# 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 that paper, they showed that increasing the size of the dataset using data augmentation techniques such as image flipping, noise injection, gamma correction, color augmentation, scaling and rotation increased the validation accuracy from 91.43% to 96.46%. They also compared their results with traditional machine-learning-based approaches and showed that a deep-learning-based approach outperformed traditional approaches. [PERSON] et al. [109] used a deep convolution network fine-tuned by transfer learning for the detection of apple leaf diseases. They compared two architectures, namely, building a shallow network from scratch and transfer learning by fine-tuning. The shallow network consisted of some convolution layer with some filters, and there were two fully connected layers and one softmax layer to predict the output. Transfer learning is a useful approach to build powerful classification network using few data, by fine-tuning the parameters of a network pretrained on a large dataset, such as ImageNet [110]. The CNN-based architectures GoogLeNet and Cifar10 were used in [111] to identify nine different types of maize leaf diseases. In that paper, the authors used data augmentation techniques to increase the data size and improved some hyperparameters by changing the pooling combinations, adding dropout operations and rectified linear unit functions. In the GoogLeNet model, the average identification accuracy obtained was 98.9%, and the Cifar10 [112] model achieved an average accuracy of 98.8%. A novel deep convolution network based on the AlexNet and GoogLeNet architectures was used by [PERSON] et al. [113] to identify four different apple leaf diseases using leaf images. In their model, they replaced the fully connected layer of AlexNet by a convolution layer and an inception layer, which reduced the model parameter by a large number with a higher accuracy rate of 97.62%. The optimizer used in their paper was [PERSON]'s accelerated gradient (NAG). Later on [PERSON] et al. [114] extended the work in [24] into a mobile-based cassava disease detection. They utilized the single-shot multibox (SSD) model with the MobileNet detector and classifier, which was pretrained on the COCO dataset [115]. To evaluate the performance, they used both image and video files of diseased leaf images. In total, 2415 images of six different diseases were used to train the CNN network and they obtained an accuracy of 80.6% and 70.4% in the case of image and video, respectively. [PERSON] et al. [116] designed a deep CNN based on the InceptionV3 [117] architecture. In their approach, they used attention map and identified and removed several layers which were not contributing to the identification. The removal of the layers reduced the number of parameters by 75% without affecting the classification accuracy and a top accuracy of 97.1% was achieved. [PERSON] et al. [118] proposed a novel deep CNN method inspired from LeNet and AlexNet to identify 10 different rice diseases. To achieve the optimal result, different convolution filter sizes and pooling operations were carried out. The maximum accuracy obtained using stochastic pooling was 95.48% and 93.29% using a \(16\times 16\) convolutional filter. Stochastic pooling has the advantages of max-pooling and also prevents the model from overfitting. One of the advantages of these models is that their computation time decreases as the number of layer used is less. A SqueezeNet [119] architecture was used by [PERSON] et al. [120] to identify tomato leaf diseases. They used a robot to detect the diseases on the plants autonomously in the field or in the greenhouse and obtained an accuracy rate of 94.3% using the SqueezeNet architecture. They compared their performance with that of the AlexNet architecture, whose size is 227.6 MB, while the size of SqueezeNet is 2.9 MB. A modified Cifar10 quick CNN model was used by [PERSON] et al. [121] to identify four different tea leaf diseases.