Tomato crop disease classification using pre-trained deep learning algorithm
By A. K. Rangarajan, Raja Purushothaman, Aniirudh Ramesh. 2018
510 citations recorded by the source corpus.
Abstract 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.
Subfields: Analytical Chemistry, Plant Science
View Paper: https://www.semanticscholar.org/paper/186d704c7f0cd96182edeb36b6c72727f8cede22
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In Analytical Chemistry
- Johan Potgieter
- Kemal Akyol
- Emine Uçar
- Muhammad Hammad Saleem
- Khalid Mahmood Arif
- Murat Uçar
- Ümit Atila
In Plant Science
- Johan Potgieter
- Kemal Akyol
- Emine Uçar
- Muhammad Hammad Saleem
- Khalid Mahmood Arif
- Murat Uçar
- Ümit Atila
Citations in the corpus
- Plant leaf disease classification using EfficientNet deep learning model (2021, background): “In PlantVillage dataset, most of the studies (Amara et al., 2017; Brahimi et al., 2017; Cruz et al., 2017; Durmus et al., 2017; Rangarajan et al., 2018; Sibiya and Sumbwanyambe, 2019; Wallelign et al., 2018; Yamamoto et al., 2017; Zhang et al., 2018b) classify the diseases of a single plant species.”
- Plant Disease Detection and Classification by Deep Learning (2019, methodology): “Moreover in [64], six tomato plant diseases were classified by using AlexNet and VGG-16 DL architectures, and a detailed comparison was provided with the help of classification accuracy.”
- Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, unknown): “Studies that investigate only one type of crop in particular highlight those that address the phytopathology of Tomato crop with 18 investigations (S3, S9, S27, S44, S48, S55, S58, S61, S67, S70, S75, S78, S86, S93, S99, S102, S105, S108).”