
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12128-7
10.1016/j.heliyon.2024.e36097
e36097
Research Article
Hybrid ensemble - deep transfer model for early cassava leaf disease classification
V Kiruthika kiruthika.v@vit.ac.in
a
S Shoba shoba.s@vit.ac.in
b⁎
Sendil Madan madansendil@gmail.com
c
Nagarajan Kishore kishynk0201@gmail.com
d
Punetha Deepak deepakpunetha@mnnit.ac.in
e
a School of Electronics Engineering, Vellore Institute of Technology, Vandalur Kelambakkam Road, Chennai, 600127, Tamilnadu, India
b Centre for Advanced Data Science, Vellore Institute of Technology, Vandalur Kelambakkam Road, Chennai, 600127, Tamilnadu, India
c Analyst, Deloitte Consulting India Pvt Ltd, Hyderbad, 500032, Telangana, India
d Associate Technical Consultant, Perficient Inc, Guindy, Chennai, 600032, Tamilnadu, India
e Department of Electronics and Communication Engineering, Motilal Nehru National Institute of Technology, Allahabad, 211004, Uttar Pradesh, India
⁎ Corresponding author. shoba.s@vit.ac.in
14 8 2024
30 8 2024
14 8 2024
10 16 e3609723 2 2024
5 8 2024
9 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Cassava is a most important carbohydrate human food consumed in many African and Asian countries. Cassava leaf disease is the major issue which affects production. Automatic early cassava leaf disease detection through deep learning models and transfer learning models were used for multiclass classification with different approaches. Existing approaches deal with imbalanced dataset for predicting the classes. This research work develops an approach based on hybrid Ensemble - deep transfer model approach for early leaf disease detection. Data augmentation was applied to the raw data for balancing the dataset. Three distinct new hybrid models namely Ensemble(InceptionV3+DenseNet-BC-121-32 + Xception), Ensemble(ResNet50V2+DenseNet-BC-121-32), Ensemble(ResNet50V2+ResNet50) were developed. The proposed model shows high performance results. A broad comparison of the proposed model was performed with custom based Convolutional Neural Network and pre-trained models. Highest accuracy of 88.83% and 97.89% was obtained in ensemble based approach that combined InceptionV3, Xception, DenseNet-BC-121-32 for five class and two class classification respectively.

Highlights

• Applying data augmentation method for balancing the dataset.

• State-of-art architectures to classify cassava leaf disease images are adopted.

• Developing a hybrid model for early detection of cassava leaf disease.

• Generating Ensemble models that combines best performing transfer learning approaches.

• Ensemble model which combines InceptionV3, DenseNet-BC-121-32 and Xception has best efficiency.

Keywords

Ensemble learning
Transfer learning
Cassava
Classification
Hybrid
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pmc1 Introduction

Cassava (manihot esculenta crantz), also known as yucca, cassava or tapioca, is one of the most important carbohydrate starch rich human food found especially in sub-Saharan Africa and South America. Common edible parts of the plant are the leaves and starchy roots [1], [2]. The leaves and soft shoots are high in protein and vitamins and are taken as a vegetable in several regions [3]. Cassava provides food security and income opportunities for millions of people, particularly in rural areas where other crops may not grow well. Leaf disease is common in many of the plants. Cassava disease was initially found in Tanzania in the end of nineteenth century [4]. Pests, viruses, bacteria, and fungi are the main causes of the various diseases that affect cassava leaves. Cassava Mosaic Disease (CMD), which makes the leaves wrinkled and yellowish and Cassava Bacterial Blight (CBB) [5], which causes root rot, are the major cassava leaf diseases that serve as threats to food security in various parts of Africa. Other diseases of cassava include Green Mite (CGM) disease which causes milky spots on leaves [6], Cassava Brown Steak Disease (CBSD) that has symptoms of red necrotic areas in oval roots, Red mite damage (RMD) that appears like dark red spot on the upper part of the leaf [7], Green mite damage (GMD) where young leaves appear small, prickle and unfolded [8] and Brown leaf spot (BLS) which causes tiny brown spots and dark layer on the top surface of the leaf [9].

