best-achieved accuracy is 98.4%. The proposed framework is evaluated on each step and also compared with some recent techniques. The comparison with some recent techniques showed that the proposed method obtained an improved performance.

Original languageEnglish
Article number593
JournalApplied Sciences (Switzerland)
Volume12
Issue number2
DOIs
StatePublished - 1 Jan 2022

Keywords

  • Crops diseases
  • Data augmentation
  • Deep learning
  • Entropy
  • Features fusion
  • Machine learning

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