A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

1  Introduction

Agriculture cultivates land and plants to produce crops and fruits [1]. It is a significant source of food for a large population in Pakistan and India [2]. Agriculture employs more than 70% of Pakistan’s total population. It is Pakistan’s most important economic sector, accounting for 19.2% of the GDP and 38.5% of the labor force. According to the Pakistan Bureau of Statistics, agriculture is Pakistan’s largest economic sector, and the majority of the country’s population is directly or indirectly dependent on it [3].

Fruits play an important role in human nutrition as sources of vitamins, minerals, and dietary fibers [4]. Furthermore, fruits and vegetables contain 16% magnesium, 19% iron, and 9 percent calories [5]. The apple is the world’s most popular fruit, containing vitamins, dietary fibers, and antioxidants; however, many apples are unable to reach customers due to diseases caused by bacteria or fungal viruses, resulting in economic losses [6]. Apple scabs, apple rust, and cedar are the most common apple diseases [7]. Fire blight fire-like appearance on infected plant parts, and secretion of golden color bacterial secretion is seen on the stem [8]. Powdery mildew is a serious disease that wreaks havoc on buds, new shoots, and leaves. A white powdery coating on the leaves distinguishes this disease [9,10]. Cedar apple or yellow patches on the leaf undersides are surrounded by a red band, and small black spots in the center are called aecia form [11].

Traditional methods of detecting and identifying fruit diseases rely on skilled naked-eye observation. Because of the remote locations of specialists’ availability, manually identifying fruit disease is a time-consuming and costly in some developing countries. Consultation with them is also time-consuming and costly [12]. Early detection of leaf disease is critical for effective prevention, which improves fruit quality and growth rate [13,14]. The symptoms that appear on the leaves of various fruit plants provide information that can be used to help diagnose disease. Many computer scientists demonstrated various methods for detecting fruit diseases. A simple computerized method includes key steps such as image preprocessing, feature extraction using pattern recognition techniques, feature fusion, and classification [15]. To achieve better results, preprocessing is working on image quality, improving some image features, and removing image distortion [16]. A variety of techniques are used to extract features, including shape-based features, point features, color features, and many others. The process of combining information from multiple descriptors to improve overall system performance is known as feature fusion. However, one limitation of this work is that the testing process’s computational time has increased [17]. Finally, the features are classified using machine learning classifiers like support vector machine, K-Nearest neighbors, neural network, and a few others.

Deep learning is a type of machine learning that shows huge success in the area of computer vision for several tasks such as object classification, medical imaging [18,19], plant diseases classification [20,21], and named a few more [22,23]. Convolutional neural network (CNN) is a deep learning technique that consists of several hidden layers, including convolutional, pooling, ReLu, batch normalization, and fully connected layers. A CNN is a powerful deep learning algorithm that can deal with millions of variables, input 2D images to reduce computational costs, and apply convolutional filters [24]. It can learn the filters while other algorithms use hand-engineering filters with enough training [25]. The convolutional layer is the linear operation in which filters (kernel) are applied to real images [25]. Transfer learning (TL) is a machine learning technique used for transferring knowledge from one deep model to another fine-tuned model [23,26]. The main advantage of this technique is that it allows for quick processing when only limited data is available.

Computer vision researchers used deep learning to introduce several techniques for recognizing fruit leaf diseases. However, due to various factors, they continue to lose classification accuracy. The hot challenges are: i) limited dataset images for training; ii) extraction of redundant and irrelevant features; iii) choice of selected deep model for training; and iv) missing some essential features during the best features selection. This article proposed an automated framework for apple fruit leaf disease recognition using deep learning and multi-stage feature selection. Our significant contributions in this work are: i) proposed a contrast enhancement technique that was later utilized for the data augmentation; ii) Two pre-trained deep models have been updated based on the last layers and trained through deep transfer learning; iii) proposed a fusion approach and developed an improved optimization technique for best feature selection.

The rest of the manuscript is organized in the following order. Related works that contain methodology and results of the presented techniques are discussed in Section 2. Sections 3 and 4 describe the proposed methods and results. Finally, Section 5 concludes the manuscript.

