Notes

classification of leaves was performed by using author-modified CNN and Random Forest (RF) classifier among 32 species in which the performance was evaluated through CA at 97.3%. On the other hand, it was not as efficient at detecting occluded objects

Leaf and fruit counting were also performed by deep CNN in [44,45] and [46] respectively. For classification of crop type, [47] used author-modified CNN, [36] applied VGG 16, [34] implemented three unit LSTM, and [33] used CNN and RGB histogram technique. [47] used CA, [36] used CA and Intersection over Union (IoU), [34] used CA and F1, and [33] used F1-score as a performance metric.

recognition of different plants has been done by the DL approach in [48,49,50]. [48,50] employed user-modified CNN while [49] used AlexNet architecture. All were evaluated on the basis of CA. [49] outperformed the other two in terms of CA. Similarly, crop/weed discrimination was performed in [51,52], in which the author proposed CNN be used, and two datasets were utilized for the evaluation of the model. [51] evaluated precision and recall; however, [52] obtained CA for the validation of the proposed models respectively.

CNN was used for the classification of diseases in maize plants and histogram techniques to show the significance of the model. In [57], basic CNN architectures like AlexNet, GoogLeNet and ResNet were implemented for identifying the tomato leaf diseases. Training/validation accuracy were plotted to show the performance of the model; ResNet was considered as the best among all the CNN architectures. In order to detect the diseases in banana leaf, LeNet architecture was implemented and CA, F1-score were used for the evaluation of the model in Color and Gray Scale modes [32]. Five CNN architectures were used in [58], namely, AlexNet, AlexNetOWTbn, GoogLeNet, Overfeat, and VGG architectures in which VGG outclassed all the other models

ight different plant diseases were recognized by three classifiers, Support Vector Machines (SVM), Extreme Learning Machine (ELM), and K-Nearest Neighbor (KNN)), used with the state-of-the-art DL models like GoogLeNet, ResNet-50, ResNet-101, Inception-v3, InceptionResNetv2, and SqueezeNet. A comparison was made between those models, and ResNet-50 with SVM classifier got the best results in terms of performance metrics like sensitivity, specificity, and F1-score. According to [59], a new DL model—Inception-v3—was used for the detection of cassava disease. In [60], plant diseases in cucumber were classified by the two basic versions of CNN and got the highest accuracy, equal to 0.823. The traditional plant disease recognition and classification method was replaced by Super-Resolution Convolutional Neural Network (SRCNN) in [61]

For the classification of tomato plant disease, AlexNet and SqueezeNet v1.1 models were used in which AlexNet was found to be the better DL model in terms of accuracy [62]. A comparative analysis was presented in [63] to select the best DL architecture for detection of plant diseases. Moreover in [64], six tomato plant diseases were classified by using AlexNet and VGG-16 DL architectures, and a detailed comparison was provided with the help of classification accuracy. In the above approaches, no visualization technique was applied to spot the symptoms of diseases in the plants.