# A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### Recent Developments in Plant Leaf Disease Identification and Classification

## 3. Comparative Analysis

#### 3.1. Pre-Processing Techniques

#### 3.1.1. Resizing

#### 3.1.2. Augmentation

#### 3.1.3. Normalization and Standardization

_{min}and new

_{max}are 0 and 1 respectively, x is the value of an attribute, max is the maximum value of the given attribute, and min is the minimum value of the given attribute. This technique gives stable gradients. However, it lacks handling outliers. Min–Max normalization scales data into a range from 0 to 1 as given in Equation (1). Here, the values of new

_{min}and new

_{max}are 0 and 1 respectively, x is the value of an attribute, max is the maximum value of the given attribute, and min is the minimum value of the given attribute. This technique gives stable gradients. Nevertheless, it lacks handling outliers.

#### 3.1.4. Annotation

#### 3.1.5. Outlier Rejection

#### 3.1.6. Denoising

#### 3.2. Convolutional Neural Networks

#### 3.3. Datasets and CNN Models

#### 3.4. Common CNN Architectures

#### 3.4.1. LeNet-5

#### 3.4.2. AlexNet

#### 3.4.3. VGGNet

#### 3.4.4. GoogLeNet

#### 3.4.5. ResNet

#### 3.4.6. ResNeXt

#### 3.4.7. DenseNet

#### 3.4.8. SqueezeNet

#### 3.4.9. LeafNet

#### 3.4.10. M-bCNN

#### 3.4.11. Comparison of Common CNN Architectures

#### 3.5. Optimization Techniques

#### 3.5.1. Batch Gradient Descent (BGD) Optimization

_{k}) is a loss function based on the training data instance indexed by k, for iteration 1 to n.

#### 3.5.2. Stochastic Gradient Descent (SGD) Algorithm

_{k}) is a loss function based on the training data instance indexed by k, and i is the iteration index.

#### 3.5.3. AdaGrad

#### 3.5.4. Root Mean Square Propagation (RMSprop)

#### 3.5.5. Adaptive Moment Estimation (Adam) Optimizer

#### 3.6. Frameworks

#### 3.6.1. TensorFlow

#### 3.6.2. Theano

#### 3.6.3. Keras

#### 3.6.4. Caffe

#### 3.6.5. Torch

#### 3.6.6. Neuroph

#### 3.6.7. Deeplearning4j

#### 3.6.8. Pylearn2

#### 3.6.9. DL MATLAB Toolbox

#### 3.7. Analysis of DCNNs for Plant Leaf Disease Identification

## 4. Discussion and Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 5.**LeNet-5 architecture [73].

**Figure 6.**VGG-16 architecture [31].

**Figure 7.**Inception module with dimension reduction [79].

**Figure 8.**Building block of residual learning [80].

**Figure 9.**Architecture of DenseNet containing 3 Dense Blocks [33].

**Figure 10.**Architecture of SqueezeNet [83].

**Figure 12.**Comparison of classification accuracy of machine learning and deep learning models [64].

Points of Difference | Machine Learning Models | Deep Learning Models |
---|---|---|

Data Requirements | Require a small amount of data for training a model. | Require a large amount of data to train a model. |

Hardware Dependency | Machine learning algorithms can work on low-end machines such as CPUs. | Deep learning models need high-end machines for execution, such as GPUs. |

Feature Engineering | Machine learning models rely on hand-crafted feature extractors such as Histogram of Oriented Gradients (HOG), Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Principle Component Analysis (PCA), etc. for extracting features from an image. | Do not require explicit identification of features from an image. Deep learning models perform automatic feature extraction without human intervention. |

Interpretability | Machine learning algorithms such as decision trees give crisp rules to justify why and what the algorithm chooses. Thus, it is quite easy to interpret the reasoning behind these algorithms. | It is difficult to interpret the reasoning behind deep learning algorithms. |

Training Time | It takes less time to train a model. The time ranges from a few minutes to a few hours. The training time is dependent on data size, hardware configuration, type of model, etc. | It takes more time to train a model. The time ranges from a few hours to a few weeks. The training time is dependent on data size, hardware configuration, type of model, number of layers in a model, etc. |

Problem Solving Technique | Divides a problem into subproblems, solves each subproblem individually, and combines results obtained from each subproblem to solve the complete problem. | Efficient in providing a solution for the complete problem. Efficient in performing both feature extraction as well as classification. |

Preprocessing Technique | Objective(s) | Methodology | Working Mechanism | Advantages | Disadvantages |
---|---|---|---|---|---|

