You are currently viewing a new version of our website. To view the old version click .
MDPIMDPI
Search all articles
Electronics
Download PDF
  • Article
  • Open Access

6 February 2023

Design of Efficient Methods for the Detection of Tomato Leaf Disease Utilizing Proposed Ensemble CNN Model

and
1
Department of Computer Engineering, Yozgat Bozok University, Yozgat 66900, Turkey
2
Department of Computer Engineering, Erciyes University, Kayseri 38280, Turkey
*
Author to whom correspondence should be addressed.
Electronics2023, 12(4), 827;https://doi.org/10.3390/electronics12040827
This article belongs to the Section Artificial Intelligence
Version Notes
Order Reprints

Abstract

Early diagnosis of plant diseases is of vital importance since they cause social, ecological, and economic losses. Therefore, it is highly complex and causes excessive workload and time loss. Within the scope of this article, nine tomato plant leaf diseases as well as healthy ones were classified using deep learning with new ensemble architectures. A total of 18.160 images were used for this process. In this study, in addition to the proposed two new convolutional neural networks (CNN) models, four other well-known CNN models (MobileNetV3Small, EfficientNetV2L, InceptionV3 and MobileNetV2) are used. A fine-tuning method is applied to the newly proposed CNNs models and then hyperparameter optimization is performed with the particle swarm optimization algorithm (PSO). Then, the weights of these architectures are optimized by the grid search method and triple and quintuple ensemble models are created and the datasets are classified with the help of the five-fold cross-validation. The experimental results demonstrate that the proposed ensemble models stand out with their fast training and testing time and superior classification performances with an accuracy of 99.60%. This research will help experts enable the early detection of plant diseases in a simple and quick manner and prevent the formation of new infections.

1. Introduction

Day by day, the world population is increasing. In proportion to this population growth, the need for agriculture increases expeditiously. The products obtained from agriculture can meet many human needs, such as eating, dressing, and heating. These products are also very essential for the import and export issues in a country. Income from agriculture plays an important role in the development and growth of the country’s economy. In this respect, the products obtained from plants must be of high quality, and the plants must be protected from diseases in order to have high-quality products. There are many factors that cause plant diseases, such as adverse environmental conditions, fungi, bacteria, and viruses. Diseases seen in the plant can damage the vital functions of the plant, such as photosynthesis, pollination, fertilization, and germination. Therefore, for treatment, it is very important to detect diseases as early as possible.
Today, instead of relying only on an outside expert, it is possible to use technological devices to determine whether a plant has a disease and what kind of disease it is. Processes such as object detection, classification, image processing, and artificial intelligence algorithms provide extremely good outcomes as the quality of image acquisition by technological devices improves. Machine learning (ML) and deep learning (DL) outperform traditional optimization and prediction methods. First, these methods can automatically learn from large amounts of data, while traditional methods require manual feature extraction and are limited by data size. Second, ML and DL models can generalize well to unseen data, unlike traditional methods. Thirdly, unlike traditional methods, ML and DL models can learn complex, non-linear data relationships. Thus, ML is better at handling multiple variables, especially complex interactions. Especially in recent years, artificial intelligence applications have been widely used in various application areas such as communication, construction, magnetic, physics, and biomedical systems [1,2,3,4,5]. In this context, accurate, timely detection and classification of plant diseases are of great importance [6]. Advances in artificial intelligence research now enable automatic plant disease detection from raw images [7].
A considerable amount of research has been conducted in the field of disease detection in plants so far. The studies were generally carried out using ready-made data sets, ready-made models, and libraries. An algorithm on the image segmentation technique used in the automatic detection and classification of diseases in plant leaves was presented by Singh and Misra [8]. The average success rate of their study was conducted on five different diseases by using a genetic algorithm in the disease detection stage, which is 97.6%. Zhang et al. performed disease detection on the cucumber leaf database [9]. In this study, they extracted shape and color features to detect diseases by segmenting diseased leaves with the k-means clustering algorithm. They obtained an 86% accuracy rate by classifying these diseased leaves with the sparse representation method. Convolutional neural network (CNN) models were developed by Ferentinos to detect and diagnose plant diseases with deep learning methods [10]. The training of the models was performed on the data set containing 87,848 images. In addition, this dataset includes 58 (plant, disease) combinations of 25 different plant species. The results of the study show that it has a success rate of 99.53%.
Lu et al. [11] proposed a disease detection method in rice based on deep CNN models. The training stage was performed using a dataset consisting of 500 images of healthy and diseased rice leaves. The trained model detects 10 different common diseases. According to the experimental results reported, the success rate in detecting the disease was 95.48%.
Cruz et al. [12] submitted a certain bacterial species and proposed a method for detecting diseased olive leaves. It is intended to detect this disease in olive leaves by retraining the previously trained deep learning model with transfer learning. As a result, it was seen that the success rate was approximately 98.60%. Mohanty et al. used the deep CNN model for the analysis of plant leaf diseases [13]. In this study, two models based on deep learning (AlexNet and GoogleNet) were selected. Using the dataset of 54,306 images of diseased and healthy plant leaves, these two CNNs were trained to classify crop types and disease status for 38 different classes, including 14 plant species and 26 diseases, with over 99% success. Hanson et al. presented an approach to detect and classify disease in leaves using deep learning techniques. As a result, success rates between 91% and 98% were obtained for separate class tests in this study. The final overall success of the trained model was 95% [14]. Namin et al. A new deep learning structure has been proposed to recognize the plant phenotype by combining the advantages of the CNN and LSTM (Long Short-Term Memory) algorithms [15]. According to the developed algorithm, deep features were extracted from the images from the CNN model, and the output was loaded into an LSTM unit to create a series of models. The results of the study showed that the sequence model significantly increased the accuracy from 68% to 76.8% when the handcrafted features were used when using CNN, and the addition of temporal information with LSTM increased the accuracy to 93%. Too et al. The use of different deep learning models was evaluated for the classification of 38 different classes, including the VGG-16 network, Inception V4, ResNet with 50, 101, and 152 layers, and DenseNet with 121 layers. The PlantVillage dataset included healthy and diseased leaf images of 14 different plants [16]. The DenseNets 17 model’s success gradually increased as the number of rotations increased, and there were no signs of overtraining or performance degradation. DenseNets used very few parameters and a reasonable computation time to achieve their state-of-the-art performance. This model outperformed other models with a test success rate of 99.75%. Models are trained using Theano and Keras backends.
There are numerous studies on the disease detection of tomato plants in the literature [17,18,19]. Rajasekaran Thangaraj et al. used transfer learning and fine tuning to modify the Xception architecture to yield good results on a smaller dataset [20]. As the dataset, 50,306 images from the PlantVillage dataset, which contains a total of 14 different disease classes and healthy classes, were used. Before the classification step, the model Adam, SGD, and RMSProp optimization methods were tested, and the best accuracy of 99.55% was achieved with Adam.
Patrick M. K. et al. [21] utilized the AlexNet, GoogleNet, and Gabor CapsNet CNN architectures for the classification of both tomato leaf diseases and citrus plant diseases. They found that the model presented gave poor results in the classification of images with mixed backgrounds.
Aliyu Muhammad A. et al. used ML to analyze pathologically local symptom lesions in tomato and potato leaves [22]. The authors experimented with KNN, naive Bayes, and SVM as classifiers and obtained the highest accuracy of 99.67% using SVM.
Automatic identification of tomato plant diseases is the main motivation for this work. In terms of filling a void in the field, the contributions of this paper include the following:
  • Diagnosing plant diseases is essential for determining the quality and health of a crop, as it evaluates numerous parameters such as crop yield, yield capacity, grain quality, and nutrient retention.
  • Suggesting new CNN models and creating new ensemble structures with the proposed CNNs models.
  • The stratified 5-fold cross-validation method is used for classification problems, and the folds are selected so that each fold contains roughly the same ratios as the target class.
  • Obtaining a higher accuracy ratio compared to the studies carried out in the literature. In particular, the architectures commonly used in the literature for the detection of tomato disease are selected and proposed CNN models and ensemble models are presented. After these systems are designed, it is intended to contribute to the literature by increasing the accuracy of the models.
  • Automatically identifying tomato plant diseases.
  • Achieving fast training and testing times and superior classification performance.
The structure of the paper is provided below. Section 2 provides a comprehensive explanation of the deep learning methods, proposed CNN models, and other CNN models available in the literature, as well as a summary of the employed algorithms. In addition, evaluation parameters for the utilized deep learning models are described in detail. In Section 3, the work and outcomes of deep learning models are detailed, along with evaluation findings such as ROC, accuracy, recall, precision, and F1-scores, as well as cross-validation outcomes. Additionally, a comparison is made between the proposed methods and those of other studies employing the same dataset, and results are presented. Section 4 contains the conclusion.