2  Related Work

Numerous automated techniques for recognizing fruit leaf diseases have been introduced in the literature. Many of them concentrated on deep learning-based feature extraction, while only a few used traditional features. Bosilj et al. [26] presented a study using a deep learning approach to detect and recognize fruit leaves by transfer learning. They trained the Nasnet model and tested it over the available Plant Village Dataset. They achieved a high level of accuracy of 93.82%. Vallabhajosyula et al. [27] presented a wide taxonomy of the performance of several pre-trained neural networks, as well as the performance of a weighted ensemble of those models that are important to the detection of plant leaf disease. Furthermore, the suggested work’s performance is assessed using a publicly accessible plant village dataset, which has 38 classes collected from 14 crops. The suggested model’s performance evaluation shows that it is effective at identifying various plant diseases and outperforms pre-trained models. Bharati Devi et al. [28] trained their model on ten different classes of tomato plant leaf images that represent various tomato plant illnesses. They used various types of data augmentation approaches to expand the quantity of the training data. Their suggested model outperforms the current models for detecting tomato leaf disease, with 92.3% accuracy on the dataset. Bimorogo et al. [29] used a Plant Village dataset with 38 different classes. MobileNet V2, NasNet Mobile, DenseNet 121 layer, and InceptionV3 are the models that have been tested and compared. NasNet Mobile had a performance of 97.3%, MobileNet V2 had a performance of 96.55%, and DenseNet 121 had a performance of 96.21%. They concluded that NasNet Mobile is the CNN model that is best for mobile devices. Bansal et al. [30] suggested a model which is a combination of pre-trained DenseNet121, EfficientNetB7, and EfficientNet NoisyStudent models that use photos to classify apple tree leaves into one of four categories: healthy, apple scab, apple cedar rust, and numerous illnesses. The study uses a variety of Image Augmentation approaches to improve the dataset size and, as a result, the model’s accuracy. Their suggested model achieves an accuracy of 96.25% on the validation dataset. With a 90% accuracy rate, the suggested model can detect leaves with different illnesses. Their suggested model performed well on several measures and can be used in the agricultural area to reliably and quickly determine plant health. Kim [31] propose superpixel-based disease classification as an alternative to traditional approaches that rely on either region segmentation or end-to-end learning of a full image by a neural network. They used Convolutional Neural Network for classification purpose. They used plant village dataset and achieved accuracy of 92.43 and Fi-score of 0.93 compared to 98.28 and 0. 98. Lu et al. [32] examined the most recent CNN networks for plant leaf disease classification. They reviewed the DL principles involved in the classification of plant diseases. They have compiled a list of the major issues with CNN and their plant disease detection solutions. They also talked about the future of plant disease classification. Sun et al. [33] suggested a lightweight CNN model for real-time detection of apple leaf diseases on mobile devices. Then, by recreating the common 3×3 convolution, a fundamental module called Mobile End AppleNet block (MEAN block) is developed to boost detection speed and reduce model size. Meanwhile, the Apple-Inception module is created by introducing GoogLeNet’s Inception module and replacing all 3×3 convolution kernels in Inception with MEAN blocks. The results of the experiment demonstrate that mean-SSD can detect 5 common apple leaf illnesses with an accuracy of 83.12% mean average precision (mAP) and a speed of 12.53 frames per second (FPS), indicating that the unique mean-SSD model can efficiently and accurately detect 5 common apple leaf diseases on mobile devices. Kumar [34] focuses on detecting plant diseases and minimizing financial damages. For image recognition, they presented a deep leaning-based technique. They looked at three different Neural Network architectures: the Faster Region-based Convolution Neural Network (Faster R-CNN), the Region-based Fully CNN (R-CNN), and the Single Shot Multibook Detector (SSD). The system proposed in their paper is capable of detecting many types of sickness and dealing with complex scenarios. The accuracy of the validation result is 94.6 percent, indicating that the Convolution Neural Network is feasible. Wang et al. [35] proposed a more simple strategy for differentiating between crop and disease identification and classifying them separately. On the test set, the crop and disease identification accuracies reached 99.99% and 99.7%, respectively. On the test set in a real-world environment, they achieved 84.11% and 75.58%, respectively. The study presented in this paper enhances the practical value of agricultural disease identification research. Pawar et al. [36] mentioned many classification techniques, such as CNN, Image Processing, K-Mean Clustering, SVM, and others, are mentioned in their study for the identification and classification of plant leaf disease detection.