Resizing | Effective utilization of storage space and reducing computation time. | Nearest-neighbor interpolation | Replaces the value of each input pixel with the translated value nearest to it. | Simple and fast. | Causes distortion, blurring, and edge halos. |

Bilinear interpolation | The average of four nearest pixel values is used to find the value of a new pixel. | No grey discontinuity defects and provides satisfactory results. | Produces blurring and edge halos. Time consuming and more complex than the nearest-neighbor interpolation. | ||

Bicubic interpolation | Considers the closest 4 × 4 neighborhood of known pixels, i.e., 16 nearest neighbors of a pixel. | Provides smoother images with less interpolation distortion. | It needs more time to generate the output due to complex calculations. | ||

Augmentation | To increase the amount of relevant data in a dataset for training a model. | Traditional augmentation techniques | Generate new data from existing data by applying various transformation techniques such as rotation, flipping, scaling, cropping, translation, adding Gaussian noise, etc. | Simple to implement. | Disadvantages of geometric transformations include additional memory, transformation compute costs, and additional training time. |

Generative Adversarial Networks (GANs) | Comprise of a generator and a discriminator. Generator generates new examples, whereas discriminator distinguishes between generated and real. | Gives very impressive result by generating realistic visual content. | It fails to recover the texture of an image correctly. In the case of too small text or distortion in an original image, it generates a completely different image. | ||

Neural style transfer | Combines the content of one image with the style of another to form a new image. | Generating artistic artifacts with high quality. | |||

Normalization and Standardization | Used to find a new range of pixel values of an image. | Decimal scaling | Divides all pixel values with the largest value, i.e., 255 (8-bit RGB image). | Simplest transformation technique. | |

Min–Max normalization | The minimum pixel value is transformed to 0; the maximum value is transformed to 1. Other values are transformed into a decimal number between 0 and 1. | It provides a uniform scale for all pixels. | It is ineffective in handling outliers. | ||

Standardization or Z-score normalization | Standardization or Z-score normalization performs zero centering of data by subtracting the value of mean from each pixel and then dividing each dimension by its standard deviation. | It effectively handles outliers. | It does not produce normalized data with a uniform scale. | ||

Annotation | Used for selecting objects in images and labeling the selected objects with their names. | Bounding box annotations | A rectangle superimposed over an image in which all key features of a particular object are expected to reside. | Easy to create, declared by simply specifying X and Y coordinates for the upper left and bottom right corners of the box. | Additional noise is also included in the bounded box. This method faces difficulty for occluded objects. |

Pixel-wise image annotations | Point-by-point object selection is completed through the edges of objects. | Easy to use for any task where sizable, discrete regions must be classified/recognized. | High computation cost in terms of time. More prone to human errors. | ||

Outlier Rejection | Ignores invalid or irrelevant images from a dataset. | OrganNet | OrganNet is a CNN model, trained on the existing image datasets (ImageNet and PlantClef) as an automatic filter for data validation. | OrganNet is more efficient than the hand-design features set. | |

Denoising | Noise removal from an image. | Gaussian filter | Blurs an image and removes noise using a Gaussian function. | Conceptually simple, reduces noise and edge blurring. | It takes time, images are blurred as image details and edges are degraded. |

Mean filter | It is a linear filter that replaces the center value in the window with the mean or average of all values of the pixel in the window. | Simple, easy to implement for smoothing of images. | Over-smooth images with high noise. | ||

Median filter | It is a non-linear filter that replaces the center value in the window with the median of all values of the pixel in the window. | Reduces noise. Better than mean filter in preserving sharp edges. | Relatively costly and complex to compute. | ||

Wiener filter | It minimizes the overall mean square error in the process of inverse filtering and noise smoothing. | It is optimal in terms of mean square error. Removes the additive noise and inverts the blurring simultaneously. | Slow to apply; blurs sharp edges. | ||

Bilateral smoothing filter | It replaces the intensity of each pixel with a weighted average of intensity values from nearby pixels. | Preserves edges. Reduces noise. Performs smoothing. | Less efficient. |

Architecture | Layers | Parameters | Highlights | Reference |
---|---|---|---|---|

AlexNet | 8 (5 Convolution + 3 Fully Connected) | 60 million | AlexNet is similar to LeNet-5, but it is deeper, contains more filters in each layer, and uses stacked convolutional layers. Winner of ILSVRC-2012. | [74] |

