A Study on the Performance of Deep Learning Models for Leaf Disease Detection


  • G Sucharitha Institute of Aeronautical Engineering College
  • M Sirisha Institute of Aeronautical Engineering College
  • K Pravalika Institute of Aeronautical Engineering College
  • K. Navya Gayathri Institute of Aeronautical Engineering College




InceptionV3, MobileNet, DenseNet121, Inception-ResNetV2, leaf Disease, Pretrained models, ResNet152V2, Classification


The backbone of our Indian economy is agriculture. Plant diseases are a key contributor to substantial reductions in crop quality and quantity. Finding leaf diseases is a crucial job in the study of plant pathology. So, Deep learning models are essential for classification objectives with positive outcomes. Many different methods have been employed in recent years to classify plant diseases. This work has aided in identifying and categorizing a plant leaf disease. Images of Tomato, Potato, and Pepper plant leaves from the PlantVillage Database, which includes fifteen disease classifications, were used in this study. The pre-trained Deep learning models like InceptionV3, MobileNet, DenseNet121, Inception-ResNetV2, and ResNet152V2 are utilized to diagnose leaf diseases. The classification of both healthy and various sorts of leaf illnesses is taught to deep learning models.



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How to Cite

G. Sucharitha, M. Sirisha, K. Pravalika, and K. N. Gayathri, “A Study on the Performance of Deep Learning Models for Leaf Disease Detection”, EAI Endorsed Trans IoT, vol. 10, Dec. 2023.