Tomato crop disease classification using pre-trained deep learning algorithm

The wide scale prevalence of diseases in tomato crop affects the production quality and quantity. In order to counteract the problem early diagnosis of diseases using a fast reliable nondestructive method will benefit the farmers. In this study images of tomato leaves (6 diseases and a healthy class...

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Vydáno v:Procedia computer science Ročník 133; s. 1040 - 1047
Hlavní autoři: Rangarajan, Aravind Krishnaswamy, Purushothaman, Raja, Ramesh, Aniirudh
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 2018
Témata:
AlexNet
bias learning rate
deep learning
minibatch size
Tomato disease recoginition
VGG16 net
weight learning rate
deep learning
minibatch size
bias learning rate
weight learning rate
Tomato disease recoginition
AlexNet
VGG16 net
ISSN:1877-0509, 1877-0509
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Shrnutí:The wide scale prevalence of diseases in tomato crop affects the production quality and quantity. In order to counteract the problem early diagnosis of diseases using a fast reliable nondestructive method will benefit the farmers. In this study images of tomato leaves (6 diseases and a healthy class) obtained from PlantVillage dataset is provided as input to two deep learning based architectures namely AlexNet and VGG16 net. The role of number of images and significance of hyperparameters namely minibatch size, weight and bias learning rate in the classification accuracy and execution time have been analyzed.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2018.07.070