A Review on Advances in Automated Plant Disease Detection


  • Radhika Bhagwat Department of Technology, Savitribai Phule Pune University, Pune, India; Department of Information Technology, Cummins College of Engineering for Women, Pune, India
  • Yogesh Dandawate Electronics and Telecommunication Engineering, Vishwakarma Institute of Information Technology, Pune, India




plant disease detection, visible range image, spectral image, traditional machine learning, deep learning


Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.


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How to Cite

R. Bhagwat and Y. . Dandawate, “A Review on Advances in Automated Plant Disease Detection”, Int. j. eng. technol. innov., vol. 11, no. 4, pp. 251–264, Sep. 2021.