# 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  
Recently, deep learning (DL) has achieved an exponential growth in the field of computer vision tasks such as object detection, pattern recognition, classification and biometry. DL models exhibit outstanding performance in image recognition task such as the ImageNet challenge. This image recognition idea extended to the agricultural field plant identification [71], disease detection [2; 72; 73; 74], pest recognition [75; 76], fruit identification [77; 78] and weed detection [79]. In DL, there is no need for segmentation and feature extraction as a DL model has the ability to learn the features automatically from the input images. [PERSON] et al. [80] identified two different types of cucumber diseases, i.e., melon yellow spot virus (MYSV) and zucchini yellow mosaic virus (ZYMV) using a CNN. In their paper, they used a rotation of images to increase the data size and showed that increasing the number of images increased the performance. The accuracy obtained with the proposed DL model was 94.9%. In [81], the authors extended their previous work to identify seven different types of cucumber diseases using two CNN architectures with a large dataset. In the dataset, they considered the a variety of images aspects such as the distance, angle, background, and a nonuniform lighting condition. Three data augmentation techniques,  
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namely, shifting, rotation and image mirroring were used to increase the size of the dataset. The CNN configuration was based on VGG Net [82] and the Caffe framework [83]. The performance of CNN-1 decreased more in the case of a bad image condition than that of CNN-2 as the CNN-2 model was trained with both good and bad condition images. They attained an accuracy rate of 82.3% with fourfold cross-validation. [PERSON] et al. [84] used pretrained fine-tuned CaffeNet model for the identification of 13 different plant diseases. To train and evaluate the performance of the model, they used 4483 internet-downloaded images and a data augmentation technique to increase the data size. A 10-fold cross-validation technique was used to evaluate the performance accuracy of model and they achieved an accuracy rate of 96.3%. Pretrained AlexNet and VGG16 net were used by [PERSON] et al. [85] to identify six different tomato leaf diseases. The classification accuracy obtained was 97.29% for VGG16 net and 97.49% for the AlexNet architecture. [PERSON] et al. [72] used two eminent established architecture of CNN, AlexNet and GoogLeNet, in the classification of 26 different diseases in 14 crop species using 54,306 images. To keep track of model overfitting, they split the dataset into different sets of training and testing ratios. Three different image data (color, grayscale, segmented) were used and the highest training accuracy of 99.35% was achieved on RGB images using GoogLeNet. Several issues existed in their method, including that the entire process was exclusively done in laboratory setup images, not in real-time images from cultivation fields. Performance decreased to 31% when the model was tested on images from different sources. One more limitation was that these models were based on the classification of front-facing single leaves on homogeneous background images. Multiple CNN architectures were used by [PERSON] et al. [86] to identify six different apple leaf diseases. The best result were obtained using the AlexNet [87] architecture and an accuracy rate of 97.3% was achieved. In order to compare the CNN results, they used a multilayer perceptron (MLP) as an MLP can achieve a high recognition accuracy in image classification [88]. The Caffe [83] and DIGITS tools were used to design the CNN architecture. A LeNet [89] based deep CNN architecture was used by [PERSON] et al. [90] to identify banana leaf diseases. Their proposed approach was effective under challenging conditions such as the illumination, a complex background, different resolutions, sizes, poses, and orientations of real scene images.