# An Improved Deep Residual Convolutional Neural Network for Plant Leaf Disease Detection

Authors: Arun Pandian J.; Kanchanadevi K.; N.R. Rajalakshmi; G.Arulkumaran
Journal: Computational Intelligence and Neuroscience
 (2022)

---

## Abstract

In this research, we proposed a novel deep residual convolutional neural network with 197 layers (ResNet197) for the detection of various plant leaf diseases. Six blocks of layers were used to develop ResNet197. ResNet197 was trained and tested using a combined plant leaf disease image dataset. Scaling, cropping, flipping, padding, rotation, affine transformation, saturation, and hue transformation techniques were used to create the augmentation data of the plant leaf disease image dataset. The dataset consisted of 103 diseased and healthy image classes of 22 plants and 154,500 images of healthy and diseased plant leaves. The evolutionary search technique was used to optimise the layers and hyperparameter values of ResNet197. ResNet197 was trained on the combined plant leaf disease image dataset using a graphics processing unit (GPU) environment for 1000 epochs. It produced a 99.58 percentage average classification accuracy on the test dataset. The experimental results were superior to existing ResNet architectures and recent transfer learning techniques.


---

## Body

## 1. Introduction

Agriculture is an important sector for many countries and provides raw resources for many businesses [1]. Diseases, insects, and nutrient deficiencies are the most common threats to the growth of crops. Disease diagnosis and treatment, pest management, and fertiliser application are performing an important role in decreasing yield loss [2]. The traditional process for disease detection is not feasible for all crop fields and farmers. Finding suitable human experts for disease diagnosis and treatment requires more time and money. An artificial intelligence approach is required for the automatic detection of plant diseases to overcome difficulties in the traditional approach [3].Deep learning is a type of artificial intelligence technique that extends from artificial neural networks [4]. The deep learning technique imitates how humans make intelligent decisions through acquiring knowledge [5]. It is increasingly being used in various industrial applications for decision support to increase productivity, reduce errors, and reduce costs. Deep learning techniques perform better than traditional artificial intelligence techniques in terms of decision accuracy and reliability [6]. Deep convolutional neural networks (DCNN) are a class of supervised deep learning techniques. The DCNNs are most successful in image classification and object detection tasks [7]. A large volume of data is required to train the DCNN models for use in various domains [8]. The data augmentation technique was introduced to increase the amount of training data without data collection for better training performance of DCNN models [9]. Training the DCNN model needs huge computation and storage.The graphics processing units (GPUs) are commonly used to train models more efficiently [10].The major contributions of this research are as follows:(i)
The leaf diseases of twenty-two different plants were diagnosed using novel deep residual convolutional neural networks.(ii)
A novel deep residual convolutional neural network with 197 layers (ResNet197) was designed and developed for leaf disease detection.(iii)
In addition, the evolutionary searching technique was used as a tuning technique to discover the suitable number of layers and hyperparameter values for the proposed ResNet197 model.(iv)
ResNet197 was trained on the plant leaf disease dataset up to 1000 epochs in a GPU environment.(v)
The classification performance of trained Resnet197 was calculated on the test dataset using standard performance metrics.(vi)
This research also proposed a model that could be used by farmers for diagnosing various plant diseases from a camera-captured image without any prior knowledge of plant diseases.(vii)
Performance comparison of the proposed model and recent transfer learning techniques showed that it is superior to other transfer learning methods in leaf disease detection tasks.The research article is organized as follows: In Section2, we provided a brief study about plant leaf disease detection using various machine learning and deep learning approaches. In Section 3, the data preparation, the ResNet197 architecture, and the corresponding training process were presented. In Section 4, we experimentally compared the performance of ResNet197 with recent deep transfer learning techniques and discussed the outcomes. Finally, we concluded the research by summarizing the outcomes and future directions in Section 5.