3  Proposed Methodology

The proposed automated framework for apple fruit leaf disease recognition is illustrated in Fig. 1. In this figure, it is described that initially original images are acquired and performed contrast enhancement. Two pre-trained models are opted for in the next step and fine-tuned based on the feature layers. Transfer learning is applied in the later step, and two models are utilized for feature extraction. Features of both models are fused using the proposed Parallel Priority (PP), and select the best features in the later step using an improved artificial butterfly optimization algorithm. Finally, the selected features are classified using machine learning classifiers for final classification results. A brief description of each step is given below.

Figure 1: Proposed framework of apple leaf disease recognition

3.1 Dataset Description

In this work, the Plant Village dataset [37] is utilized for the experimental process. This dataset contains a variety of leaf diseases from various crops and fruits. In this article, we will look at apple leaf images. Apple scab, apple cedar rust, apple black rot, and apple healthy are the four apple classes (as illustrated in Fig. 2). The total number of images is 3171, all in RGB format. There are 630 images of apple scab, 621 images of apple black rot, 275 images of apple cedar rust, and 1645 images of healthy class.

Figure 2: Sample images of selected apple leaf disease classes (collected from plant village dataset)

3.2 Dataset Augmentation

Data Augmentation is a technique for increasing the size of a dataset and modifying data from existing data. In this work, we used data augmentation to increase the number of images of the selected apple classes. Our target number of images is 4500 for each class after the augmentation process. We applied three different operations to increase the images: i) horizontal flip, ii) vertical flip, and iii) top-hat operation-based contrast enhancement of original images. In the healthy class augmentation process, horizontal and vertical flip operations are performed and stop the process when images are reached 4500. The algorithm of data augmentation is given below.

3.3 Convolutional Neural Networks

Convolutional Neural Network (CNN) is a type of artificial neural network inspired by the visual cortex of humans. Mainly, CNN has several layers, such as an input layer, convolution layer, pooling layer, ReLu layer, fully connected layer, and output layer [38]. The structure of a convolutional neural network is shown in Fig. 3.

Figure 3: Convolutional neural network

A CNN normally accepts an image of the three dimensional such as N×M×3. The original image’s pixel values are weights of a first convolutional layer. In this layer, most features are extracted and involve various important concepts like feature map, kernel, stride, and padding [38]. The filter (kernel) is applied to an input image to create the feature map. Finally, the convolutional maps are calculated as:

Mi=ai+∑jNij∗Yj(1)

where Yj and ith is the input, ∗ is the convolutional operator, Nij is sub-kernel, and ai is bias term. For the reduction of the size of the feature map, a max-pooling operation is applied. The size of the convolution maps is reduced using sub-sampling layers. The max-pooling output layer is calculated by multiplying the input layer’s maximum activation value by the number of sub-windows in each feature map. The Relu layer is rectified linear units layer which applies the function on all input values and changes all the negative activations into zero. A non-linear activation function is applied to every single convolution. Relu is computed as f(r)=max(0,r) where r is the input. In the fully connected layer, all of the inputs from one layer are connected to every activation unit of the next layer. This layer compiles the data which is extracted by the previous layer to get the final output [39]. Mathematically, this layer is defined as follows:

Xmn(I)=f(∑j=1nHMmnIj+Mk0)(2)

