# 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
A smooth L1 loss function was also used. They obtained an accuracy rate of 83%. In their model, they also included the stage of a disease on the leaves and the position where it occurred. [PERSON] et al. [24] used an image-based DL method for the identification of diseases in cassava plants. They used a transfer-learning approach on the InceptionV3 model to train the network for the identification of three diseases and two pest damages. They used two different datasets, namely, the original cassava dataset consisting of multiple leaves on a single image and the leaflet cassava dataset consisting of single leaf images. There was an improvement in the accuracy for the leaflet dataset in comparison with the original cassava dataset. To analyze the performance, they used a softmax layer, SVM and KNN classifiers and obtained the maximum accuracy of 98% using an SVM classifier. [PERSON] et al. [101] identified four different tomato diseases using pretrained deep learning models, namely, VGG16, VGG19, ResNet and InceptionV3. They also fine-tuned the network to obtain the optimal result. The authors used two datasets, one of images in laboratory conditions, and another of self-collected field images; They observed that the laboratory images performed better. Among the DL models, InceptionV3 gave the best performance accuracy of 99.60% and 93.70% on laboratory and field images, respectively, after fine-tuning the parameters. [PERSON] et al. [25] identified five different cassava plant diseases using a plain convolution neural network (PCNN) and a deep residual network (DRNN) and showed that the DRNN outperformed the PCNN by a margin of 9.25%. A MobileNet CNN-based model was used by [PERSON] et al. [102] to identify the 10 most common types of tomato diseases and they obtained an accuracy rate of 90.3%. [PERSON] et al. [103] identified ginkgo leaf diseases using VGG16 and InceptionV3 with images under both laboratory and field conditions. Between the two models, InceptionV3 gave better a performance accuracy on field images and VGG16 gave a better performance accuracy on laboratory images. The InceptionResNetV2 architecture was used by [PERSON] et al. [104] to identify different crop diseases and insect pests in crops. They built an Internet of things (IoT) platform in remote areas, such as mountains, and identified diseases and insect pests with an accuracy rate of 86.1%. We summarize the above the papers as follows:  
* Extracting multiple features from different filter sizes in parallel improves the model performance. * A CNN with residual connection can train a large model without increasing the error rate. * A residual connection handles the vanishing gradient issue using identity mapping. A pipeline CNN architecture was used by [PERSON] et al. [105] to identify maize plant diseases. Three layers of CNNs were trained: firstly, several CNNs were trained to classify small regions of images that contains the lesions; secondly, the predictions of the first layer were combined into a separate heat map; and finally, they were fed into the CNN layer to classify whether it was affected by the disease or not. A Faster R-CNN was used by [PERSON] et al. [106] to identify sugar beet diseases and a correct classification rate of 95.48% was obtained. [PERSON] et al. [107] detected four different potato diseases using a deep CNN. In that paper, the database of images used contained potatoes of different shapes and sizes and the images were labeled manually by experts. Several dropout layers were used to deal with the problem of overfitting, and the dataset was split into different training and testing ratios; the best accuracy of 96% was achieved with a 90%-10% training and testing ratio. A nine-layer deep convolution neural network was used by [PERSON] et al. [108] to identify different plant diseases.