## 2. Literature Survey

The recent developments in artificial intelligence techniques support efficient identification of numerous diseases and pest attacks in precision farming. This survey discusses the modern artificial intelligence approaches to plant leaf disease detection. In [11], the authors compared the performance of standard machine learning and deep transfer learning techniques in plant leaf disease detection. They identified that the performance of the deep learning techniques was better than that of machine learning techniques in leaf disease detections. The VGG-16 net produced a classification accuracy of 89.5% on plant leaf disease detection, which is higher than that of other machine learning and deep learning techniques.The authors in [12] proposed a DCNN with nineteen convolutional layers for the classification of two major apple leaf diseases. The classification accuracy of the model on test data for apple disease detection was 99.2%. The model produced a better performance than support vector machine (SVM), k-nearest neighbour (K-NN), random forest (RF), and logistic regression (LR) techniques. On the other hand, the authors in [13] used a capsule network with a bidirectional long short-term memory model for the classification of apple leaf diseases. The classification performance of their model was better than that of the standard machine learning techniques. Also, the ensemble subspace discriminant analysis classifier with a mask region-based convolutional neural network was used to detect the infected regions of apple crop leaves by the authors in [14]. They achieved a classification accuracy of 96.6% on the tomato leaf disease dataset using their model.The authors in [15] used a dense convolutional neural network (DenseNet) and multilayer perceptron for detecting bacterial leaf blight, brown spot, and leaf smut diseases in rice crops. The maximum classification accuracy of the rice disease detection model was 97.68%. In [16], the authors proposed a rice crop disease detection model using an attention-based neural network and MobileNet. The rice crop disease detection model has classified the diseases with an accuracy of 94.65% on the test data. The authors in [17] developed a VGG16Net-based rice and wheat leaf disease detection model. The rice disease and wheat disease classification accuracy of the model was 97.22% and 98.75%, respectively. They compared the performance of the model in rice and wheat disease detection with that of other transfer learning techniques.Likewise, the authors in [18] designed a simple DCNN to diagnose tomato crop diseases, and they achieved a 98.49% of classification accuracy on testing data. In [19], the authors developed a tomato leaf disease detection model using the DenseNet121 transfer learning technique. They used the conditional generative adversarial network (C-GAN) for creating augmented data for balancing training datasets. The DenseNet121 model achieved an accuracy of 97.11% on tomato disease classification. In [20], the authors proposed a custom convolutional neural network for plant disease classification. The custom network achieved a classification accuracy of 94.5% on the test dataset. The authors in [21] developed an EfficientNet pretrained model for detecting peach plant diseases with an accuracy of 96.6% on the test data. The improved MobileNet model was proposed for cassava disease detection by the authors in [22]. Also, they achieved better performance than other machine learning and transfer learning techniques in cassava leaf disease detection using MobileNet.Similarly, the authors in [23] proposed a cucumber leaf disease severity classification model using U-Net architecture and achieved a testing accuracy of 92.85% on the cucumber leaf disease dataset. In [24], the authors proposed a pumpkin powdery mildew disease identification technique using principal component analysis (PCA) and SVM. The model detected the pumpkin powdery mildew disease on the pumpkin leaf with an accuracy of 97.3%, and the authors in [25] developed a cotton lesion detection model using the Resnet50 transfer learning technique. The model produced a classification accuracy of 89.2%, which is better than that of GoogleNet and standard machine learning techniques. Moreover, the authors in [26] developed a super-resolution generative adversarial network (SR-GAN) as an augmentation technique for balancing the data numbers in classes of the dataset.Also, they identified that the augmented dataset increases the classification accuracy of deep learning models. A custom DCNN model with nine layers was proposed to identify the diseases of thirteen different species by the authors in [27]. The model classified 96% of the images accurately in the test dataset. Recently, the authors in [28] proposed a custom DCNN model for the detection of plant leaf diseases on the standard dataset and field-collected images. The custom DCNN model achieved an average testing accuracy of 99.84% on the test dataset. The authors in [29] proposed a DenseNet architecture for the diagnosis of the twenty-seven different classes of diseases from six crops. The validation and testing accuracy of the classification model was 99.58% and 99.19%, respectively. The authors in [30] proposed a custom network for detecting pearl millet diseases. They achieved an accuracy of 98.78%, which is higher than that of the transfer learning techniques. The authors in [31] studied various plant leaf disease detection techniques using a deep convolutional neural network. Also, they discussed several datasets, which are available for plant leaf disease detection model development.The literature survey recognized that residual and dense convolutional neural networks performed better than other transfer learning techniques in plant disease detection [32]. The residual and dense network created deeper connections between the layers than simple convolutional neural networks. The residual and dense networks avoided the vanishing-gradient problem and minimized the number of training parameters. The performance of the residual and dense network in existing plant leaf disease detection applications provided the motivation to propose a residual convolutional neural network for plant leaf disease detection. Most of the state-of-the-art transfer learning techniques were trained on the ImageNet dataset. The transfer learning techniques may cause negative transfer and overfitting problems while using the architecture and weights of the pretrained models for new applications.In addition, the literature survey shows the significance of data augmentation and hyperparameter tuning for the classification algorithms. A novel residual convolutional neural network was proposed in this research with improved performance than existing residual networks and other transfer learning techniques for detecting plant diseases. The subsequent section discussed the architecture and training process of the proposed plant disease detection model.


