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Deep Network with Score Level Fusion and Inference-Based Transfer Learning to Recognize Leaf Blight and Fruit Rot Diseases of Eggplant

Author

Listed:
  • Md. Reduanul Haque

    (Information Technology, Murdoch University, Murdoch, WA 6150, Australia)

  • Ferdous Sohel

    (Information Technology, Murdoch University, Murdoch, WA 6150, Australia
    Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA 6150, Australia)

Abstract

Eggplant is a popular vegetable crop. Eggplant yields can be affected by various diseases. Automatic detection and recognition of diseases is an important step toward improving crop yields. In this paper, we used a two-stream deep fusion architecture, employing CNN-SVM and CNN-Softmax pipelines, along with an inference model to infer the disease classes. A dataset of 2284 images was sourced from primary (using a consumer RGB camera) and secondary sources (the internet). The dataset contained images of nine eggplant diseases. Experimental results show that the proposed method achieved better accuracy and lower false-positive results compared to other deep learning methods (such as VGG16, Inception V3, VGG 19, MobileNet, NasNetMobile, and ResNet50).

Suggested Citation

  • Md. Reduanul Haque & Ferdous Sohel, 2022. "Deep Network with Score Level Fusion and Inference-Based Transfer Learning to Recognize Leaf Blight and Fruit Rot Diseases of Eggplant," Agriculture, MDPI, vol. 12(8), pages 1-16, August.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1160-:d:880586
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    Cited by:

    1. Lichao Liu & Quanpeng Bi & Jing Liang & Zhaodong Li & Weiwei Wang & Quan Zheng, 2022. "Farmland Soil Block Identification and Distribution Statistics Based on Deep Learning," Agriculture, MDPI, vol. 12(12), pages 1-17, November.

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