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Mob-psp: modified MobileNet-V2 network for real-time detection of tomato diseases

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Abstract

Tomato, as an essential food crop, is consumed worldwide, and at the same time, it is susceptible to several diseases that lead to a reduction in tomato yield. Proper diagnosis of tomato diseases is required to increase the output of tomato crops. For this purpose, this paper proposes a tomato plant disease detection algorithm based on Pyramid Scene Parsing Network (PSPNet) and deep learning. First, the training data set is augmented with data to alleviate the data imbalance problem in each category, and then the augmented images are fed into the proposed Mob-PSP network for training. The proposed network utilizes the lightweight MobileNet-V2 model as the feature extraction technique while integrating the PSPNet module to enhance the network’s detection performance. The aim is to effectively extract local and global features from plant disease images, which are being introduced in plant disease detection. This study evaluated the model on the tomato subset of the public data set PlantVillage. The experimental results demonstrate that this algorithm achieves a balance between inference speed and detection accuracy, outperforming other state-of-the-art algorithms. Additionally, compared to the baseline model Inception-V3, the inference speed is improved by 10.73 frames per second, while maintaining an average accuracy of 99.69\(\%\) with only 6.5M parameters.

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Data Availability

The associated data sets of the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the Fujian Province Nature Science Foundation of China under Grant No.2024J01818 and No.2021J011002, the Research Project on Education and Teaching Reform of Undergraduate Colleges and Universities in Fujian Province under Grant No.FBJG20210070 and No.FBJY20230170, and the 2022 Annual Project of the Fourteenth Five-Year Plan for Fujian Educational Science under Grant No.FJJKBK22-173.

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Authors and Affiliations

  1. School of Computer Science, Minnan Normal University, Zhangzhou, 363000, Fujian, China

    Hengmiao Qiu, Jingmin Yang, Juan Jiang & Wenjie Zhang

  2. Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou, 363000, Fujian, China

    Hengmiao Qiu, Jingmin Yang, Juan Jiang & Wenjie Zhang

  3. Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan, China

    Jingmin Yang

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  1. Hengmiao Qiu
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Correspondence to Jingmin Yang.

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Qiu, H., Yang, J., Jiang, J. et al. Mob-psp: modified MobileNet-V2 network for real-time detection of tomato diseases. J Real-Time Image Proc 21, 181 (2024). https://doi.org/10.1007/s11554-024-01561-2

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