The traditional style of detecting the plant disease with human intervention is laborious and lacks the ability to detect cassava disease in a convenient way [10]. It becomes more cumbersome and creates burden on farmers. The farmers can be benefitted by developing an automated system for early detection and prevention of disease in cassava leaves. In the modern era, Deep Learning (DL) algorithms play a predominant role in providing fostering solutions to problems related to prediction and classification. Intelligent algorithms help in early prediction and aid in preventing further losses. Every model proposed tries to achieve a higher accuracy but leaves out the computational complexity of the architecture. Expect for a few works, not much have experimented with different modern-day Machine Learning (ML) architectures. In most cases the dataset used is imbalanced (more samples belong to a particular class) [11]. The resolution of the image while doing various internal operations gets deteriorated, but aiming at improving the resolution would again increase the complexity of the architecture. So, a novel approach is yet to be proposed/invented to address this. Our objective is to propose a novel architecture to achieve better classification results with reduced computational complexity using Convolutional Neural Network (CNN) based custom architectures, Transfer Learning (TL) and Ensemble Learning.

The overview of each section of the paper is as follows: Section 2 explains about the existing literature survey. Section 3 discusses about various CNN based custom architectures, TL models and proposes an ensemble model. Section 4 elaborates the results and compares the proposed work with other existing models. The conclusion and the future work are discussed in section 5.

2 Related work

Proper identification of plant diseases is important, as there is a necessity to maintain food security across globe due to the globalization phenomenon. The best way to detect the leaf diseases is through automated algorithms. ML, DL and TL models have aided the process of identifying many plant diseases. Most of the literature works of cassava leaf disease detection and prediction discussed in this section deal with ML and DL techniques. These techniques involve training a neural network model to recognize patterns in cassava leaves and accurately classify them into healthy or diseased categories. DL involves training a multilayer neural network model to detect complex patterns in images [12]. Initially, the leaf disease detection was done for binary classification which include categorizing them as healthy and diseased. But later techniques were developed for multiclass disease classification. Justice O et al. [13] classified two cassava plant diseases based on ML models. Modeling was performed using a database of around 80,000 images and trained using Coarse Support Vector Machine (CSVM) and Coarse Gaussian Support Vector Machine (CGSVM). The model used 2 tier architecture where first level classifies whether the data is healthy or not using CSVM model and if not healthy the second level classifies it into CMD or CBB using CGSVM model. CSVM model had an accuracy of 83.9% in classifying the plant as healthy or not. In second tier using CGSVM an accuracy of 61.6% was obtained to classify the disease as either blight or mosaic. TL is a technique that involves use of a pre-trained DL model that is tuned and applied on a new dataset. In another related study, pre-trained models such as ResNet50 or VGGNet16 [14] were used as a starting point and then fine-tuned on a dataset of cassava leaf images. This helped to improve model accuracy and reduce the amount of training data needed.

Although the DL model works well for two class classification with leaf disease detection, but there are multiple diseases affecting cassava leaves which in turn affects the production to a larger extent. Research works were directed to detect the leaf disease by fine tuning the parameters of DL model [15]. It involved identifying five categories of diseases namely CBSD, CGM, CBB, and CMD or healthy. It was a fairly basic study as it involved CNN. But a different data augmentation technique was used wherein techniques like gaussian blur, thresholding and sharpening were applied to the images in order to rectify the imbalanced dataset. It had a training accuracy of 95% and validation accuracy of 77% which was better on comparison with ResNet50 or MobileNetV2 when used.

A comparative study was performed on various CNN models [17] such as VGGNets, ResNet50, DenseNet-BC-121-32 and InceptionV3 to identify four cassava leaf diseases namely CBB, CBSD, CGM, and CMD. The results showed that DenseNet-BC-121-32 had best accuracy of 80.52% continued by InceptionV3 with 77.25%, ResNet50 with 75.76% followed by VGGs and traditional CNN architectures. The enhancement of the work was done for identifying healthy and four categories of diseases namely CBSD, CGM, CBB, and CMD using a CNN architecture along with TL [21]. The CNN model U-Net was used in order to segment the dataset whereas EfficientNet-B4 TL model was applied on original and segmented dataset. So here the authors showed that segmentation technique along with TL model boosted the model as the accuracy on original dataset was 81% and segmented dataset was 89% showing 8% increase. By increasing the number of samples as 20,000 images, the healthy and four disease (CBSD, CGM, CMD and CBB) datasets were processed for training and testing. It was then modelled with ResNet50 and MobileNetV2 architectures of DL by extracting the features from the detected areas. K-fold validation was used with a value of k as 5 which resulted in 84% accuracy [19]. A novel deep residual convolutional neural network (DRNN) was used for detecting CMD in leaf images of cassava. Experimental results showed that using an imbalanced image dataset yielded a classification accuracy of 46.28% with Pulse-Coupled Neural Networks (PCNN) and an increase in 10% with DRNN in the Kaggle cassava disease dataset [11].

