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Identification of apple leaf diseases using C-Grabcut algorithm and improved transfer learning base on low shot learning

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Abstract

Plant disease control is an indispensable research topic in the field of agriculture. Different apple leaf diseases may have similar manifestations, and it is time-consuming and laborious to rely on manual means. In this paper, we propose an apple leaf disease classification algorithm for a small number of samples, which is based on the C-Grabcut image segmentation algorithm proposed in this paper and the improved EfficientNetB4 transfer learning algorithm. Firstly, data augmentation is used to expand the samples, which effectively solves the problems of insufficient samples and unbalanced sample categories. Then the leaves are extracted from the images using the C-Grabcut algorithm to reduce the interference brought by the background. Finally, the improved Vgg16, ResNet50, EfficientNetB0, EfficientNetB4 and EfficientNetB7 transfer learning algorithms are used to classify leaves into four categories: rust, scab, multiply and healthy. The experimental results show that the improved EfficientB4 algorithm works best with an average accuracy of 98% and the Kappa value of 0.98. In addition, the C-Grabcut algorithm reduces the training time from 153 to 73 s during an epoch, allowing the proposed algorithms to be deployed on devices with lower computing power and memory.

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Data availability

The datasets analysed during the current study are available in the Kaggle repository, which are available from https://www.kaggle.com/c/plant-pathology-2020-fgvc7/data.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China [grant numbers 42061067, 52063002]; the Science and technology projects of Jiangxi Provincial Department of Education [grant numbers 190745].

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Authors and Affiliations

  1. School of Physics and Electronic Information, Gannan Normal University, Ganzhou, 341000, China

    Suyun Lian, Lixin Guan, Gui Zeng & Mengshan Li

  2. Electronics Engineering Department With CIE, Shenzhen University, Shenzhen, 518000, China

    Suyun Lian & Jihong Pei

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  1. Suyun Lian
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  2. Lixin Guan
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  3. Jihong Pei
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  4. Gui Zeng
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Correspondence to Lixin Guan.

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Lian, S., Guan, L., Pei, J. et al. Identification of apple leaf diseases using C-Grabcut algorithm and improved transfer learning base on low shot learning. Multimed Tools Appl 83, 27411–27433 (2024). https://doi.org/10.1007/s11042-023-16602-4

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  • DOI: https://doi.org/10.1007/s11042-023-16602-4

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