## 3. Materials and Methods

The proposed plant leaf disease detection model implementation steps are classified into two stages. Implementation of the proposed ResNet197 model started with the data preparation. The data preparation phase concentrates on data collection, augmentation, and data preprocessing. The model training phase includes ResNet197 design, fine-tuning, and training processes. The following subsections describe each of the implementation phases in detail.


### 3.1. Data Preparation

Implementation of a deep learning algorithm starts with the data preparation phase. It includes data collection, data augmentation, and preprocessing stages. The proposed dataset was collected from various standard leaf disease detection datasets [27, 32]. There are 103 classes of healthy and diseased images in the proposed dataset. Table 1 illustrates the list of diseased and healthy plant leaf classes in the proposed dataset.Table 1
List of classes in the proposed dataset.
IDClass name1Aloe_Vera_Healthy2Aloe_Vera_Leaf_Rot3Aloe_Vera_Leaf_Rust4Apple_Black_Rot5Apple_Healthy6Apple_Leaf_Rust7Apple_Leaf_Scab8Banana_Bacterial_Wilt9Banana_Black_Sigatoka10Banana_Healthy11Banana_Mosaic12Carrot_ Alternaria Leaf Blight13Carrot_ Cercospora Leaf Blight14Carrot_ Sclerotinia Rot15Carrot_ Healthy16Cherry_Healthy17Cherry_Leaf_Rust18Cherry_leaf_Spot19Cherry_Powdery_Mildew20Citrus_Black_Spot21Citrus_Canker22Citrus_Greening23Citrus_Healthy24Citrus_Melanose25Coffee_Cercospora_Leaf_Spot26Coffee_Healthy27Coffee_Leaf_Rust28Coffee_Red_Spider_Mite29Corn_Common_Rust30Corn_Healthy31Corn_Leaf_Spot32Corn_Northern_Leaf_Blight33Corn_Southern_Leaf_Blight34Eggplant_Cercospora_Leaf_Spot35Eggplant_Healthy36Eggplant_Powdery_Mildew37Eggplant_Verticillium_Wilt38Grape_Black_Measles39Grape_Black_Rot40Grape_Healthy41Grape_Leaf_Blight42Groundnut_Early_Leaf_Spot43Groundnut_Healthy44Groundnut_Late_Leaf_Spot45Groundnut_Leaf_Rust46Groundnut_Web_Blotch47Guava_Algal_Leaf_Spot48Guava_Healthy49Guava_Leaf_Rust50Guava_Pseudocercospora_Leaf_Spot51Paddy_Bacterial_Blight52Paddy_Brown_Spot53Paddy_Cercospora_Leaf_Spot54Paddy_Healthy55Paddy_Hispa56Paddy_Leaf_Blast57Paddy_Leaf_Streak58Peach_Bacterial_Spot59Peach_Healthy60Peach_Leaf_Curl61Peach_Leaf_Rust62Pepper_Cercospora_Leaf_Spot63Pepper_Fusarium_Wilt64Pepper_Gray_Leaf_Spot65Pepper_Healthy66Potato_Early_Blight67Potato_Healthy68Potato_Late_Blight69Potato_Leaf_Roll70Potato_Potato_Virus_Y71Strawberry_Angular_Leaf_Spot72Strawberry_Healthy73Strawberry_Leaf_Scorch74Strawberry_Leaf_Scorch75Sugarcane_Eye