2. Material and Methods

In this section, the methods used to perform the classification of tomato plant leaf disease detection, optimization methods, evaluation parameters, and dataset are briefly explained. The procedure of the study is shown in Figure 1.
Figure 1. Procedure of study.

2.1. Deep Learning

Deep learning is a methodology for ML that employs numerous nonlinear information processing layers and is a method for feature extraction, pattern analysis, and classification in supervised and unsupervised learning [23,24].
The greatest benefit of this method is that the features are taken directly from the raw data, and the classification algorithm trains itself automatically based on the collected characteristics [25]. Raw pictures are incompatible with conventional image categorization and processing methods. This necessitates several preprocessing techniques and feature extraction algorithms. This is an extremely difficult and experience-required process. In the beginning, researchers aimed to eliminate human feature extraction in favor of the most effective automatic feature extraction. Despite the fact that this learning process was implemented using artificial neural networks with several layers, there were substantial shortcomings in areas such as image processing. To accomplish this, scientists developed the convolutional neural network (CNN), which is compatible with the way images are constructed.

2.1.1. Proposed Convolutional Neural Networks (CNN)

In this part, several new CNN models are designed, and the two models with the highest accuracy among these models are selected and used in the classification process. The two CNN models proposed within the scope of this study contain fewer parameters than the existing models in the literature and also have less storage space. This is because the proposed CNN models are designed with fewer layers. An example of CNN architecture is shown in Figure 2.
Figure 2. CNN architecture.
The architectural structure of the proposed CNN1 and CNN2 models’ layers and parameter values used are given in Table 1. We used a typical CNN architecture with 4 (CNN1) and 5 (CNN2) sequential Convolution/Max-pooling modules, followed by a flattening layer, a fully connected layer, a dropout layer, and a SoftMax layer.
Table 1. Architecture details of CNN1 and CNN2.
In general, CNNs are able to achieve good performance with relatively small model sizes due to their ability to learn features directly from the input data and their use of shared weights in the convolutional layers. This can allow them to be trained faster and require less storage compared to models with a larger number of parameters, such as fully connected deep neural networks. Thus, the objectives of this section are to design new CNN models that require less storage space and training time than the other models proposed in the literature.