Gaussian TL models were used for cassava leaf disease detection and classification. The research involved 21,397 images from Makerere University Artificial Image lab and National Crops Resources Research Institute (NaCRRI) which is grouped into five categories such as CBB, CBSD, CGM, CMD and Healthy. Pretrained and hybrid models were used which yielded an accuracy of around 90% [18].

Another focus of research is based on reducing the computational complexity of the architecture. Training the benchmark datasets of cassava affected leaves is a time-consuming process and sometimes leads to the misclassification of class labels. To reduce the time complexity, an integrated image recognition technique along with TL model was employed successfully for disease detection. This approach uses CNN-based EfficientNet transfer model [22] for identification of tiny affected cassava leaf regions. The dimensionality of the feature can be reduced by applying principal component analysis (PCA). The reduced feature was given as input to SVM and Random Forest (RF) classifier to classify the image from the disease affected one. This is one of the methods which is successful in many real time agricultural applications. An intelligent cassava leaf disease detection system was developed using the MobileNetV2 model of DL and displayed in a graphical user interface in Python [15]. An overall accuracy of 65.6% was obtained for the test data.

Categorization of healthy and cassava leaf diseases such as CBB, CGM, CBSD, CMD were performed with simple CNN model. The model achieved around 99% training accuracy, but the validation accuracy was 53.35% [20]. Ensemble models play a vital role in prediction and classification problems. They are formed by amalgamating various models to enable better results. A study on heart disease classification indicated that usage of deep based ensembling learning framework for classifying the different heart diseases rectified the problem of imbalanced heart disease dataset and resulted in a better classification [23]. This study proves that ensemble models aid better accuracy in prediction and classification.

Overall, DL and TL methods used in various cassava leaf disease studies that show a promise for high accuracy in cassava leaf detection is summarized in Table 1.Table 1 Existing Cassava leaf disease classification models.

Table 1Name	Model	Accuracy	Classes	Reference	Dataset	Samples	Test-Train Split	Application	
Cassava Leaf Disease Classification using Deep Neural Networks	MobileNetV2	89.09%	4 CBB, CBSD, CMD, CGM	[15]	iCassava 2019	22032	85-15	GUI Interface	
One-vs-All Methodology based cassava Leaf Disease Detection	EfficientNet B4 model	85.64	4 CBB, CBSD, CMD, CGM	[16]	Makerere University AI lab	21,397	80-20	Android studio, Java, XML	
Cassava Leaf Disease Detection Using Deep Learning	Custom CNN Architecture	77%	5 CBSD, CGM, CBB, CMD, healthy	[12]	Kaggle Dataset	5656	80-20	-	
Detection and classification of cassava diseases using ML	Coarse Gaussian SVM	83.9%	2 CMD, CBBD	[13]	Own farm Cassava dataset	18000	25-95	Matlab application designer package	
Cassava Leaf Disease Recognition Using CNN	DenseNet-BC-121-32	80.52%	4 CBB, CBSD, CMD, CGM	[17]	Makerere University AI lab and NaCRRI	9436	75-25	-	
Transfer model cassava leaf disease detection	Deep Gaussian CNN	89%	5 CBSD, CGM, CBB, CMD, healthy	[18]	Makerere University AI lab and NaCRRI	21397	30-70	-	
Cassava Leaf Images in web environment	MobileNetV2 +SVM Training	84%	4 CBB, CBSD, CMD, CGM	[19]	Makerere University AI lab and NaCRRI	21397	85-15	-	
Real time Cassava leaf disease detection	Simple CNN model	53.35%	5 CBSD, CGM, CBB, CMD, healthy	[20]	Kaggle Dataset	1698	70-30	Mobile application	
DRNN distinct block processing technique	Deep residual CNN	52.87% PCNN by 9.25%	5 CBSD, CGM, CBB, CMD, healthy	[11]	iCassava 2019	22031	50-65	-	

The methods discussed in Table 1 ensure early disease detection, support for management and control of cassava foliar diseases, and improvement for cassava yield and food safety. But most of the DL based studies have either incorporated huge input leaf samples or unbalanced dataset. Moreover, the accuracy that is obtained is also not highly satisfactory. But if a novel system is proposed to be developed, the above literature gives a clarity of challenges that have to be addressed.• Less number of samples for certain classes