3.4 Transfer Learning

In this work, we utilized transfer learning (TL) for training pre-trained deep models on selected apple datasets for classification purposes (apple diseases). TL [40] is useful when the training data of target domain is smaller than the source domain. Mathematically, the TL is defined as follows: A domain D which is consists of X and P(X) where X={x1,…,xn} is feature space and P(X) is marginal probability distribution [41]. Thus the domain D can mathematically express as D={X,P(X)}. A task T can be defined as two components, one is label space that is ɣ and other is objective predictive function that is f. For a new instance x, function f is used to predict the corresponding labels. The task T is denoted by T={γ,f(x)} which is learned training data that is consists of pair of xi,yi where x belongs to X and ∈γ. Given a source domain Ds and Ts corresponding task and DT target domain as well as learning task TT where Ds≠DT or Ts≠TT so that transfer learning aims to improve the learning of targeted predictive functions [42]. Visually, the process of TL is illustrated in Fig. 4.

Figure 4: Transfer learning-based training of pre-trained models for apple leaf diseases classification

3.5 Deep Learning Features

In this work, we utilized two pre-trained models named Nasnet Mobile [43] and Darknet-53 [44] for deep feature extraction. Google’s brain team developed the Neural Architecture Search Network (Nasnet), which has two key functions, normal cell, and reduction cell. Nasnet first performs its operations on a small dataset and then transfers its block to a larger dataset to get a greater mean average precision (mAP) [45]. The network learned a powerful feature to detect images and the input image sizeis 224×224 pixels. The architecture of NasNet mobile is illustrated in Fig. 5. In this figure, the convolution cells are used to improve classification performance and reduce processing costs. Normal and reduction cells are developed with these convolutional cells to extend the use of NasNet for images of any size.

Figure 5: NasNet mobile architecture

Darknet-53 is a 53-layered convolutional neural network. In this model, the feature extractor is mainly composed of a series of convolutional layers at the dimension of 1×1 and 3×3 [46]. A batch normalization (25) layer and a Leaky rectified linear activation unit (ReLU) layer followed each convolution layer. It can speed up network convergence and prevent over-fitting after the convolutional layer. The activation function (LeakyRelu) is a linear unit with leakage correction, which increases the network model’s nonlinearity [47]. The architecture of fine-tuned Darknet-53 network is illustrated in Fig. 6.

Figure 6: Fine-tuned darknet53 architecture for apple leaf diseases classification

We utilized the transfer learning process for deep learning feature extraction and trained both fine-tuned models. For the NasNet Mobile, a fully connected layer is removed, and added a new layer. After that, TL-based training is performed, and extract features from the global average pooling layer and obtained a feature vector of dimensional N×1056. For Darknet-53, the conv53 layer is removed and added a new layer. After that, TL-based training is performed, and extract features from the average pooling layer of the newly trained model. The obtained feature vector of dimensional N×1024. In the training process, several hyperparameters are utilized with fixed values such as a learning rate of 0.001, momentum is 0.7, epochs of 50, mini-batch size is 32, training function is Adam, and loss function is cross entropy.

3.6 Proposed Parallel Features Fusion

In this work, we proposed a parallel features fusion approach named Parallel Priority (PP). The main motivation behind using parallel fusion is to reduce the feature vector with more important features. This approach is based on three important steps. In the first step, make the equal size of both vectors based on the entropy value padding. Then, the entropy value is computed from a large-size feature vector and utilized for padding of a smaller feature vector. Mathematically, the entropy of large size feature vector is calculated as follows:

ha(λ)=11−alog⁡(∑i=1nPia)(3)

Here λ denotes the feature points of the higher dimensional feature vector, Pia is the probability of each feature point, and n denotes the total number of features. Based on the h, padding is performed for smaller-size feature vectors as follows:

padding=(FV,h)←FV⏞(4)

where, FV⏞ denotes the large-size feature vector and FV is the small size feature vector. The feature vector obtained after padding is denoted by FV~. In the second step, define the priority of feature values based on higher feature values.

vector=priority(FV~,FV⏞)(5)

where the function priority selects the higher value for each i and j features first then so on. In the third step, the mean value is computed for the entire vector, and define a threshold function for the final fusion is as follows:

Th={Fus(k)forvector(i)≥μIgnore,Elsewhere(6)

where, Fus(k) is a final fused vector of dimensional N×1290. The obtained fused vector is analyzed based on hits and trails, and it is noted that several redundant features are present; therefore, it is important to remove the irrelevant features. For this purpose, we proposed an improved artificial Butterfly Algorithm.

