Investigation of early symptoms of tomato leaf disorder by using analysing image and deep learning models


  • Surendra Reddy Vinta Vellore Institute of Technology University image/svg+xml
  • Ashok Kumar Koshariya Lovely Professional University image/svg+xml
  • Sampath Kumar S Sri Eshwar College of Engineering image/svg+xml
  • Aditya National Institute of Food Technology Entrepreneurship and Management
  • Annantharao Gottimukkala Koneru Lakshmaiah Education Foundation image/svg+xml



Leaf illness, Image processing, crop disease, Deep learning


Despite rapid population growth, agriculture feeds everyone. To feed the people, agriculture must detect plant illnesses early. Predicting crop diseases early is unfortunate. The publication educates farmers about cutting-edge plant leaf disease-reduction strategies. Since tomato is a readily accessible vegetable, machine learning and image processing with an accurate algorithm are used to identify tomato leaf illnesses. This study examines disordered tomato leaf samples. Based on early signs, farmers may quickly identify tomato leaf problem samples. Histogram Equalization improves tomato leaf samples after re sizing them to 256 × 256 pixels. K-means clustering divides data space into Voronoi cells. Contour tracing extracts leaf sample boundaries. Discrete Wavelet Transform, Principal Component Analysis, and Grey Level Co-occurrence Matrix retrieve leaf sample information.


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How to Cite

S. R. Vinta, A. K. Koshariya, S. Kumar S, Aditya, and A. Gottimukkala, “Investigation of early symptoms of tomato leaf disorder by using analysing image and deep learning models ”, EAI Endorsed Trans IoT, vol. 10, Jan. 2024.