Identifying corn leaves diseases by extensive use of transfer learning: a comparative study

<span lang="EN-US">Deep learning is currently playing an important role in image analysis and classification. Diseases maize diminish productivity, which a major cause of economic damages the agricultural business throughout world. Researchers have previously utilized hand-crafted characteristics to classify images identify leaf illnesses Maize plants. With advancement deep learning, researchers can now significantly enhance accuracy object classification identification. Using "Corn or Leaf Disease Dataset" from Kaggle website, four forms diseases were investigated: blight, common rust, gray spot, healthy. The pictures obtained these corn are categorized using convolutional neural network (CNN) models that been pre-trained (GoogleNet, AlexNet, ResNet50 VGG16). Accuracy, precision, specificity, recall, F-score, time six metrics used assess performance any transfer (TL) model. MATLAB programming software design train TL models. each item dataset has checked. It determined GoogleNet, VGG16, 98.57%, 98.81%, 99.05%, 99.36%, respectively.</span>