ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network
By M. Agarwal, Abhishek Singh, S. Arjaria, A. Sinha, S. Gupta. Procedia Computer Science, 2020
552 citations recorded by the source corpus.
Abstract Tomato is the most popular crop in the world and in every kitchen, it is found in different forms irrespective of the cuisine. After potato and sweet potato, it is the crop which is cultivated worldwide. India ranked 2 in the production of tomato. However, the quality and quantity of tomato crop goes down due to the various kinds of diseases. So, to detect the disease a deep learning-based approach is discussed in the article. For the disease detection and classification, a Convolution Neural Network based approach is applied. In this model, there are 3 convolution and 3 max pooling layers followed by 2 fully connected layer. The experimental results shows the efficacy of the proposed model over pre-trained model i.e. VGG16, InceptionV3 and MobileNet. The classification accuracy varies from 76% to 100% with respect to classes and average accuracy of the proposed model is 91.2% for the 9 disease and 1 healthy class.
Subfields: Analytical Chemistry, Plant Science
View Paper: https://www.semanticscholar.org/paper/bece229080a0dd29ea684dd74c2e0c03a0263760
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In Analytical Chemistry
In Plant Science
Citations in the corpus
- An advanced deep learning models-based plant disease detection: A review of recent research (2023, background): “CNNs are wellsuited for leaf disease detection, thanks to their architecture consisting of up-sampling, down-sampling and learnable layers (Agarwal et al., 2020).”
- Plant disease detection and classification techniques: a comparative study of the performances (2024, methodology): “The authors of [84] have developed a DL-based technique to detect the disease.”
- Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques (2021, methodology): “Agarwal et al. [23] proposed the Tomato Leaf Disease Detection (ToLeD) model, a CNN-based architecture for the classification of 10 diseases from tomato leaf photographs, with a 91.2% accuracy rate.”