2.1.2. CNN Models

Convolutional neural networks (CNNs) are the type of deep learning models that are particularly well-suited for image classification tasks. They are able to learn features directly from the input data and have been successful in a wide range of applications, including object recognition, face detection, and image generation.
MobileNet
Howard et al. developed the MobileNet architecture to run vision applications on embedded and mobile platforms [26]. The proposed algorithm is based on convolutional layers that are depth-wise distinct. Even though the networks have fewer parameters than other deep networks, they are still capable of producing successful results. This model is powered by convolutional layers with granular separation. Deeply distinct convolution layers map spatial and inter-channel correlations in input image feature maps. In this study, MobileNetV3Small and MobileNetV2 architectures are used for classification.
EfficientNet
EfficientNet is a network architecture for deep learning that was developed in 2019 [27]. This architecture illustrates the relationship between three terms that significantly impact the performance of architectures for deep networks. These are defined as depth, width, and resolution. This architecture uses the composite scaling method. The grid search algorithm is this method’s first step. This algorithm enables the network to correlate between scaling sizes of varying sizes.
Inception
The InceptionV3 model is an architecture that was created by enhancing previously developed Inception architectures. It is designed to accomplish more with fewer resources than its predecessors. The GoogleNet model is a deep CNN network that was proposed by Google researchers in 2014 and achieved 93.3% accuracy in the top five in ILSVRC [28]. The GoogleNet architecture, which consists of 22 layers and utilizes a network at the network layer as opposed to conventional sequential processing, is based on the Inception model. Parallel computing is used to calculate the pooling, major convolution, and minor convolution layers. Individual convolutions are used to reduce the dimensionality of features in the Inception architecture. Due to this architecture’s size reduction and parallelism, the number of parameters and operations has been drastically reduced; as a result, these features conserve memory and reduce computational costs [29].

2.2. Dataset

The PlantVillage dataset is a well-known dataset in the field of plant disease detection and consists of a large number of images of plant leaves along with labels indicating the type of disease (if any) present in the image. Using this dataset to train a CNN model for tomato plant disease detection can be a useful way to demonstrate the effectiveness of the model in detecting various types of diseases and can help establish a baseline for comparison with other approaches.
In this study, the dataset composed of images of tomato plants gathered from the PlantVillage dataset “https://data.mendeley.com/datasets/tywbtsjrjv (accessed on 3 March 2022)” was used [30]. This dataset consists of images of plant leaves classified into ten categories as healthy and diseased for the tomato plant species and includes a total of 18,160 leaf images. The images are in the RGB color space and have 256 × 256 pixels. Example images of each class are shown in Figure 3. The total numbers of training, validation, and test images used for each class are given in Table 2.
Figure 3. Healthy and diseased images: (a) Bacterial spot, (b) Early blight, (c) Healthy, (d) Late blight (e) Leaf mold, (f) Septoria leaf spot, (g) Spider mites (two-spotted spider mite), (h) Target spot, (i) Tomato mosaic virus, and (j) Tomato yellow leaf curl virus.
Table 2. The Total Number of training, validation, and test images are used for each class.

2.3. Tuning

Identifying the optimal (hyperparameter) settings for each model is essential for a bias-free evaluation of a model’s predictive ability. In this study, the algorithms used for tuning are given below.

2.3.1. Fine Tuning

Fine tuning refers to the process of training and applying a model that has been trained on one set of data to another set of data. Fine tuning is a concept in transfer learning. To accelerate learning, CNN models are fine-tuned to identify and classify nine plant disease categories using four pre-trained models on the ImageNet dataset. Fine tuning a model involves adjusting its layers in order to improve its performance. The goal of fine tuning is to find the optimal model that gives the best performance on the planned task, and this method is used in our study to adjust our proposed models.
Figure 4 shows the implementation of a fine-tuned model for CNN. In this section, firstly, the weights of the CNN model are fixed. Then, the updating and fine tuning of the weights of the last two layers is carried out using transfer learning with the VGG-16 network.
Figure 4. Implementation of fine-tuned model for CNN [31].

2.3.2. Hyperparameter Optimization with Particle Swarm Optimization (PSO)

The different parameters utilized to train the neural network model in a deep learning model are referred to as “hyperparameters”. In order to improve the performance of a neural network, these hyperparameters are tuned as if they are buttons, resulting in an optimized model. The hyperparameters include the number of hidden layers, the number of units or nodes in the hidden layer, the learning rate, the dilution rate, the epochs or iterations, adam, nadam, adagrad, rmsprop, etc., and optimizers such as ReLu, sigmoidal, etc.
A deep learning model with fine-tuned hyperparameters will provide the most effective model performance optimizers, activation functions, learning coefficients, etc. It is the procedure of determining the value of hyperparameters, such as in manual search, grid search, random search [32,33], and Bayesian optimization [34,35]. The scheme of PSO is shown in Figure 5. In this paper, the PSO algorithm is utilized for hyperparameter tuning.
Figure 5. Scheme of PSO [36].