VGGNet | 16–19 (13–16 convolution + 3 FC) | 134 million | The depth of a model is increased by using small convolutional filters of dimensions 3 × 3 in all layers to improve its accuracy. First runner-up in ILSVRC-2014 challenge. | [31] |

GoogLeNet | 22 Convolution layers, 9 Inception modules | 4 million | A deeper and wider architecture with different receptive field sizes and several very small convolutions. Winner of ILSVRC-2014. | [78] |

Inception v3 | 42 Convolution layers, 10 Inception modules | 22 million | Improves the performance of a network. It provides faster training with the use of Batch Normalization. Inception building blocks are used in an efficient way for going deeper. | [29] |

Inception v4 | 75 Convolution layers | 41 million | Inception-v4 is considerably slower in practice due to many layers. | [108] |

ResNet | 50 in ResNet-50, 101 in ResNet-101, 152 in ResNet-152 | 25.6 million in ResNet-50, 44.5 million in ResNet-101, 60.2 million in ResNet-152. | A novel architecture with ‘skip connections’ and heavy batch normalization. Winner of ILSVRC 2015. | [32] |

ResNeXt-50 | 49 Convolution layers and 1 Fully Connected layer | 25 million | Use ResNeXt blocks based on the strategy of ‘split–transform–merge’. Despite creating filters for a full channel depth of input, the input is split into groups. Each group represents a channel. | [81] |

DenseNet-121 | 117 Convolution layers, 3 Transition layers and 1 Classification layer | 27.2 million | All layers are connected directly with each other in a feed-forward manner. It reduces the vanishing-gradient problem and requires few parameters. | [33] |

SqueezeNet | Squeeze layer and Expand layers | 50 times fewer parameters than AlexNet. | SqueezeNet is a lightweight model of size 2.9 MB. It is approximately 80 times smaller than AlexNet. Achieves the same level of accuracy as AlexNet. Reduces the number of parameters by using a smaller number of filters. | [83] |

LeNet-5 | 7 (5 Convolution + 2 FC) | 60 thousand | Fast to deploy and efficient in solving small-scale image recognition problems. | [73] |

Name of Optimizer | Advantages | Disadvantages |
---|---|---|

BGD | Easy to compute, implement and understand. | It requires large memory for calculating gradients on the whole dataset. It takes more time to converge to minima as weights are changed after calculating the gradient on the whole dataset. May trap to local minima. |

SGD | Easy to implement. Efficient in dealing with large-scale datasets. It converges faster than batch gradient descent by frequently performing updates. It requires less memory as there is no need to store values of loss functions. | SGD requires a large number of hyper-parameters and iterations. Therefore, it is sensitive to feature scaling. It may shoot even after achieving global minima. |

AdaGrad | Learning rate changes for each training parameter. Not required to tune the learning rate manually. It is suitable for dealing with sparse data. | The need to calculate the second-order derivative makes it expensive in terms of computation. The learning rate is constantly decreasing, which results in slow training. |

RMSProp | A robust optimizer has pseudo curvature information. It can deal with stochastic objectives very nicely, making it applicable to min-batch learning. | The learning rate is still handcrafted. |

Adam | Adam is very fast and converges rapidly. It resolves the vanishing learning rate problem encountered in AdaGrad. | Costly computationally. |

Framework | Compatible Operating System | Programming Language Used for Development | Interface | Open Source | OpenMP Support | OpenCL Support | CUDA Support |
---|---|---|---|---|---|---|---|

TensorFlow | Linux, macOS, Windows, Android | C++, Python, CUDA | Python, Java, Go, JavaScript, R, Swift, Julia | Yes | No | Build TensorFlow with Single Source OpenCL | Yes |

Theano | Cross-platform | Python | Python | Yes | Yes | Under development | Yes |

Keras | Linux, macOS, Windows | Python | R, Python | Yes | Yes | TensorFlow as backend | Yes |

Caffe | Linux, macOS, Windows | C++ | C++, MATLAB, Python | Yes | Yes | Under development | Yes |

Torch | Linux, macOS, Windows, Android, iOS | C, Lua | Lua, LuaJIT, C, C++/OpenCL | Yes | Yes | Third-party implementations | Yes |

deeplearning4j | Linux, macOS, Windows, Android | Java, C++ | Java, Scala, Python, Clojure, Kotlin | Yes | Yes | No | Yes |

DL Matlab Toolbox | Linux, macOS, Windows | MATLAB, Java, C, C++ | MATLAB | No | No | No | Via GPU Coder |