3.7 Improved Artificial Butterfly Optimization

In this article, we proposed an improved butterfly optimization algorithm (BOA) to select the best features. Originally, the BOA [48] is divided into three phases: the initialization phase, the searching phase, and the concluding phase. At first, the initialization of the butterfly swarm has been processed followed by the initialization of the cost function [49]. Artificial butterflies in the search space move to new positions after the iteration begins, and their cost values are attained. After that, butterflies produce the following fragrance at their respective locations.

aix+1=aix+(b2×c∗−aix)×di(7)

From this equation c∗ is the best iteration x solution, aix is the solution vector for ith butterfly. The b is the random constant between 0 and 1 di is the fragrance of ith butterfly. The algorithm for local search describes as follows:

aix+1=aix+(b2×ajx−akx)×di(8)

where in the search space, ajx is the jth and akx is the kth element of butterfly [50]. Although the butterfly optimization approach performs well when exploring optimal values, it has a disadvantage in convergence. A new strategy for adjusting the essential parameters of the butterfly optimization algorithm which increases the convergence speed. For solving this problem, key parameters for vectors of the butterfly optimization algorithm which are V=[p,q,r] is considered based on chaos theory. Chaos theory models highly sensitive dynamic systems that are usually affected by even minor changes. This feature generates points with a higher distribution and simpler complexity to increase the distribution of points in the search space. The following is a general form of chaos theory; Vj+1i=f(Vji)andj=1,2,3…,k, where the k is map dimension and f(Vji) is the chaos model. The logistic mapping for all parameters of vectors are calculated as:

pi+1=γpi(1−pi)(9)

qi+1=γqi(1−qi)(10)

ri+1=γri(1−ri)(11)

In the above equations of logistic mapping, i is the iteration number of every parameter of vectors and γ is the control parameter [51]. The cubic support vector machine (SVM) is utilized as a fitness function and the cost is computed for every iteration based on the error rate. The feature vector obtained through Eqs. (9)–(11), is further passed to the next stage of processing in which features are further cleaned based on K-Means Clustering and Euclidean Distance. Through K-Means Clustering, two sets of features are defined as follows:

e(m1,m2)=∑i=1M∑j=1Nγ(fi∈CLk)||fi−mk||(12)

where m1 and m2 are two defined feature clusters. Using both clusters, Euclidean Distance is computed among features and selects the closer distance features.

D=∑k=1K(x[f]−x′[f])2(13)

where D denotes the distance formulation and K represents the total features m1 and m2 clusters. The fine-KNN is utilized for the fitness function and computed error after each iteration. This process returns a feature vector of N×642 that is finally classified using machine learning classifiers.

4  Results and Discussion

The experimental process of the proposed framework is conducted on augmented Plant Village Apple leaf images. Several classifiers have been utilized and select the best classifier based on maximum accuracy. A 60:40 approach has opted for the training and testing of the proposed framework performance. All the results are computed using 10-Fold cross-validation. Seven performance measures have been implemented: sensitivity rate, precision rate, F1-Score, accuracy, false positive rate (FPR), area under the curve (AUC), and testing time of the classification process. The framework is implemented on MATLAB 2021b using a Personal Computer Corei7 with 16GB of Ram and an 8GB graphic processing unit.

The proposed framework results are presented in this section. Several experiments are conducted such as i) experiment 1 computes results using Nasnet Mobile deep features; ii) experiment 2 computes the results of Darknet-53 deep features; iii) experiment 3 is computed the results of the proposed fusion approach, and iv) experiment 4 computed the results of the proposed feature selection approach. The results are computed using several classifiers, and the best classifier sensitivity rate is also verified through a confusion matrix.