2.3.3. Grid Search

In the grid search approach, there is a grid space where different possible values of hyperparameters are defined. After testing all combinations in the search space, the best-performing point is selected. Therefore, certain ranges are determined for these parameters. The model is trained with values in the determined ranges, and the best combination group obtained by observing the results is determined as the hyperparameters of the model [30]. Weight optimization with a grid search algorithm is given in Figure 6.
Figure 6. Weight optimization with grid search algorithm.

2.4. Evaluation Metrics

The metrics described in the equations given below are commonly used to evaluate the performance of a classification model, such as a convolutional neural network (CNN) used for plant disease detection. These metrics provide different insights into the performance of the model and can help identify strengths and weaknesses in its ability to classify images correctly.
The confusion matrix is used in this study to assess the performance of the proposed systems.
  • The true-positive (tp) value represents the number of correctly identified infected image samples.
  • False-positive (fp) indicates the number of samples incorrectly identified as infected.
  • True-negative (tn) identifies correctly classified non-infected plants.
  • False-negative (fn) identifies individuals who are incorrectly classified as healthy.
Depending on the earlier statuses, statistical measures are used to have a complete vision of the model’s performance. Performance measures such as accuracy, recall, precision, and F1-score are used using the confusion matrix. Mathematical expressions related to these performance criteria are given below [37]:
A c c u r a c y = t n + t p t n + t p + f n + f p
R e c a l l = t p t p + f n
P r e c i s i o n = t p t p + f p
F 1 -Score = 2 × P r e c i s i o n × R e c a l l P r e c i s i o n + R e c a l l

3. Experimental Results

In this section, detailed modeling studies with deep learning architectures are given for each problem, using the data in the original data set created for tomato plant disease prediction and classification problems. The configuration of the hardware and size of the used models are given in Table 3 and Table 4, respectively.
Table 3. Configuration of the hardware.
Table 4. Size of used models.
In this study, we use the cross-validation methods to eliminate the over-learning issue [38], and the dataset is separated into training and testing groups. The data utilized for training and testing groups are distinct. Thus, in the testing phase of the algorithm, data distinct from those used in the training phase are utilized. In other words, the algorithm is tested with data that it does not know. Thus, a more realistic testing procedure and accuracy rate are obtained.
Tuning a model involves adjusting its hyperparameters in order to improve its performance. This can be achieved by manually adjusting the hyperparameters or by using an optimization algorithm such as the particle swarm optimization (PSO) algorithm described in the study. The goal of tuning is to find the optimal set of hyperparameters that gives the best performance on the planned task.
Using two CNN models and four ready-made models proposed in this study, the detection of tomato plant disease is carried out by the 5-fold cross-validation method. The proposed CNN1 and CNN2 models were fine-tuned in order to improve their accuracy, as shown in Figure 4. In addition, the hyperparameter values of the models are optimized with the help of the PSO algorithm (See Figure 5) for the hyperparameter setting of these two models, as shown in Table 5.
Table 5. Hyperparameters of the models.
In this study, ensemble models are proposed for the methods given above to classify 10 classes of tomato plant diseases. In the ensemble models proposed here, a method based on weighted average ensemble learning is used and given in Figure 6. The best combinations of base models in the proposed system are found using the grid search method, and the effect of each base model on the average recall score is presented as weighted, unweighted, and ideal. In addition, the accuracy results are given in Table 6 with other used models.
Table 6. Performance metrics of deep learning architectures.
When examining Table 6 for the application conducted within the scope of this study, four distinct CNN models (from the literature) are offered for comparison in addition to the two proposed models. The results for all classes of tomato plant leaf disease in this study are given in Figure 7 separately. When Figure 7 is examined, performance results (accuracy, precision, recall, and F1-Score) are given on a class basis. In addition, it is seen that each class produces values very close to the average success. It is seen that ROC and AUC values are generally one. When these metrics are evaluated, it is seen that the different sample sizes on a class basis do not negatively affect the results of the study.
Figure 7. Performance plot of the CNN models.
In this part, models (CNN1–CNN2) are proposed that take up significantly less space (5.1 and 6 MB) than models in the literature for two parameters (space and training/testing time). These models reduce training time from approximately 16 h to nearly 1.5 h, and the testing times of the two CNN models are faster than other models. The low value of the store parameter reveals the significance of the offered models, particularly when portable systems are considered in daily life. In addition, the accuracy of the presented models is roughly equivalent to that of the existing models. Compared to other models, the CNN2 model presented within the scope of this study has a relatively small number of total parameters. The performance plot of the CNN models is shown in Figure 7.
The values in the tables given above are created by calculating the arithmetic average of the classification reports obtained as a result of the five-fold cross-validation of each CNN model. The accuracy and loss graphs of the CNN models proposed within the scope of the study are obtained for k = 5 and shown in Figure 8 and Figure 9. In addition, the confusion matrix of the models is given in Table 7, and the values obtained are the average values taken by calculating k = 5-fold. When the accuracy and loss graphs are examined, over- or under-learning did not occur in the two proposed CNN models, and early stopping was not completed in the applications within the scope of this study. The values in the table are rounded, with values less than one percent disregarded.
Figure 8. Accuracy and loss graph of CNN1 with 5-fold cross-validation.
Figure 9. Accuracy and loss graph of CNN2 with 5-fold cross-validation.
Table 7. Confusion matrix of used CNN models.