Plant | Disease | Architecture | Datasets | Results |
---|---|---|---|---|

Banana | Black sigatoka and Black speckle | LeNet [21] | PlantVillage: 3700 images | Accuracy: 99% |

Apple | Black rot on Apple leaves | VGG16, VGG19, Inception-v3 and ResNet50 [18] | PlantVillage: 2086 images | VGG16: 90.4%, VGG19: 90.0%, Inception-v3: 83.0%, ResNet50: 80.0% |

14 different crop species | 26 different diseases | AlexNet, GoogLeNet [23] | PlantVillage: 54,306 images | AlexNet: Accuracy: 99.28% GoogLeNet: Accuracy: 99.35% |

6 different fruit plant species | 13 different diseases | Modified CaffeNet [25] | Authors created database containing 4483 images downloaded from the internet | Accuracy: 96.3% |

Tomato | 9 different diseases in tomato | AlexNet, GoogLeNet [35] | PlantVillage: 14,828 Images | GoogleNet: Accuracy: 99.18% AlexNet: Accuracy: 98.66% |

Cucumber | Melon Yellow Spot Virus (MYSV), Zucchini Yellow Mosaic Virus (ZYMV) | Author-defined CNN [36] | 800 images of cucumber leaves captured by Saitama Prefectural Agriculture and Forestry Research Center, Japan | Average accuracy: 94.9%, MYSV Sensitivity: 96.3%, ZYMV Sensitivity: 89.5%, |

Rice | 10 different diseases | Author-defined CNN [19] | The author created a database of 500 images captured from experimental rice fields of Heilongjiang Academy of Land Reclamation Sciences, China | Accuracy: 95.48% |

Tomato | 9 different types of diseases and pests | VGG-16, ResNet-50, ResNet-101, ResNet-152, ResNetXt-50, [82] | The author created a dataset of 5000 images captured through a camera from tomato farms located in Korea | VGG-16: 83.06%, ResNet-50: 75.37%, ResNet-101: 59.0%, ResNet-152: 66.83%, ResNetXt-50: 71.1% |

25 different Plant’s species | 19 different plant diseases | AlexNet, AlexNetOWTBn, GoogLeNet, Overfeat and VGGNet [24] | PlantVillage: 87,848 images of different plants (Both laboratory and field conditions) | AlexNet: 99.06%, AlexNetOWTBn: 99.49%, GoogLeNet: 92.27%, Overfeat: 98.96%, VGGNet: 99.53% |

Apple | Mosaic, Rust, Brown spot, and Alternaria leaf spot | Authors-defined CNN architecture based on AlexNet [39] | Dataset of 13,689 synthetic images | Proposed Model: 97.62%, AlexNet: 91.19%, GoogLeNet: 95.69%, ResNet-20: 92.76%, VGGNet-16: 96.32% |

Olive | Olive Quick Decline Syndrome (OQDS) | Authors-defined LeNet [28] | PlantVillage | Accuracy of 99% |

Tomato | 9 different types of diseases of tomato plant | AlexNet and SqueezeNet [40] | PlantVillage | AlexNet: 95.65%, SqueezeNet: 94.3% |

Wheat | 6 different diseases of wheat | VGG-CNN-S, VGG-CNN-VD16, VGG-FCN-S and VGG-FCN-VD16 [38] | WDD2017: 9230 wheat crop images | VGG-FCN-VD16: 97.95%, VGG-FCN-S: 95.12%, VGG-CNN-VD16: 93.27%, VGG-CNN-S: 73.00% |

Cucumber | Anthracnose, Downy mildew, powdery mildew and Target leaf spots | Architecture similar to LeNet-5 [37] | 1184 images: PlantVillage, forestry and captured through digital camera | Proposed model: 93.4%, SVM: 81.9%, RF: 84.8%, AlexNet: 94.0% |

Radish | Fusarium wilt | VGG-A [26] | 139 Images captured by a commercial UAV equipped with an RGB camera | Accuracy: 93.3% |

14 different plant species | 79 different diseases | GoogLeNet [65] | 1567 images captured using smartphones, compact cameras, DSLR cameras | Average accuracy: 94% |

Potato | Black Scurf disease, Silver Scurf, Common Scab and Black Dot disease | VGG [22] | A total of 2465 patches of diseased potatoes | Accuracy: 96.00% |