Spot76Sugarcane_Red_Rot77Sugarcane_Pineapple_Disease78Sugarcane_Leaf_Scald79Sugarcane_Mosaic_Virus80Sugarcane_Healthy81Tea_Healthy82Tea_Leaf_Blight83Tea_Red_Leaf_Spot84Tea_Red_Scab85Tomato_Bacterial_Spot86Tomato_Early_Blight87Tomato_Healthy88Tomato_Late_Blight89Tomato_Leaf_Mold90Tomato_Leaf_Spot91Tomato_Mosaic_Virus92Tomato_Spider_Mite93Tomato_Target_Spot94Tomato_Yellow_Leaf_Curl_Virus95Turmeric_Bacterial_Wilt96Turmeric_Healthy97Turmeric_Leaf_Blotch98Turmeric_Leaf_Spot99Wheat_Bacterial_Leaf_Streak100Wheat_Healthy101Wheat_Leaf_Rust102Wheat_Powdery_Mildew103Wheat_Tan_SpotSome classes in the original dataset have fewer samples. On the other hand, some classes have more images. For example, the tea leaf blight disease class has only 214 images, but the tomato yellow leaf curl virus disease classes have 3209 samples. The number of samples should be equal in each class to increase the performance of the classification algorithms. Data augmentation techniques were used in this research to increase the number of samples without collecting new data. The scaling, cropping, flipping, padding, rotation, affine transformation, saturation, and hue transformation techniques were used to produce augmented images on the dataset. The data augmentation process equalized the number of images in each class to become 1500. Figure1 shows the sample augmented images on the plant leaf disease dataset using data augmentation techniques.Figure 1
Sample augmented images from the plant leaf disease dataset.
(a)(b)(c)(d)After the augmentation step, the dataset was split for the training, validation, and testing process. The images in the dataset were shuffled and randomly selected for training, validation, and testing. The number of images in the training, validation, and the testing dataset is illustrated in Table2.Table 2
Size of training, validation, and the test dataset.
Dataset nameNumber of imagesNumber of images in each classTraining set133,9001,300Validation set10,300100Testing set10,300100The training process of the proposed ResNet197 model was discussed in subsequent sections. The training process includes model design, fine-tuning, and model training steps.


