Notes

The machine learning deals with classification, the research
work is used to classify healthy and unhealthy plants. Our
work is based on morphological features of the plant leaf. The
techniques represented in this paper are
1. Decision tree
2. K-mean clustering
3. Naive Bayes
4. Random forest
5. Artificial neural network

introduced the saliency map for visualizing the symptoms of plant disease; [27] identified 13 different types of plant disease with the help of CaffeNet CNN architecture, and achieved CA equal to 96.30%, which was better than the previous approach like SVM. Moreover, several filters were used to indicate the disease spots

used AlexNet and GoogLeNet CNN architectures by using the publicly available PlantVillage dataset. The performance was evaluated by means of precision (P), recall (R), F1 score, and overall accuracy. The uniqueness of this paper was the implication of three scenarios (color, grayscale, and segmented) for evaluating the performance metrics and comparison of the two famous CNN architectures. It was concluded that GoogLeNet outperformed AlexNet. Moreover, visualization activation in the first layers clearly showed the spots of diseases.

a modified LeNet model was used to detect olive plant diseases. The segmentation and edges maps were used to spot the diseases in the plants. Detection of four cucumber diseases was done in [66] and accuracy was compared with Random Forest, Support Vector Machines, and AlexNet models. Moreover, the image segmentation method was used to view the symptoms of diseases in the plants. A new DL model was introduced in [67] named teacher/student network and proposed a novel visualization method to identify the spots of plant diseases. DL models with some detectors were implemented in [68], in which the diseases in plants were marked along with their prediction percentage.


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ResNet-50 with the detector R-FCN gave the best results. Furthermore, a kind of bounding box was drawn to identify the particular type of disease in the plants. In [69], a banana leaf disease and pest detection was performed by using three CNN models (ResNet-50, Inception-V2 and MobileNet-V1) with Faster-RCNN and SSD detectors. According to [70], different combinations of CNN were used and presented heat maps as input to the diseased plants’ images and provided the probability related to the occurrence of a particular type of disease. Moreover, ROC curve evaluates the performance of the model.

feature maps for rice disease were also included in the paper. LeNet model was used in [71] to detect and classify diseases in the soybean plant. In [72], a comparison between AlexNet and GoogLeNet architectures for tomato plant diseases was done, in which GoogLeNet performed better than the AlexNet; also, it proposed occlusion techniques to recognize the regions of diseases. The VGG-FCN and VGG-CNN models were implemented in [73], for the detection of wheat plant diseases and visualization of features in each block. In [74], VGG-CNN model was used for the detection of Fusarium wilt in radish and K-means clustering method was used to show the marks of diseases

A semantic segmentation approach by CNN was proposed in [75] to detect the disease in cucumber. In [76], an approach based on the individual symptoms/spots of diseases in the plants was introduced by using a DL model for detecting plant diseases. A Deep CNN framework was developed for identification, classification, and quantification of eight soybean stresses in [77]. In [78], rice plant diseases were identified by CNN, and feature maps were obtained to identify the patches of diseases.