# 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
To evaluate the performance, they used both color and grayscale images and recorded an accuracy rate of 98.61% and 94.44%, respectively. The fusion of shape- and texture-based features such as Hu's moments [91], Zernike moments [92] with a CNN was used by [93] to identify olive leaf diseases. After 300 epochs, the authors [93] reported an accuracy rate of 98.61%. A fine-tuned AlexNet architecture was used by [PERSON] et al. [94] to identify rice plant diseases and they achieved an accuracy rate of 91.23%. [PERSON] et al. [2] used five different deep CNN architectures (AlexNet, AlexNetOWTBn, GoogLeNet, Overfeat and VGG) for the identification of plant diseases through leaf images of healthy and diseased leaves. The dataset used consisted of 58 different classes of images and they achieved the highest accuracy rate of 99.53% using the VGG [82] architecture. The dataset used was the largest available plant disease dataset, which included partial shading on leaves, images with different objects such as hands, finger, shoes, etc. The average time required to train these models was much more. One of the issues related to their method was the expansion of the existing database to incorporate a wider variety of plant species and diseases. Another issue was that the testing dataset used for the classification of the models was part of the same database that constituted the training set. Five different eggplant diseases were identified by [PERSON] et al. [95] using a pretrained VGG16 network. To evaluate the result, they also used three different color spaces of the images, namely, HSV, YCbCr and grayscale. They obtained an accuracy rate of 99.4% with the RGB and YCbCr images. Images used to train and test the network were captured by mobile devices in both laboratory and field conditions. Six different pretrained network were used in [96] to identify 10 categories of several crop (eggplant, beans, lime, ladies finger) diseases. Among all the models, VGG16 gave the highest accuracy of 90%. We summarize the above papers as follows:  
* The use of a pretrained deep learning model eliminates the preprocessing and feature extraction in the identification of disease. * A fine-tuned and transfer-learning approach where the model is pretrained with a large dataset performs better than learning from scratch. * RGB images give better performance accuracies than other formats of images. * The number of parameters used in LeNet, AlexNet, VGG and GoogLeNet is large and hence the computation takes longer. * The required training time is much longer in these models and requires high-power GPUs to train the model. [PERSON] et al. [97] used the VGG16, InceptionV3 and GoogLeNetBN architectures to identify diseases in plants. In their paper, they examined and compared the performances of these models based on a transfer-learning approach. They proved that disease detection using a pretrained model reduced the overfitting impact. [PERSON] et al. [98] extended the work from [99] for the identification of three different wheat diseases based on mobile devices. A modified ResNet50 architecture was used in that paper. Firstly, a 7\(\times\)7 convolution layer of ResNet50 was replaced by two consecutive 3 \(\times\) 3 convolution layers. Secondly, a dense layer with a softmax activation function was replaced by a sigmoid function, which was able to detect multiple diseases on the same leaf. They obtained an improved accuracy rate of 87% using a superpixel segmentation approach and images containing the artificial background used in training. [PERSON] et al. [73] designed a practical and applicable solution in the real-time detection of tomato diseases and pests recognition using a robust deep-learning-based method. Furthermore, their model was able to deal with some of the complexities such as illumination conditions, object dimension and background variation. In their approach, they used a meta-architecture of CNNs, such as Faster R-CNN, SSD and R-FCN models; for the extraction of features, VGG16 [82], ResNet-50 [100], ResNet-101 models were used.