### 3.2. Model Training

This section discussed the construction and training process of the proposed ResNet197 model for leaf disease detection. Six blocks of layers were used in the proposed model. Also, the proposed model was called a deep residual convolutional neural network with 197 layers (ResNet197). The proposed ResNet197 model includes 197 layers in total. The layered architecture of the proposed ResNet197 model is shown in Figure2.Figure 2
Layered architecture of the proposed ResNet197 model.The input image size of the proposed ResNet197 model was 224 × 224 × 3 pixels. The first block consisted of one convolutional (Conv) layer. The first convolutional (Conv) layer produced 112 × 112 sized outputs using a 7 × 7 Conv function with a stride of 2. The convoluted data were forwarded to the second block. The second block consisted of one max-pooling layer and three Conv layers. The three Conv layers were used three times in sequence. The output of block 1 was forwarded to the max-pooling layer, which uses a 3 × 3 max-pooling function with a stride of 2. The output of the pooling layer was sent as an input to three Conv layers. The second layer block produced an output sized 56 × 56. The output of the second block was forwarded to the third block. The third layer block consisted of three Conv layers sized 1 × 1, 3 × 3, and 1 × 1 filter size. The Conv layers were used 12 times in a sequence. The third block produced an output sized 28 × 28. After the third block layer, the data were forwarded to the fourth layer block. Three Conv layers were available in the fourth block. The three Conv layers were used 47 times in a sequence. The fourth Conv layer produced the output data with a size of 14 × 14. The fifth layer block was introduced after the fourth block. Three Conv layers were used in the fifth block three times in a sequence. The fifth block produced the 7 × 7 sized output. The output of the fifth block was forwarded to the sixth and final block of the model. The sixth block consisted of an average pooling layer and one fully connected (dense) layer with 103 neurons. The softmax activation function was used in this layer for classifying the input leaf images.The suitable batch size, loss function, optimizer function, and learning rate of the proposed ResNet197 model were identified using the evolutionary search technique. Table3 displays the optimised hyperparameter value of the proposed ResNet197 model.Table 3
Optimized hyperparameter values of the ResNet197 model.
HyperparameterOptimized valueBatch sizes64LossCategorical cross entropyOptimizerAdamLearning rate0.001The proposed ResNet197 model was trained on the plant leaf disease dataset using the optimised hyperparameters up to 1000 training epochs. The training progress and validation progress of the proposed ResNet197 model are shown in Figure3.Figure 3
(a) Training and (b) validation results of ResNet197.
(a)(b)There was no significant change in the validation performance of ResNet197 after reaching 1000 epochs. So, the training process of the model was stopped with 1000 epochs in the GPU environment. The proposed ResNet197 model was deployed after the successful completion of the training process. The testing process of the proposed ResNet197 model was discussed in the upcoming section.


