An Integrated Deep Learning Framework for Fruits Diseases Classification

Abstract

Agriculture has been an important research area in the field of image processing for the last five years. Diseases affect the quality and quantity of fruits, thereby disrupting the economy of a country. Many computerized techniques have been introduced for detecting and recognizing fruit diseases. However, some issues remain to be addressed, such as irrelevant features and the dimensionality of feature vectors, which increase the computational time of the system. Herein, we propose an integrated deep learning framework for classifying fruit diseases. We consider seven types of fruits, i.e., apple, cherry, blueberry, grapes, peach, citrus, and strawberry. The proposed method comprises several important steps. Initially, data increase is applied, and then two different types of features are extracted. In the first feature type, texture and color features, i.e., classical features, are extracted. In the second type, deep learning characteristics are extracted using a pretrained model. The pretrained model is reused through transfer learning. Subsequently, both types of features are merged using the maximum mean value of the serial approach. Next, the resulting fused vector is optimized using a harmonic threshold-based genetic algorithm. Finally, the selected features are classified using multiple classifiers. An evaluation is performed on the PlantVillage dataset, and an accuracy of 99% is achieved. A comparison with recent techniques indicate the superiority of the proposed method.

PDD-Net: Plant Disease Diagnoses Using Multilevel and Multiscale Convolutional Neural Network Features

“…Although their suggested CNN frameworks [22,25] are resilient to class diversity in plant diseases, these models failed to detect plant infections at an early stage. Tariq et al [30] suggested a hybrid framework for plant infection classification. They utilized the ResNet-50 [39] deep learning model for training.…”

“…Figure 7 corresponds to the combined confusion matrices for all 38 classes on unseen 12,784 test images. Several other CNN-based classifiers, including DenseNet-201 [21,22], DenseNet-121 [22,25,29,33], ResNet-50 [21,30,33], and VGG-16 [26,32,33] were also trained on the same samples. The confusion matrix of each model was generated and combined, and their corresponding true positive rates were compared with the proposed model; the comparison is shown in Figure 8 We calculate the precision, recall, and F1 score of the proposed PDD-Net, as shown in Table 3, for all 38 classes of the PlantVillage benchmark dataset.…”

“…The overall performance was evaluated by taking the average of all classes. We compared the overall performance of PDD-Net with several CNN-based classifiers used for plant disease classification, including DenseNet-201 [21,22], DenseNet-121 [22,25,29,33], ResNet-50 [21,30,33], and VGG-16 [26,32,33].…”

“…Recently, with the advent of domain-specific architectures [18] (i.e., GPU, TPU, etc. ), deep learning (DL)-based classifiers, particularly CNN, are becoming increasingly popular [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. A CNN-based classifier (i.e., VGG-Net [34], ImageNet [35], Inception-Net [36], DenseNet-121 [37], MobileNet [38], ResNet50 [39], etc.)…”

Implementing deep‐learning techniques for accurate fruit disease identification

“…The models were trained and tested on two sets of data with the best results from CNN, with an accuracy of 96.9% and a computational time of 11.8 minutes. Tariq et al (2022) proposed a DL framework for classifying fruit diseases using transfer learning and a harmonic threshold‐based genetic algorithm. The method achieved an accuracy of 99% when tested on the PlantVillage data set (https://www.kaggle.com/datasets/emmarex/plantdisease).…”