Can Deep Learning Identify Tomato Leaf Disease?

Zhang, Keke; Wu, Qiufeng; Liu, Anwang; Meng, Xiangyan

doi:10.1155/2018/6710865

Cited by 148 publications

(47 citation statements)

References 36 publications

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“…The output goes into a series of two fully-connected layers, in which the second fully-connected layer feed into a softmax classifier. In order to prevent overfitting in the fully-connected layers, the authors employed a regularization method called "dropout" with a ratio of 0.5 [32]. Another feature of the AlexNet model is the use of Rectified Linear Unit (ReLU), which is applied to each of the first seven layers.…”

Section: Alexnetmentioning

confidence: 99%

Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant Diseases

et al. 2020

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Tomato plants are highly affected by diverse diseases. A timely and accurate diagnosis plays an important role to prevent the quality of crops. Recently, deep learning (DL), specifically convolutional neural networks (CNNs), have achieved extraordinary results in many applications, including the classification of plant diseases. This work focused on fine-tuning based on the comparison of the state-of-the-art architectures: AlexNet, GoogleNet, Inception V3, Residual Network (ResNet) 18, and ResNet 50. An evaluation of the comparison was finally performed. The dataset used for the experiments is contained by nine different classes of tomato diseases and a healthy class from PlantVillage. The models were evaluated through a multiclass statistical analysis based on accuracy, precision, sensitivity, specificity, F-Score, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results present significant values obtained by the GoogleNet technique, with 99.72% of AUC and 99.12% of sensitivity. It is possible to conclude that this significantly success rate makes the GoogleNet model a useful tool for farmers in helping to identify and protect tomatoes from the diseases mentioned.

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Section: Alexnetmentioning

confidence: 99%

Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant Diseases

et al. 2020

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“…Mohanty et al (2016) visualized the top activated feature maps at the output of early convolution layers (Figure 7A). Zhang K. et al (2018) used t-distributed Stochastic Neighbor Embedding (t-SNE) to visualize the features of their final fully connected layer and to evaluate the distance between their classes (Figure 7B). The insight brought by all these visualization methods can help us understand the behavior of trained models while suggesting new improvements.…”

Section: Understanding the Trained Modelsmentioning

confidence: 99%

“…Visualization examples found in the corpus (A) Activations in the first convolution layer visualization (Mohanty et al, 2016). (B) T-distributed Stochastic Neighbor Embedding on the final fully connected layer (Zhang K. et al, 2018). …”

Section: Understanding the Trained Modelsmentioning

confidence: 99%

Convolutional Neural Networks for the Automatic Identification of Plant Diseases

et al. 2019

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Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Since 2016, many applications for the automatic identification of crop diseases have been developed. These applications could serve as a basis for the development of expertise assistance or automatic screening tools. Such tools could contribute to more sustainable agricultural practices and greater food production security. To assess the potential of these networks for such applications, we survey 19 studies that relied on CNNs to automatically identify crop diseases. We describe their profiles, their main implementation aspects and their performance. Our survey allows us to identify the major issues and shortcomings of works in this research area. We also provide guidelines to improve the use of CNNs in operational contexts as well as some directions for future research.

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“…Similarly, advanced imaging techniques including hyperspectral [ 3 , 4 , 5 , 6 , 7 ] and multispectral imaging [ 8 ] have also been used for plant/leaf disease identification. However, after the evolution of deep learning (DL), many state-of-the-art architectures, including AlexNet [ 9 , 10 , 11 , 12 , 13 , 14 ], Visual Geometry Group (VGG) [ 10 , 11 , 13 , 15 , 16 ], DenseNet [ 16 ], Inception-v4 [ 16 ] and ResNet [ 11 , 13 , 14 , 16 ], got promising results for the classification of plant disease. In this regard, several studies proved the significance of deep learning-based methods as compared to the traditional ML techniques.…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers

Saleem

Potgieter

Arif

2020

Plants

117

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Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes.

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Can Deep Learning Identify Tomato Leaf Disease?

Cited by 148 publications

References 36 publications

Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant Diseases

Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant Diseases

Convolutional Neural Networks for the Automatic Identification of Plant Diseases

Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers

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