3.1. The Framework of Proposed Ensemble Models

In this study, ensemble models are presented using newly proposed CNN models and existing CNN models available in the literature. The ensemble models with the highest accuracy rate within the scope of the study are listed below, along with their results.
A voting ensemble is a deep-learning ensemble model that combines the predictions of numerous other models. We use the voting method for the proposed ensemble models.
It is a technique that can be used to enhance model performance, ideally achieving a higher level of performance than any individual model used in the ensemble. A voting ensemble functions by combining the forecasts of multiple models. It is applicable to both classification and regression.
In hard voting, also known as majority voting, each classifier votes for a class, and the class with the most votes wins. In statistical terms, the ensemble label prediction is the mode of the distribution of individual label predictions. Max-voting, which is typically employed for classification problems, is one of the most straightforward methods for combining predictions from multiple ML algorithms. Each base model makes a prediction and votes for each sample in maximum voting. The final predictive class is composed of only the sample class with the most votes.
Maximum and hard voting methods are employed by using CNN models to generate ensemble models for this study.

3.1.1. Model I, 3-EnsCNNs (MobileNetV3Small, EfficientNetV2L, CNN2)

In this paper, a new ensemble model, Model I, is presented using the MobileNetV3Small, EfficientNetV2L, and CNN2 models. The results of training the ensemble structure created within the scope of the study using the proposed dataset are given below. The classification report obtained for the proposed ensemble model is presented in Table 8. In addition, the confusion matrix and ROC diagram of the model are given in Figure 10.
Table 8. Classification report of proposed 3-EnsCNNs (Model I).
Figure 10. Confusion matrix and ROC graph of Model I.

3.1.2. Model II, 3-EnsCNNs (MobileNetV3Small, EfficientNetV2L, CNN1)

In this section, a new ensemble model II is presented using the MobileNetV3Small, EfficientNetV2L, and CNN1 models. The classification report obtained for the proposed ensemble model is presented in Table 9. In addition, the confusion matrix and ROC diagram of the model are given in Figure 11.
Table 9. Classification report of proposed 3-EnsCNNs (Model II).
Figure 11. Confusion matrix and ROC graph of Model II.

3.1.3. Model III, 3-EnsCNNs (InceptionV3, CNN2, and MobileNetV2)

In this section, a new ensemble model III is presented using the InceptionV3, CNN2, and MobileNetV2 models. The classification report obtained for the proposed ensemble model is presented in Table 10. In addition, the confusion matrix and ROC diagram of the model are given in Figure 12.
Table 10. Classification report of proposed 3-EnsCNNs (Model III).
Figure 12. Confusion matrix and ROC graph of Model III.
In this part, Table 11 display the values obtained for the proposed triple and quintuple ensemble models. Examining Table 11, the unweighted average (hard voting) produced better results than the weighted average (maximum voting).
Table 11. Accuracy results of 3-EnsCNNs models.

3.1.4. Model IV, 5-EnsCNNs (MobileNetV3Small, EfficientNetV2L, CNN2, CNN1, InceptionV3)

In this section, a new ensemble model IV is presented using the MobileNetV3Small, EfficientNetV2L, CNN2, CNN1, and InceptionV3 models. The classification report obtained for the proposed ensemble model is presented in Table 12. In addition, the confusion matrix and ROC diagram of the model are given in Figure 13.
Table 12. Classification report of proposed 5-EnsCNNs (Model IV).
Figure 13. Confusion matrix and ROC graph of Model IV.
In this section, Table 13 display the values obtained for the proposed triple and quintuple ensemble models. Examining Table 13, the unweighted average (hard voting) produced better results than the weighted average (maximum voting).
Table 13. Accuracy results of 5-EnsCNNs model.
The confusion matrices of 3-EnsCNNs models and 5-EnsCNNs models are demonstrated in Table 14. With its quick training duration and superior classification performance, the proposed study stands out. Table 15 lists various methods for the classification of tomato leaf diseases and research using datasets from tomatoes. The accuracy rate part of this table displays the individual successes of tomato leaf diseases in the datasets in the literature. The findings of the model with the highest rate of accuracy in investigations using various methodologies, aside from the suggested study, are shown in the table.
Table 14. Confusion matrix of 3-EnsCNNs models and 5-EnsCNNs model.
Table 15. Comparison of the studies.
The accuracy rates obtained using k-fold cross-validation are nearly the same as those discovered in previous studies. This study, which may serve as a model for further research, could be expanded to use a dataset with more data to achieve a greater accuracy rate. Tomato producers can utilize the suggested approach in this way to identify diseases more quickly. A cost-effective calculation can be used to identify diseases in tomato leaves using the proposed models, which have an accuracy of nearly 99.60%. Additionally, it is anticipated that it will aid in the development of future cooperative decision-making systems. Additionally, the CNN classification performances put forth in the presented study demonstrated that analyses with high accuracy are conceivable and that developing a CNN-based mobile application can facilitate analyses in terms of practicality and adaptability.
For farmers and the agricultural sector, plant disease and insect infestation are risky. By destroying plants and production quality, it has a significant negative economic impact. An autonomous system for spotting plant diseases is crucial to the development and improvement of agricultural output. The image-based automatic disease and pest detection system, which uses a variety of image processing and neural network approaches, provides the foundation for this research. Without compromising the performance evaluation criteria, the presented approach can be expanded to compress other well-known pre-trained models, including MobileNet, EfficentNet, and Inception. On a variety of publicly accessible plant villages, training, validation, and testing are carried out. When compared to conventional methods, the research reported in this study produced more favorable results with improved performance. Future research on the automatic plant disease and pest detection approach is primarily focused on enhancing system efficiency by speeding up computation. In accordance with the degree of the impact, it also focuses on additional treatment and prevention strategies. Since this system can be utilized on embedded systems with autonomous drones, the entire system should be implemented in real-time using mobile and web applications.