• Imbalance in the dataset

• Increasing the accuracy level

In the proposed research, the primary objective is to help farmers in early disease prediction with classification and facilitate the increase in the production of cassava plants. Hence there is a dire need to develop a novel early cassava leaf disease prediction and classification system addressing the above-mentioned challenges. Since it is very evident that ensemble models perform well in classification problems [23], this study proposes a new Hybrid Ensemble - deep transfer model (HEDTM) which combines the viable TL models. The HEDTM uses Kaggle dataset that has imbalanced number of samples in each class. The contribution of the work is as follows.• Balancing the dataset by data augmentation process

• Resolving the inappropriateness in data samples

• Developing a HEDTM by combining three distinct ensemble approaches

• Evaluating the proposed model with various metrics to show analysis in terms of performance

3 Proposed methodology

CNN and their variants have been very successful when it comes to imagery data. There have been different variants of CNN namely InceptionV3, ResNet50, DenseNet-BC-121-32, VGG16, EfficientNet, and many more. Each state of art model has its own pros and cons based on the amount of depth scaling, width scaling, and resolution scaling its capable of. TL is another efficient approach when it comes to reducing computational complexity as the upper layers of the pre-trained architecture's weights are frozen as they are trained for 1000 classes over an ImageNet dataset. Modifications could be done for the higher feature maps in the model through the addition of different layers based on the objective of the task. The work involves a study of twelve different architectures which include three CNN based approaches such as Convolutional Autoencoder, and custom CNN with Dilated Convolution, Parellel Convolution, six TL approaches like ResNet50, Xception, InceptionV3, DenseNet-BC-121-32, ResNet50V2 and VGGNet16, and three ensemble-based approaches. The objective of this work is to develop a new hybrid approach based on ensemble learning with a combination of viable TL models to detect the leaf disease. This work proposes three distinct ensemble approaches namely Ensemble(InceptionV3+DenseNet-BC-121-32+Xception), Ensemble(ResNet50V2+DenseNet121-32-BC), Ensemble(ResNet50v2 + ResNet50). The overall pipeline shown in Fig. 1 gives an overview of the proposed system.Figure 1 Pipeline of the proposed system.

Figure 1

The input data Ni comprising of the image dataset Dk is subjected to data augmentation DAi. DAi is done to aid the problem of data imbalance and improve the quality of the data. Detailed explanation of dataset and data augmentation is given in section 4. The augmented data Aj is trained and training models are developed using the custom CNN Architectures and TL architectures. The untrained Aj is tested using the trained model and its efficiency is evaluated using standard performance metrics. Simultaneously, suitable trained TL models are selected and three different ensemble-based approaches are developed where the predicted results of each TL model are taken and the weighted average of the results is calculated to aid final prediction. The tested Aj are used to develop three ensemble models. Performance metrics are evaluated and the best performing ensemble approach is selected as the suitable model for the proposed system.

3.1 Custom CNN architectures

CNN architectures are widely used for various detection and classification problems [12]. Approaches such as Convolutional Autoencoder, Custom CNN with Dilated Convolution, Parellel Convolution was used in this study.

3.1.1 Convolutional autoencoder

The Convolutional Autoencoder is a reduced dimensionality model representing the images Ni in compressed form. The Ni dimensions were (256,256,3) fed to the convnet layer on which 32 filters of size 3x3 was applied which transformed the dimensions into (256,256,32). The output of the convnet acts as an input to batch normalization followed by leaky ReLu with a slope of 0.2 which was then max-pooled using a (2,2) filter which filtered out the most important features ending in a dimension of (128,128,32). In a similar fashion, with change in number of filters the spatial features were passed to 64 neurons with dense layer which resulted in obtaining important features. The shape's dimension was then (none,131072). This was then reshaped to a dimension (32,64,64) so that no information was lost. To aid this, up-sampling can be performed but it is a light process and the learning is less. So instead, Conv2DTranspose a convolution operation was performed whose kernel was learned while training the model. Finally, the higher feature maps were passed through global max pooling and dense layers to obtain the output.

