# A Survey on Different Plant Diseases Detection Using Machine Learning Techniques
## 3 Different Existing Machine Learning Based Techniques for Plant Disease Detection
### Deep-Learning-Based Identification of Diseases
In their paper, the standard convolution was replaced by a depthwise separable convolution, which reduced the number of parameters. They also compared their results with traditional machine-learning-based techniques and some classical CNN models such as LeNet-5 [122], AlexNet and VGG16 [109], and achieved an improved accuracy rate of 92.5%. [PERSON] et at. [123] proposed a low-cost mobile deployable model to identify two different common types of apple leaf diseases. They used the MobileNet deep learning model and compared its performance with that of ResNet152 and InceptionV3. The dataset used were collected by agricultural experts. The authors achieved accuracy rates of 73.50%, 75.59%, and 77.65% for MobileNet, InceptionV3 and ResNet152, respectively. The average handling time in MobileNet was much less than that of InceptionV3. Rice and maize leaf diseases were identified by [PERSON] et al. [74] using the INC-VGGN method. In their approach, they replaced the last convolution layer of VGG19 with two inception layers and one global average pooling layer. In their model, basic features were extracted using a pretrained model and high-dimensional features by the inception layer. They obtained an accuracy rate of 92% and 80.38% in rice and maize, respectively. [PERSON] et al. [124] used an EfficientNet architecture to identify different diseases in plant. The performance of their model was compared with that of other CNN models such as AlexNet, ResNet50, VGG16 and InceptionV3 and it showed that EfficientNet outperformed the other CNN models. The highest accuracy rate of 99.91% was obtained using EfficientNetB5 on the original dataset and 99.97% using EfficientNetB4 on the original dataset. The number of parameters generated in the EfficientNet model was much less that that of the other deep learning models and hence it required less time to train the network. [PERSON] [125] used a hybrid CNN approach to identify plant leaf diseases. In that paper, the author used an inception network with a depthwise separable convolution, which reduced the number of parameters and computational cost of the model. Using a k-fold cross-validation, the model achieved a maximum accuracy of 99.27% and an average accuracy of 99% on the PlantVillage dataset. We summarize the above papers as follows:  
* Removing convolution layers, changing the filter sizes, replacing the standard convolution by a depthwise separable convolution reduce the number of parameters. * An attention network which focuses on a particular region reduces the complexity of the network. * The time required to train the network is much less. * It is easy to implement on small devices and the computation time is reduced. In [126], the authors used CNN, VGG and Inception architectures to identify plant leaf diseases. In their approach, they used 15% images of the PlantVillage dataset and some real-time captured images to evaluate the accuracy and obtained an accuracy rate of 98% and 95%, respectively, with the CNN architecture. Pretrained AlexNet and GoogleNet [127] were used in [128] to detect three different soybean diseases from healthy leaf images with some modified hyperparameters such as minibatch size, max epoch and bias learning rate. In [129], the authors classified the maize leaf diseases from healthy leaves using deep forest techniques. In their approach, they varied the hyperparameters of the deep forest, such as the number of trees, number of forests, number of grains, and compared their results with traditional machine learning models such as SVM, RF, LR and KNN. The deep forest model achieved an accuracy rate of 96% and a maximum F1 score of 0.96 among all other classifiers. Using the principles of deep learning, a fully connected CNN model was built by [PERSON] et al. [130] to classify maize leaf diseases. The model was able to recognize three different types of maize diseases with an accuracy rate of 92.85%. A multilayer CNN was used by [PERSON] [131] to identify mango leaf diseases and obtained an accuracy rate of 97.13%.