## 3.1. Data Preparation

Implementation of a deep learning algorithm starts with the data preparation phase. It includes data collection, data augmentation, and preprocessing stages. The proposed dataset was collected from various standard leaf disease detection datasets [27, 32]. There are 103 classes of healthy and diseased images in the proposed dataset. Table 1 illustrates the list of diseased and healthy plant leaf classes in the proposed dataset.Table 1
List of classes in the proposed dataset.
IDClass name1Aloe_Vera_Healthy2Aloe_Vera_Leaf_Rot3Aloe_Vera_Leaf_Rust4Apple_Black_Rot5Apple_Healthy6Apple_Leaf_Rust7Apple_Leaf_Scab8Banana_Bacterial_Wilt9Banana_Black_Sigatoka10Banana_Healthy11Banana_Mosaic12Carrot_ Alternaria Leaf Blight13Carrot_ Cercospora Leaf Blight14Carrot_ Sclerotinia Rot15Carrot_ Healthy16Cherry_Healthy17Cherry_Leaf_Rust18Cherry_leaf_Spot19Cherry_Powdery_Mildew20Citrus_Black_Spot21Citrus_Canker22Citrus_Greening23Citrus_Healthy24Citrus_Melanose25Coffee_Cercospora_Leaf_Spot26Coffee_Healthy27Coffee_Leaf_Rust28Coffee_Red_Spider_Mite29Corn_Common_Rust30Corn_Healthy31Corn_Leaf_Spot32Corn_Northern_Leaf_Blight33Corn_Southern_Leaf_Blight34Eggplant_Cercospora_Leaf_Spot35Eggplant_Healthy36Eggplant_Powdery_Mildew37Eggplant_Verticillium_Wilt38Grape_Black_Measles39Grape_Black_Rot40Grape_Healthy41Grape_Leaf_Blight42Groundnut_Early_Leaf_Spot43Groundnut_Healthy44Groundnut_Late_Leaf_Spot45Groundnut_Leaf_Rust46Groundnut_Web_Blotch47Guava_Algal_Leaf_Spot48Guava_Healthy49Guava_Leaf_Rust50Guava_Pseudocercospora_Leaf_Spot51Paddy_Bacterial_Blight52Paddy_Brown_Spot53Paddy_Cercospora_Leaf_Spot54Paddy_Healthy55Paddy_Hispa56Paddy_Leaf_Blast57Paddy_Leaf_Streak58Peach_Bacterial_Spot59Peach_Healthy60Peach_Leaf_Curl61Peach_Leaf_Rust62Pepper_Cercospora_Leaf_Spot63Pepper_Fusarium_Wilt64Pepper_Gray_Leaf_Spot65Pepper_Healthy66Potato_Early_Blight67Potato_Healthy68Potato_Late_Blight69Potato_Leaf_Roll70Potato_Potato_Virus_Y71Strawberry_Angular_Leaf_Spot72Strawberry_Healthy73Strawberry_Leaf_Scorch74Strawberry_Leaf_Scorch75Sugarcane_Eye Spot76Sugarcane_Red_Rot77Sugarcane_Pineapple_Disease78Sugarcane_Leaf_Scald79Sugarcane_Mosaic_Virus80Sugarcane_Healthy81Tea_Healthy82Tea_Leaf_Blight83Tea_Red_Leaf_Spot84Tea_Red_Scab85Tomato_Bacterial_Spot86Tomato_Early_Blight87Tomato_Healthy88Tomato_Late_Blight89Tomato_Leaf_Mold90Tomato_Leaf_Spot91Tomato_Mosaic_Virus92Tomato_Spider_Mite93Tomato_Target_Spot94Tomato_Yellow_Leaf_Curl_Virus95Turmeric_Bacterial_Wilt96Turmeric_Healthy97Turmeric_Leaf_Blotch98Turmeric_Leaf_Spot99Wheat_Bacterial_Leaf_Streak100Wheat_Healthy101Wheat_Leaf_Rust102Wheat_Powdery_Mildew103Wheat_Tan_SpotSome classes in the original dataset have fewer samples. On the other hand, some classes have more images. For example, the tea leaf blight disease class has only 214 images, but the tomato yellow leaf curl virus disease classes have 3209 samples. The number of samples should be equal in each class to increase the performance of the classification algorithms. Data augmentation techniques were used in this research to increase the number of samples without collecting new data. The scaling, cropping, flipping, padding, rotation, affine transformation, saturation, and hue transformation techniques were used to produce augmented images on the dataset. The data augmentation process equalized the number of images in each class to become 1500. Figure1 shows the sample augmented images on the plant leaf disease dataset using data augmentation techniques.Figure 1
Sample augmented images from the plant leaf disease dataset.
(a)(b)(c)(d)After the augmentation step, the dataset was split for the training, validation, and testing process. The images in the dataset were shuffled and randomly selected for training, validation, and testing. The number of images in the training, validation, and the testing dataset is illustrated in Table2.Table 2
Size of training, validation, and the test dataset.
Dataset nameNumber of imagesNumber of images in each classTraining set133,9001,300Validation set10,300100Testing set10,300100The training process of the proposed ResNet197 model was discussed in subsequent sections. The training process includes model design, fine-tuning, and model training steps.