Tomato | Early Blight, Late Blight, Yellow Leaf Curl Virus, Spider Mite Damage and Bacterial Spot | VGG-19, Xception, Inception-v3, ResNet-50 [59] | PlantVillage: 3750 images | ResNet-50: 99.7%, Xception: 98.6%, Inception-v3: 98.4%, VGG-19: 98.2% |

Wheat | Septoria, Tan Spot and Rust | ResNet50 [68] | Author-defined dataset of 8178 images | Accuracy: 96.00% |

Cassava | 3 diseases: Brown leaf spot, Brown streak, and cassava mosaic 2 | Inception-v3 [104] | Author-defined dataset. Originally: 2756 images. Leaflet: 15,000 images | Accuracy: 93.00% |

14 different plant species | Not mentioned | VGG 16, Inception V4, ResNet50, ResNet101, ResNet152 and DenseNet121 [20] | PlantVillage | VGG16: 82%, Inception V4: 98%, ResNet50: 99.6%, ResNet101: 99.6%, ResNet152: 99.7% and DenseNet121: 99.75% |

Maize | 8 different diseases | GoogLeNet and Cifar10 | 500 images were collected from different sources: Plant Village and Google websites | GoogLeNet: 98.9% Cifar10: 98.8% |

Wheat | 6 different diseases | Author-defined architecture named M-bCNN (Matrix-based CNN) [85] | 16,652 images collected from Shandong Province, China | Accuracy: 90.1% |

Maize | Northern Leaf Blight | Five CNNs were trained on the augmented data set with variations in the architecture and hyperparameters of the networks [64] | 1796 images of maize leaves grown on the Musgrave Research Farm in Aurora, NY | Accuracy: 96.7%, |

Apple | 6 different diseases | AlexNet [106] | 2539 images of three species of apple trees from orchards located in the southern part of Brazil | Accuracy: 97.3%, |

Radish | Fusarium wilt of radish | GoogLeNet [102] | The images were captured in Korea, including Jungsun, Gangwon, and Hongchun, using two commercial UAVs | Accuracy: 90% |

Tomato | 8 different diseases | AlexNet, GoogLeNet, and ResNet [60] | PlantVillage: 5550 images | ResNet: 97.28% |

Rice | Rice Blast Disease | Two CNN models similar to Lenet5 [86] | 5808 images are obtained from the Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing, China | First CNN: 95.37% Second CNN: 95.83% |

Banana | Five major diseases along with a pest class | ResNet50, InceptionV2, and MobileNetV1 [103] | Dataset comprises about 18,000 field images of bananas from Bioversity International, Africa, and Tamil Nadu Agricultural University, India | Accuracy between 70–90% |

Apple, Banana | Apple scab, apple rot, banana sigotka, banana cordial leaf spot, banana diamond leaf spot, and Deightoniella leaf and fruit spot | VGG-16 [27] | 6309 sample images of apple and banana fruits PlantVillage and CASC-IFW datasets | Accuracy: 98.6% |

Grapevine | Esca disease | LeNet-5 [77] | The dataset consists of 70,560 learning patches by the UAV system with an RGB sensor | The best results were obtained with the combination of ExR, ExG and ExGR vegetation indices using (16 × 16) patch size reaching 95.80% |

Maize | The northern corn leaf blight, common rust and gray leaf spot | Author-defined CNN [61] | PlantVillage | Accuracy: 92.85% |

Tea | 7 Diseases: Red leaf spot, Algal leaf spot, Bird’s eye spot, Gray blight, White spot, Anthracnose, Brown blight | Author-defined CNN model named LeafNet (Improvement over AlexNet) [84] | A total of 3810 tea leaf images captured using a Canon PowerShot G12 camera in the natural environments of Chibi and Yichang within the Hubei province of China. | Accuracy: 90.16% |

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Dhaka, V.S.; Meena, S.V.; Rani, G.; Sinwar, D.; Kavita; Ijaz, M.F.; Woźniak, M.
A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases. *Sensors* **2021**, *21*, 4749.
https://doi.org/10.3390/s21144749

**AMA Style**

Dhaka VS, Meena SV, Rani G, Sinwar D, Kavita, Ijaz MF, Woźniak M.
A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases. *Sensors*. 2021; 21(14):4749.
https://doi.org/10.3390/s21144749

**Chicago/Turabian Style**

Dhaka, Vijaypal Singh, Sangeeta Vaibhav Meena, Geeta Rani, Deepak Sinwar, Kavita, Muhammad Fazal Ijaz, and Marcin Woźniak.
2021. "A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases" *Sensors* 21, no. 14: 4749.
https://doi.org/10.3390/s21144749