4. Conclusions

This study aims to investigate and apply several deep-learning models to identify diseases in tomato plants. Nine distinct diseases of tomato plant leaves were used in this context, and the widely available PlantVillage dataset was chosen. Using this data set, modeling studies are conducted by recommending two novel CNN architectures for tomato plant disease prediction and classification, in addition to MobileNet, Inception, and EfficientNet, which are specialized deep learning architectures. CNN models found in the literature have been seen to be useful for classification tasks. However, because of its intricate and substantial architecture, the storage space and inference time are extremely high. These models cannot be applied to edge computing devices due to storage capacity and inference time limitations. The proposed CNN models are used to overcome this issue, and the results are detailed in Section 3. Each architecture included in the study is initially categorized using the 5-fold cross-validation approach. The PSO method is used to perform hyperparameter adjustments in the CNN models proposed here, and the VGG16 model is used for the fine-tuning method. The proposed model is one of the best models out of all the applied architectures in the initial modeling tests utilizing the data set generated for the classification of plant species. As a consequence of comprehensive experimental studies, four novel ensemble models are suggested for the optimal topologies for each categorization task. According to the ensemble model results, each model successfully passes the test with an accuracy of 99.60%. Within the parameters of the investigation, the obtained ROCs, a confusion matrix, and classification reports are presented. The findings demonstrate that a significant amount of data for deep learning boosts both the model’s learning success and the test success, and it may be employed with excellent results particularly in classification tasks using image data. This disease prediction model’s future applicability can be expanded through the incorporation of mobile agent-based feature selection and classification algorithms, which are capable of selecting the most important features and predicting plant diseases of various types. In addition, ML classifiers can be replaced by the final layers of the proposed CNN model. Additionally, images can be enhanced during pre-processing to improve the results.

Author Contributions

Writing—original draft, H.U.; writing—review & editing, H.U. and V.A.; visualization, H.U.; supervision, V.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data in the work is available and collected in the literature which is referenced in the manuscript.