## 3.2. Model Training

This section discussed the construction and training process of the proposed ResNet197 model for leaf disease detection. Six blocks of layers were used in the proposed model. Also, the proposed model was called a deep residual convolutional neural network with 197 layers (ResNet197). The proposed ResNet197 model includes 197 layers in total. The layered architecture of the proposed ResNet197 model is shown in Figure2.Figure 2
Layered architecture of the proposed ResNet197 model.The input image size of the proposed ResNet197 model was 224 × 224 × 3 pixels. The first block consisted of one convolutional (Conv) layer. The first convolutional (Conv) layer produced 112 × 112 sized outputs using a 7 × 7 Conv function with a stride of 2. The convoluted data were forwarded to the second block. The second block consisted of one max-pooling layer and three Conv layers. The three Conv layers were used three times in sequence. The output of block 1 was forwarded to the max-pooling layer, which uses a 3 × 3 max-pooling function with a stride of 2. The output of the pooling layer was sent as an input to three Conv layers. The second layer block produced an output sized 56 × 56. The output of the second block was forwarded to the third block. The third layer block consisted of three Conv layers sized 1 × 1, 3 × 3, and 1 × 1 filter size. The Conv layers were used 12 times in a sequence. The third block produced an output sized 28 × 28. After the third block layer, the data were forwarded to the fourth layer block. Three Conv layers were available in the fourth block. The three Conv layers were used 47 times in a sequence. The fourth Conv layer produced the output data with a size of 14 × 14. The fifth layer block was introduced after the fourth block. Three Conv layers were used in the fifth block three times in a sequence. The fifth block produced the 7 × 7 sized output. The output of the fifth block was forwarded to the sixth and final block of the model. The sixth block consisted of an average pooling layer and one fully connected (dense) layer with 103 neurons. The softmax activation function was used in this layer for classifying the input leaf images.The suitable batch size, loss function, optimizer function, and learning rate of the proposed ResNet197 model were identified using the evolutionary search technique. Table3 displays the optimised hyperparameter value of the proposed ResNet197 model.Table 3
Optimized hyperparameter values of the ResNet197 model.
HyperparameterOptimized valueBatch sizes64LossCategorical cross entropyOptimizerAdamLearning rate0.001The proposed ResNet197 model was trained on the plant leaf disease dataset using the optimised hyperparameters up to 1000 training epochs. The training progress and validation progress of the proposed ResNet197 model are shown in Figure3.Figure 3
(a) Training and (b) validation results of ResNet197.
(a)(b)There was no significant change in the validation performance of ResNet197 after reaching 1000 epochs. So, the training process of the model was stopped with 1000 epochs in the GPU environment. The proposed ResNet197 model was deployed after the successful completion of the training process. The testing process of the proposed ResNet197 model was discussed in the upcoming section.