3.1.2 Parallel convolution approach

This architecture was applied to the same dimensions of images. It was initially fed to three convnet layers parallelly for a stride of 1 with the uniform weight distribution as the kernel initializer. These parallel outputs were average pooled using a (2, 2) filter and each output was of different dimensions due to the different filters used, (128, 128, 64), (128, 128, 64), and (128, 128, 25). These parallel outputs were concatenated. Finally, these were batch normalized and were fed to dense networks to obtain the desired output.

3.1.3 Custom CNN with dilated convolution

The third approach was to create another custom architecture with dilated convolution. Here 3 convnet layers and 3 maxpool layers were stacked alternatively where the convnet layers were fed with a dilation rate of (2,2). This was used as it helped in expanding the region of input image which was not covered with pooling. The intention was to learn more amount of information from the output of each convolution operation. A broad field of view was obtained with same computational cost. There was also a possibility to retrieve high amount of information without incrementing the kernel parameters. Convolution and up-sampling were done alternatively with the same dilation rate. Finally, the output was global average pooled and was passed through a series of dense layers with 128, 256 and 512 neurons, and then the sigmoid layer giving the probabilistic distribution of the classes.

3.2 Transfer learning approaches

TL is an efficient approach and has its own advantage with pre-trained architecture's weights [18]. Pre-trained models can be fine-tuned for specific tasks with relatively little data and computation. It can significantly reduce the training time and improve the performance of the models. In this study, pre-trained models were chosen and were trained on five classes of diseased cassava leaf images. Each pretrained model was customized and fine-tuned for better efficiency. Initially the layers were freezed to prevent the updation in the initial training and the useful features from learned cassava leaf disease dataset was retained. After the initial training, few layers were unfreezed and fine tuning was done. Customizing the pre trained model helped in effective disease classification. Hyperparameter tuning was done. This allowed the model to fine-tune its parameters for a better fit and helped in identifying the best configuration for the proposed cassava leaf disease classification task. The model was compiled with suitable optimizers, loss function and metrics. Approaches such as ResNet50, Xception, InceptionV3, VGGNet16, DenseNet-BC-121-32, ResNet50V2 were used in this study. Every pretrained model has its advantages for different applications.

3.2.1 VGGNet16

VGGNet16 is a CNN based architecture which can classify 1000 different classes of ImageNet. The Ni was subjected to global average pooling and the output was fed to a densely connected layer of 32 neurons. A dropout layer was added to prevent overfitting. Out of the 5 convolutional blocks present in VGGNet16, the first 4 blocks were freezed and the last block was unfreezed on which training was performed.

3.2.2 InceptionV3

This is the third and improved version of InceptionV3 architecture which is 48 layers deep trained on 1000 different classes of ImageNet. Ni was passed through the model, where the output of the model was passed through a densely connected layer of 512 neurons with L2 regularization to reduce overfitting. Finally global average pooling operation was performed instead of conventional adding of fully connected layers on top of the feature maps and the average of every feature map was taken.

3.2.3 Xception

Xception stands for extreme Inception. It is a CNN architecture with 36 depth wise separable convolution layers. It consists of 14 modules excluding for the first and last module. All the modules comprise of linear residual connections. In this study, a slight modification was made in the architecture where once the input was fed, it was then passed through a dense layer of 512 neurons with L1L2 regularizers where it applies both L1 and L2 regularization penalties followed by bias regularizers. Then it was global average pooled and fed to the output layer.

3.2.4 ResNet50 and ResNet50V2

ResNet50 is a CNN with 48 deep convolutional layers which is also a pretrained network that can classify 1000 categories of ImageNet. ResNet50V2 is a modified version of ResNet50 that performs better than ResNet50 and ResNet101. This architecture works on the concept of pre-activation of weight layers i.e., batch normalization and ReLU activation is applied to the input before convolution operation. The Ni was subjected to global average pooling and the output was fed to a densely connected layer of 256 neurons and a dropout layer was added to prevent overfitting for both the architectures.

3.2.5 DenseNet-BC-121-32

DenseNet-BC-121-32 weights were trained on pre-trained DenseNet-121. Here the output was passed through a fully connected dense layer with L1L2 regularizers. Then it was global average pooled and later was fed to the output layer.

In models such as InceptionV3, Xception, ResNet50, ResNet50V2 and DenseNet-BC-121-32, for fine tuning, the first few layers were unfreezed and the model was fit, so that the model weights are based on dataset instead of the pre-trained ImageNet data. Followed by this, remaining last layers were trained and the layers above were freezed. As a result good generalization was obtained in accordance to the data.