Acknowledgments

A significant part of this paper includes the Doctorate Thesis data of Hasan ULUTAŞ.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Yu, Y.; Samali, B.; Rashidi, M.; Mohammadi, M.; Nguyen, T.N.; Zhang, G. Vision-based concrete crack detection using a hybrid framework considering noise effect. J. Build. Eng. 2022, 61, 105246. [Google Scholar] [CrossRef]
  2. Şahin, M.E.; Ulutaş, H.; Yuce, E. A deep learning approach for detecting pneumonia in chest x-rays. Avrupa Bilim Ve Teknol. Derg. 2021, 28, 562–567. [Google Scholar] [CrossRef]
  3. Kaloxylos, A.; Gavras, A.; Camps, D.; Ghoraishi, M.; Hrasnica, H. AI and ML–Enablers for beyond 5G Networks. Zenodo Tech. Rep. 2020, 1–145. [Google Scholar] [CrossRef]
  4. Yu, Y.; Liang, S.; Samali, B.; Nguyen, T.N.; Zhai, C.; Li, J.; Xie, X. Torsional capacity evaluation of RC beams using an improved bird swarm algorithm optimised 2D convolutional neural network. Eng. Struct. 2022, 273, 115066. [Google Scholar] [CrossRef]
  5. Spantideas, S.T.; Giannopoulos, A.E.; Kapsalis, N.C.; Capsalis, C.N. A deep learning method for modeling the magnetic signature of spacecraft equipment using multiple magnetic dipoles. IEEE Magn. Lett. 2021, 12, 1–5. [Google Scholar] [CrossRef]
  6. Park, H.; Eun, J.S.; Kim, S.H. Image-based disease diagnosing and predicting of the crops through the deep learning mechanism. In Proceedings of the 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea, 18–20 October 2017; pp. 129–131. [Google Scholar]
  7. Sardogan, M.; Tuncer, A.; Ozen, Y. Plant leaf disease detection and classification based on CNN with LVQ algorithm. In Proceedings of the 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, Bosnia and Herzegovina, 20–23 September 2018; pp. 382–385. [Google Scholar]
  8. Singh, V.; Misra, A.K. Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 2017, 4, 41–49. [Google Scholar] [CrossRef]
  9. Zhang, S.; Wu, X.; You, Z.; Zhang, L. Leaf image based cucumber disease recognition using sparse representation classification. Comput. Electron. Agric. 2017, 134, 135–141. [Google Scholar] [CrossRef]
  10. Ferentinos, K.P. Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 2018, 145, 311–318. [Google Scholar] [CrossRef]
  11. Lu, Y.; Yi, S.; Zeng, N.; Liu, Y.; Zhang, Y. Identification of rice diseases using deep convolutional neural networks. Neurocomputing 2017, 267, 378–384. [Google Scholar] [CrossRef]
  12. Cruz, A.C.; Luvisi, A.; De Bellis, L.; Ampatzidis, Y. Vision-based plant disease detection system using transfer and deep learning. In Proceedings of the 2017 ASABE Annual International Meeting, Spokane, WA, USA, 16–19 July 2017; p. 1. [Google Scholar]
  13. Mohanty, S.P.; Hughes, D.P.; Salathé, M. Using deep learning for image-based plant disease detection. Front. Plant Sci. 2016, 7, 1419. [Google Scholar] [CrossRef]
  14. Hanson, A.M.G.J.; Joel, M.G.; Joy, A.; Francis, J. Plant leaf disease detection using deep learning and convolutional neural network. Int. J. Eng. Sci. 2017, 5324, 2–4. [Google Scholar]
  15. Namin, S.T.; Esmaeilzadeh, M.; Najafi, M.; Brown, T.B.; Borevitz, J.O. Deep phenotyping: Deep learning for temporal phenotype/genotype classification. Plant Methods 2018, 14, 66. [Google Scholar] [CrossRef] [PubMed]
  16. Too, E.C.; Yujian, L.; Njuki, S.; Yingchun, L. A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric. 2019, 161, 272–279. [Google Scholar] [CrossRef]
  17. Kanda, P.S.; Xia, K.; Kyslytysna, A.; Owoola, E.O. Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks. Plants 2022, 11, 2935. [Google Scholar] [CrossRef] [PubMed]
  18. Saranya, S.M.; Rajalaxmi, R.; Prabavathi, R.; Suganya, T.; Mohanapriya, S.; Tamilselvi, T. Deep Learning Techniques in Tomato Plant—A Review. J. Phys. Conf. Ser. 2021, 1767, 012010. [Google Scholar] [CrossRef]
  19. Sangeetha, R.; Rani, M.M.S. Tomato leaf disease prediction using convolutional neural network. Int. J. Innov. Technol. Explor. Eng. 2019, 9, 1348–1352. [Google Scholar] [CrossRef]
  20. Thangaraj, R.; Anandamurugan, S.; Kaliappan, V.K. Automated tomato leaf disease classification using transfer learning-based deep convolution neural network. J. Plant Dis. Prot. 2021, 128, 73–86. [Google Scholar] [CrossRef]
  21. Kwabena, P.M.; Weyori, B.A.; Mighty, A.A. Gabor capsule network for plant disease detection. Int. J. Adv. Comput. Sci. Appl. 2020, 11, 388–395. [Google Scholar] [CrossRef]
  22. Abdu, A.M.; Mokji, M.M.; Sheikh, U.U. Automatic vegetable disease identification approach using individual lesion features. Comput. Electron. Agric. 2020, 176, 105660. [Google Scholar] [CrossRef]
  23. Sahin, M.E. A Deep Learning-Based Technique for Diagnosing Retinal Disease by Using Optical Coherence Tomography (OCT) Images. Turk. J. Sci. Technol. 2022, 17, 417–426. [Google Scholar]
  24. Sahin, M.E. Deep learning-based approach for detecting COVID-19 in chest X-rays. Biomed. Signal Process. Control 2022, 78, 103977. [Google Scholar] [CrossRef]
  25. LeCun, Y. Deep learning & convolutional networks. In Proceedings of the Hot Chips Symposium, Cupertino, CA, USA, 22–25 August 2015; pp. 1–95. [Google Scholar]
  26. Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; Chen, L.C. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 4510–4520. [Google Scholar]
  27. Tan, M.; Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In Proceedings of the International Conference on Machine Learning, Beach, CA, USA, 10–15 May 2019; pp. 6105–6114. [Google Scholar]
  28. Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7–12 June 2015; pp. 1–9. [Google Scholar]
  29. Garcia-Garcia, A.; Orts-Escolano, S.; Oprea, S.; Villena-Martinez, V.; Martinez-Gonzalez, P.; Garcia-Rodriguez, J. A survey on deep learning techniques for image and video semantic segmentation. Appl. Soft Comput. 2018, 70, 41–65. [Google Scholar] [CrossRef]
