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An Artificial-Intelligence-Based Novel Rice Grade Model for Severity Estimation of Rice Diseases

Author

Listed:
  • Rutuja Rajendra Patil

    (Department of Computer Science, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, Maharashtra, India)

  • Sumit Kumar

    (Department of Electronics and Telecommunication, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, Maharashtra, India)

  • Shwetambari Chiwhane

    (Department of Computer Science, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, Maharashtra, India)

  • Ruchi Rani

    (Department of Computer Science, Indian Institute of Information Technology, Kottayam 686635, Kerala, India)

  • Sanjeev Kumar Pippal

    (Department of Technology, NSBT, MGM University, Aurangabad 431005, Maharashtra, India)

Abstract

The pathogens such as fungi and bacteria can lead to rice diseases that can drastically impair crop production. Because the illness is difficult to control on a broad scale, crop field monitoring is one of the most effective methods of control. It allows for early detection of the disease and the implementation of preventative measures. Disease severity estimation based on digital picture analysis, where the pictures are obtained from the rice field using mobile devices, is one of the most effective control strategies. This paper offers a method for quantifying the severity of three rice crop diseases (brown spot, blast, and bacterial blight) that can determine the stage of plant disease. A total of 1200 images of rice illnesses and healthy images make up the input dataset. With the help of agricultural experts, the diseased zone was labeled according to the disease type using the Make Sense tool. More than 75% of the images in the dataset correspond to one disease label, healthy plants represent more than 15%, and multiple diseases represent 5% of the images labeled. This paper proposes a novel artificial intelligence rice grade model that uses an optimized faster-region-based convolutional neural network (FRCNN) approach to calculate the area of leaf instances and the infected regions. EfficientNet-B0 architecture was used as a backbone as the network shows the best accuracy (96.43%). The performance was compared with the CNN architectures: VGG16, ResNet101, and MobileNet. The model evaluation parameters used to measure the accuracy are positive predictive value, sensitivity, and intersection over union. This severity estimation method can be further deployed as a tool that allows farmers to obtain perfect predictions of the disease severity level based on lesions in the field conditions and produce crops more organically.

Suggested Citation

  • Rutuja Rajendra Patil & Sumit Kumar & Shwetambari Chiwhane & Ruchi Rani & Sanjeev Kumar Pippal, 2022. "An Artificial-Intelligence-Based Novel Rice Grade Model for Severity Estimation of Rice Diseases," Agriculture, MDPI, vol. 13(1), pages 1-19, December.
    • Handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:47-:d:1013225
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    References listed on IDEAS

    as
    1. Shuo Chen & Kefei Zhang & Yindi Zhao & Yaqin Sun & Wei Ban & Yu Chen & Huifu Zhuang & Xuewei Zhang & Jinxiang Liu & Tao Yang, 2021. "An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation," Agriculture, MDPI, vol. 11(5), pages 1-18, May.
    2. Yiannis Ampatzidis & Luigi De Bellis & Andrea Luvisi, 2017. "iPathology: Robotic Applications and Management of Plants and Plant Diseases," Sustainability, MDPI, vol. 9(6), pages 1-14, June.
    Full references (including those not matched with items on IDEAS)

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