Experiment No 1: Deep NasNet Mobile features are retrieved and performed classification in this experiment. The classification findings from this experiment are shown in Table 1. The best accuracy in this table is 95.7% for the Cubic SVM classifier, and the computing time was 39.638 s. This classifier has a sensitivity rate of 95.9%, which is higher than the rest of the classifiers included in this table. The Cubic SVM’s confusion matrix, shown in Fig. 7, illustrates the accuracy of each apple class’s predictions. According to this statistic, the apple healthy class has a higher error rate of 5.4%.

Figure 7: Confusion matrix of cubic SVM for experiment no 1

Experiment No 2: In this experiment, DarkNet-53 deep features are extracted, and performed apple leaf disease classification. Table 2 presents the classification results of this experiment. In this table, the best obtained accuracy is 100.0% for the Cubic SVM classifier whereas the computational time is 53.909 (S). The sensitivity rate of this classifier is also 100.0%, which is better than the rest of the classifiers listed in this table. Fig. 8 illustrates the Cubic SVM confusion matrix that confirms each apple class’s correct prediction accuracy. Compared with Table 1, it is noted that the accuracy rate of experiment 2 is better than experiment 1, but the time of the first experiment is almost half for all listed classifiers.

Figure 8: Confusion matrix of cubic SVM for experiment no 2

Experiment 3: Deep extracted features are fused in this experiment using the proposed features fusion approach. The dimension of the fused feature vector is N×1290. This feature vector is passed to selected classifiers of this work and results are given in Table 3. In this table, the best-obtained accuracy of 99.6% for Cubic SVM, whereas the computational time is 198.48 s. The sensitivity rate of this classifier is 99.7%. Fig. 9 illustrates the confusion matrix of Cubic SVM for experiment 3. Compared with Tables 1 and 2, this experiment’s accuracy is improved, but time is increased which is the limitation of this step.

Figure 9: Confusion matrix of cubic SVM for experiment no 3

Experiment 4: The proposed improved optimization algorithm is applied to fused feature vector and obtains maximum accuracy of 99.7%, as given in Table 4. The sensitivity rate of Cubic SVM is 99.8%, whereas the precision and F1-Score values are 99.7%. The computational time of Cubic SVM is 32.972 s. Compared with the first three experiments, the time is significantly reduced and accuracy is also improved. Moreover, the sensitivity rate of Cubic SVM is also verified by a confusion matrix, illustrated in Fig. 10. Overall, the proposed features fusion technique and improved optimization algorithm give better accuracy.

Figure 10: Confusion matrix of cubic SVM for experiment no 4

A comprehensive comparison is also conducted of the proposed method with other deep models, as given in Table 5. In this table, accuracy and classification time during the testing process are noted. For example, the accuracy of fine-tuned Darknet-53 is maximum but the time is 53.909 s. On the other side, the accuracy of the proposed fusion approach is 99.6% but the time is 198.48 s which is higher than the previous methods (as mentioned in this table). The proposed framework obtained an overall accuracy of 99.8% whereas the time is 32.978 s. The noted time is almost half than the above-mentioned features which shows the significance of the proposed approach.

5  Conclusion

In this article, we proposed an automated framework based on feature fusion and an improved optimization algorithm. Data augmentation is used to increase the number of training samples. Transfer learning was used to fine-tune and train two pre-trained models, Darknet-53 and NasNet Mobile. A parallel priority approach was proposed for the fusion of deep features of both models. Furthermore, an improved optimization algorithm is proposed, which selects the best features for final classification. Overall, we concluded that the proposed fusion method improves classification accuracy while increasing computational time. As a result, the enhanced selection algorithm reduces classification time while maintaining accuracy. A CNN-based network will be proposed in the future, and the hyperparameters will be optimized using Bayesian optimization. Furthermore, more optimal feature selection techniques will be used [52,53] and employed some active learning methods [54].

Funding Statement: This work was supported by “Human Resources Program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resources from the Ministry of Trade, Industry & Energy, Republic of Korea. (No. 20204010600090).

Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.

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