  30. Pandian, J.A.; Geetharamani, G. Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network. Mendeley Data 2019, V1, 323–338. [Google Scholar] [CrossRef]
  31. Xu, X.; Shi, B.; Gu, Z.; Deng, R.; Chen, X.; Krylov, A.S.; Ding, Y. 3D no-reference image quality assessment via transfer learning and saliency-guided feature consolidation. IEEE Access 2019, 7, 85286–85297. [Google Scholar] [CrossRef]
  32. Bergstra, J.; Bengio, Y. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 2012, 13, 281–305. [Google Scholar]
  33. Kennedy, J.; Eberhart, R. Particle swarm optimization. In Proceedings of the ICNN’95—International Conference on Neural Networks, Perth, WA, Australia, 27 November–1 December 1995; pp. 1942–1948. [Google Scholar]
  34. Klein, A.; Falkner, S.; Bartels, S.; Hennig, P.; Hutter, F. Fast bayesian optimization of machine learning hyperparameters on large datasets. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, Lauderdale, FL, USA, 20–22 April 2017; pp. 528–536. [Google Scholar]
  35. Akay, B.; Karaboga, D.; Akay, R. A comprehensive survey on optimizing deep learning models by metaheuristics. Artif. Intell. Rev. 2022, 55, 829–894. [Google Scholar] [CrossRef]
  36. Yamasaki, T.; Honma, T.; Aizawa, K. Efficient optimization of convolutional neural networks using particle swarm optimization. In Proceedings of the 2017 IEEE Third International Conference on Multimedia Big Data (BigMM), Laguna Hills, CA, USA, 19–21 April 2017; pp. 70–73. [Google Scholar]
  37. Sahin, M.E. Image processing and machine learning-based bone fracture detection and classification using X-ray images. Int. J. Imaging Syst. Technol. 2023, 1–13. [Google Scholar] [CrossRef]
  38. Arlot, S.; Celisse, A. A survey of cross-validation procedures for model selection. Stat. Surv. 2010, 4, 40–79. [Google Scholar] [CrossRef]
  39. Ashqar, B.A.; Abu-Naser, S.S. Image-based tomato leaves diseases detection using deep learning. Int. J. Acad. Eng. Res. 2018, 2, 10–16. [Google Scholar]
  40. Brahimi, M.; Boukhalfa, K.; Moussaoui, A. Deep learning for tomato diseases: Classification and symptoms visualization. Appl. Artif. Intell. 2017, 31, 299–315. [Google Scholar] [CrossRef]
  41. Durmuş, H.; Güneş, E.O.; Kırcı, M. Disease detection on the leaves of the tomato plants by using deep learning. In Proceedings of the 2017 6th International Conference on Agro-Geoinformatics, Fairfax, VA, USA, 7–10 August 2017; pp. 1–5. [Google Scholar]
  42. Zhang, K.; Wu, Q.; Liu, A.; Meng, X. Can deep learning identify tomato leaf disease? Adv. Multimed. 2018, 10, 6710865. [Google Scholar] [CrossRef]
  43. Elhassouny, A.; Smarandache, F. Smart mobile application to recognize tomato leaf diseases using Convolutional Neural Networks. In Proceedings of the 2019 International Conference of Computer Science and Renewable Energies (ICCSRE), Agadir, Morocco, 22–24 July 2019; pp. 1–4. [Google Scholar]
  44. Agarwal, M.; Gupta, S.K.; Biswas, K.K. Development of Efficient CNN model for Tomato crop disease identification. Sustain. Comput. Inform. Syst. 2020, 28, 100407. [Google Scholar] [CrossRef]
  45. Lamba, M.; Gigras, Y.; Dhull, A. Classification of plant diseases using machine and deep learning. Open Comput. Sci. 2021, 11, 491–508. [Google Scholar] [CrossRef]
  46. Zhao, S.; Peng, Y.; Liu, J.; Wu, S. Tomato leaf disease diagnosis based on improved convolution neural network by attention module. Agriculture 2021, 11, 651. [Google Scholar] [CrossRef]
  47. Moussafir, M.; Chaibi, H.; Saadane, R.; Chehri, A.; Rharras, A.E.; Jeon, G. Design of efficient techniques for tomato leaf disease detection using genetic algorithm-based and deep neural networks. Plant Soil 2022, 479, 251–266. [Google Scholar] [CrossRef]
  48. Li, M.; Zhou, G.; Chen, A.; Yi, J.; Lu, C.; He, M.; Hu, Y. FWDGAN-based data augmentation for tomato leaf disease identification. Comput. Electron. Agric. 2022, 194, 106779. [Google Scholar] [CrossRef]
  49. Paymode, A.S.; Malode, V.B. Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG. Artif. Intell. Agric. 2022, 6, 23–33. [Google Scholar] [CrossRef]
  50. Devi, S.N.; Muthukumaravel, A. A Novel Salp Swarm Algorithm with Attention-Densenet Enabled Plant Leaf Disease Detection and Classification in Precision Agriculture. In Proceedings of the 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA), Coimbatore, India, 4–5 March 2022; pp. 1–7. [Google Scholar]
  51. Bhujel, A.; Kim, N.E.; Arulmozhi, E.; Basak, J.K.; Kim, H.T. A lightweight Attention-based convolutional neural networks for tomato leaf disease classification. Agriculture 2022, 12, 228. [Google Scholar] [CrossRef]
  52. Zhao, Y.; Sun, C.; Xu, X.; Chen, J. RIC-Net: A plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism. Comput. Electron. Agric. 2022, 193, 106644. [Google Scholar] [CrossRef]
  53. Tarek, H.; Aly, H.; Eisa, S.; Abul-Soud, M. Optimized Deep Learning Algorithms for Tomato Leaf Disease Detection with Hardware Deployment. Electronics 2022, 11, 140. [Google Scholar] [CrossRef]
  54. Özbılge, E.; Ulukök, M.K.; Toygar, Ö.; Ozbılge, E. Tomato Disease Recognition Using a Compact Convolutional Neural Network. IEEE Access 2022, 10, 77213–77224. [Google Scholar] [CrossRef]
  55. Mukherjee, G.; Chatterjee, A.; Tudu, B. Identification of the types of disease for tomato plants using a modified gray wolf optimization optimized MobileNetV2 convolutional neural network architecture driven computer vision framework. Concurr. Comput. Pract. Exp. 2022, 34, e7161. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
Download PDF
Image-Based Pothole Detection Using Multi-Scale Feature Network and Risk Assessment
Previous
Non-Rigid Point Cloud Matching Based on Invariant Structure for Face Deformation
Next