## 4. Results and Discussions

This section discussed the performance of the proposed ResNet197 model in plant leaf disease detection. Also, it compares the ResNet197 model with other versions of ResNet models and state-of-the-art transfer learning techniques using standard performance metrics. VGG-19 Net, ResNet-152, InceptionV3 Net, Mobile Net, and DenseNet201 are the state-of-the-art transfer learning techniques that are used for the performance comparison.The area under the curve-receiver operating characteristics (AUC-ROC) curve is the most popular metric for estimating the performance of classification techniques. The ROC of classification techniques for a specific class is calculated using the true positive rate (TPR) and false positive rate (FPR) values of the class on the test data. The TPR represents the number of correctly classified positive samples in the test data [27]. Similarly, the FPR represents the number of incorrect positive predictions among negative samples in the test data. The TPR and FPR values are used to plot the ROC curve and calculate the AUC value of the classification model for a specific class. The x-axis and y-axis of the graph represent the scale of TPR and FPR, respectively. The AUC-ROC curves of proposed and existing models on two randomly selected classes are shown in Figure 4. The AUC values of ResNet197 on the sample classes were higher than those of other standard transfer learning techniques. The AUC value of the proposed ResNet197 model on the sample classes is between 0.98 and 1.0; it shows the performance excellence of ResNet197 on plant leaf disease classification.Figure 4
Sample AUC curves of ResNet197.
(a)(b)Classification accuracy, precision, recall, and F1-score are the standard measures to assess the overall performance of the classification techniques [27]. The performance of ResNet197 and most recent transfer learning techniques was compared using the abovementioned metrics. The performance comparison of the proposed ResNet197 model and transfer learning techniques is illustrated in Figure 5.Figure 5
Performance comparison of ResNet architectures.Also, Table4 illustrates the performance comparison of the proposed ResNet197 model and other ResNet models.Table 4
Performance comparison of ResNet models.
ModelAccuracyPrecisionSensitivityF1-scoreSpecificityResNet5087.6585.9486.9286.4385.68ResNet10190.3491.1490.8390.9891.23ResNet15294.7293.6893.7493.792.87Proposed ResNet19799.5899.3699.4299.3999.27In addition, the classification performance of the proposed ResNet197 model was compared with that of existing state-of-the-art transfer learning techniques. The proposed model achieved an average classification accuracy of 99.58% on the test data. The performance comparison of the proposed ResNet197 model and transfer learning techniques using standard performance metrics is illustrated in Figure6.Figure 6
Performance comparison of ResNet197 and transfer learning techniques.Also, Table5 shows the performance score of the proposed and existing models on the plant leaf disease dataset. The comparison result shows that the proposed model achieved better classification accuracy, precision, sensitivity, F1-score, and specificity than existing transfer learning techniques.Table 5
Performance comparison of ResNet197 and transfer learning techniques.
ModelAccuracyPrecisionSensitivityF1-scoreSpecificityVGG19Net90.3591.4690.3590.990.23ResNet15294.7293.6893.7493.7192.87InceptionV3Net96.4395.8693.8594.8595.64MobileNet89.6290.3589.2489.7989.15DenseNet20195.7393.8794.3494.195.36Proposed ResNet19799.5899.3699.4299.3999.27The inceptionV3 network showed better performance among the transfer learning techniques in plant leaf disease detection. The average classification accuracy of the proposed ResNet197 model on the test dataset was 99.58%, which is 3.15% higher than that of the inceptionV3 network. The average classification accuracy, average precision, average recall, and average F1-score of the proposed ResNet197 model were superior to those of the other transfer learning techniques. The AUC values and performance metric outcomes of the proposed ResNet197 model showed that the performance and reliability of the proposed ResNet197 model were superior to those of advanced transfer learning techniques in plant leaf disease detection.


## 5. Conclusions and Future Works

Automatic plant disease detection is a crucial process in precision agriculture. This research study proposed a novel deep residual convolutional neural network with 197 layers (ResNet197) for the detection of common leaf diseases in 22 different plants. Some standard datasets and a few recent image augmentation techniques were used to prepare the proposed dataset for the ResNet197 training. Scaling, cropping, flipping, padding, rotation, affine transformation, saturation, and hue transformation techniques were used to produce the augmented images. The proposed dataset consisted of 133,900 images of 103 diseased and healthy classes. The evolutionary searching technique was used to identify suitable values for the hyperparameters of the proposed ResNet197 model in plant leaf disease detection. The training process of ResNet197 and existing transfer learning models was performed on GPU-enabled workstations up to 1000 training epochs. The classification accuracy, precision, sensitivity, F1-score and specificity of the proposed ResNet197 model were 99.58%, 99.36%, 99.42%, 99.39%, and 99.27%, respectively. The performance results of the proposed ResNet197 model were superior to those of the transfer learning techniques such as VGG19Net, ResNet152, InceptionV3Net, MobileNet, and DenseNet201. Also, AUC curves demonstrated the performance and reliability of ResNet197 in plant leaf disease detection. This research concludes that the deep residual convolutional neural networks with the optimised number of layer blocks perform better than traditional deep learning techniques. This research study also identified that the performance of the classification algorithms can be improved by data augmentation and hyperparameter optimization techniques. The limitation of ResNet197 is its computational density. It requires significantly more FLOPS than similar models such as VGG19Net and MobileNet. The development of a novel deep convolutional neural network using residually connected networks for the diagnosis of a number of plant diseases is a future direction of the research study.



---

*Source: 5102290-2022-09-14.xml*