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Performance analysis of segmentation models to detect leaf diseases in tomato plant

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Abstract

In agriculture around 22% of crop yield loss is due to living and non-living organisms such as biotic and abiotic stress/disease. The early-stage diagnosis of these stresses is an important issue for farmers through naked eyes. Using computer vision technologies can detect the pattern and clustering of diseases at an early stage. However, in recent times, deep learning technologies based on computer vision is helpful for the diagnosis of biotic stress (single biotic and multi biotic) in tomato plant leaves. In this work, the PlantVillage dataset is gathered for the segmentation of object detection. The labeled, enhanced and augmented data has been used for training the model. The proposed hybrid Deep Segmentation Convolutional Neural Network (Hybrid-DSCNN) model has been segmenting the diseased objects in the tomato plant. This Hybrid-DSCNN is assembled using U-Net and Seg-Net pre-trained models with instance segmentation for better detection of objects. The semantic segmented data has been recognized for the single and multiple leaf diseases for identification and classification in this work. A comparison of the predicted Hybrid-DSCNN model's output has been made with other modified U-Net, M-SegNet, and modified U-SegNet in terms of Accuracy, Precision, Recall, and Intersection over Union (IoU), and mean Intersection over Union (mIoU). The proposed model processed 1004 images in 30 ns,which is better than other compared models. The accuracy achieved using the proposed model is 98.24%, which is far better than other modified segmentation models.

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Data availability

Data and source codes are available from the authors upon reasonable request.

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Authors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

    Prabhjot Kaur, Shilpi Harnal & Vinay Gautam

  2. School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India

    Mukund Pratap Singh

  3. Department of Computer Science & Engineering, Rashtrakavi Ramdhari Singh Dinkar College of Engineering, Begusarai, India

    Santar Pal Singh

Authors
  1. Prabhjot Kaur
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  2. Shilpi Harnal
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  3. Vinay Gautam
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  4. Mukund Pratap Singh
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  5. Santar Pal Singh
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Contributions

PK.: Conceptualization, data collection and Methodology, Writing, SH.:Methodology and Supervision, VG: Conceptualization and Supervision, MPS: Validation and supervision, SPS: Validation.

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Correspondence to Santar Pal Singh.

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Kaur, P., Harnal, S., Gautam, V. et al. Performance analysis of segmentation models to detect leaf diseases in tomato plant. Multimed Tools Appl 83, 16019–16043 (2024). https://doi.org/10.1007/s11042-023-16238-4

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