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Deep learning framework for timely detection and classification of chili leaf diseases and pests

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Abstract

Food security continues to be a significant challenge the world over, with crop production becoming increasingly threatened by crop diseases and pest infestations. In the case of chili production, farmers often suffer significant yield loss and economic insecurity due to the unpredictable nature of both of these problems. Current pest control options (agrochemical and organic methods alike) have not reliably been enough for timely and effective control and demonstrate the importance of early and effective pest and disease identification processes. To solve this problem, the present work describes a deep learning implementation that leverages advanced YOLO-based architectures for the detection and classification of leaf diseases and pest on chili plant leaves. The present work trains and evaluates three models (YOLOv5, YOLOv7, and YOLOv8) on a newly created and balanced dataset of over 28,800 images combining 20 total classes of pest and leaf diseases. The dataset was supplemented by preprocessing the images and conducting an augmentation process to create a total of 32,000 images for training to generate reliable models. The results from the experiments found that YOLOv8 provided the best baseline performance of 95.1% mean Average Precision (mAP), while YOLOv5 had an mAP of 86.1%, and YOLOv7 had an mAP of 67.5%. An additional enhancement in the construction of a Modified YOLOv8 hybrid model—reflecting all advantages of YOLOv5, YOLOv7, and YOLOv8—achieved a highest mAP of 99.5% to be the most effective model in this study, the results suggest that the newly proposed Modified YOLOv8 framework, is highly accurate and reliable for the early detection of pests and diseases in chili, and is helpful to improve sustainable agricultural practices, mitigate crop losses, and increase global food security.

Data availability

The dataset images utilized for the analysis were collected by our team from different chilli agricultural lands of Guntur district of Andhra Pradesh, India, a well-known place for chilli cultivation all over the world. The authors assert that the images utilized for the representation or examination of previous research are cited in the article and appropriately referenced in the references section.

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Acknowledgements

The authors thank the School of Computer Science & Engineering, VITAP-University for the support and facilitation given to accomplish the project and article. The authors also thank the authors of the referenced articles for their ideas and suggestions for improvement.

Funding

Open access funding provided by Vellore Institute of Technology- AP University.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering , VITAP-University , Amaravati, Andhra Pradesh, India

    Kantha Raju Kanaparthi & S. Sudhakar Ilango

Authors

  1. Kantha Raju Kanaparthi
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  2. S. Sudhakar Ilango
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Contributions

The K. K has made the major contribution to the project with algorithm design and development, coding and analysis, manuscript preparation, etc. The S. I have contributed to the project in various ways including the review and problem statement, directions in coding and analysis and overall supervision of this project.

Corresponding authors

Correspondence to
Kantha Raju Kanaparthi or S. Sudhakar Ilango.

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Kanaparthi, K., Ilango, S.S. Deep learning framework for timely detection and classification of chili leaf diseases and pests.
Sci Rep (2026). https://doi.org/10.1038/s41598-025-34477-3

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  • DOI: https://doi.org/10.1038/s41598-025-34477-3

Keywords

  • Convolutional neural networks
  • Image analysis
  • Artificial intelligence
  • Sustainable agriculture
  • YOLO
  • Chili pest and disease detection

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