Table 2 depicts the hyperparameters of transfer learning model and Table 3 shows the feature size of the different learning models. The model was compiled with sparse categorical cross entropy loss function for the five-class data and binary cross entropy loss function for the two class data. Adam optimizer and a learning rate of 10−4 were used for both classifications. The pretrained models have the following advantages• InceptionV3 primarily emphasizes on consuming less computational power by adjusting the earlier Inception architectures. i.e., computation complexity was reduced by dividing or factorizing larger outputs into smaller convolutions.

• Xception involves depth-wise separable convolution, which in contrast to conventional convolution, does not perform convolution on all channels. Hence, due to fewer connections the model is lighter than InceptionV3.

• In VGGNet16, instead of having large number of hyperparameters, this architecture follows a consistent arrangement of convolutional layers (13 layers in total with a 3x3 filter of stride 1) and max-pooling layers (5 layers in total with a 2x2 filter of stride 2).

• DenseNet-BC-121-32 resolves the issue of vanishing information regarding the input or gradient as it passes across numerous layers. Due to increase in the depth of convolutional neural networks and a less complicated connectivity pattern, maximum information flow amongst layers is ensured in forward and backward gradient computation.

• ResNet50V2 architecture uses a bottleneck design i.e., residual block uses 1×1 convolutions, that helps in the reduction of parameters. This allows a faster training of every layer. ResNet50V2 is one of the most effective architectures, since even in a much deeper network they facilitate a lesser error rate. This also addresses the vanishing gradient problem by using residual blocks.

Table 2 Hyperparameters of transfer learning models.

Table 2Hyperparameter	Specification	
Learning Rate	10−4	
Batch Size	32	
Optimizer	Adam	
Activation Function in Output	Softmax -5 Class Sigmoid -2 Class	
Loss Function	Sparse Categorical Cross Entropy -5 Class Binary Cross Entropy -2 Class	

Table 3 Feature size of different learning models.

Table 3Model	Input Dimension	Activation Function - Hidden Layers	No. Of Layers	No. Of Epochs	
Convolutional Autoencoder	256, 256, 3	LeakyReLU	34	50	
VGGNet16	ReLU	23	25	
InceptionV3	317	15—15	
ResNet50	178	15	
Custom CNN with Dilated Convolution	18	50	
Parallel Convolution	12	25	
ResNet50V2	192	15—20	
Xception	135	15—20	
DenseNet-BC-121-32	429	45	

3.3 Proposed ensemble approaches

The related work discusses different TL architectures and its accuracy level. Most of the studies use multiclass classification and promised to show the accuracy with different approaches. To the best of our knowledge, none of the existing research work pertaining to cassava leaf disease detection use ensemble method by involving the mixture of pre-trained models. This research work proposes an ensemble method that involves the combination of few TL models which have high performance metrics to provide better prediction results. A weighted ensemble classifier aggregates predictions from several models, assigning each a weight proportional to its performance. Combining multiple models typically enhances accuracy and robustness compared to using individual models alone. In this study, a weighted ensemble classifier was used. Here different models were trained on the cassava leaf disease dataset that were tuned with different hyperparameters. Each model was evaluated for its disease classification accuracy and weights were assigned in proportion to their performance. The weighted average of the prediction from the base models were used to make final predictions. The ensemble model is less sensitive to the weaknesses of any single model. The research work proposes three different models of ensemble method as shown in Fig. 2. Here, the models that have been chosen are InceptionV3, Xception, DenseNet-BC-121-32, ResNet50 and ResNet50V2. These models were trained individually as mentioned in section 3.2 and the weights of each model were stored to perform an ensemble prediction. Three distinct ensemble approaches namely Ensemble(InceptionV3+DenseNet-BC-121-32 +Xception), Ensemble(ResNet50V2+DenseNet-BC-121-32), Ensemble(ResNet50V2+ResNet50) were developed. Grid-search [24] helps in determining the optimal hyperparameters of a model that gives the most ‘accurate’ predictions. In this work, the grid search helps in choosing the appropriate combination of weights based on the accuracy. The combination that yields the maximum accuracy was chosen as the optimal weights. The weighted average using grid search helps in considering the varying degrees of importance of the different features available in the dataset. They help in determining the relative significance of each feature on an average. By this approach, instead of neglecting the underperforming models their predictions can be combined to improve the results obtained.Figure 2 Block Diagram of Ensemble Based Approach.

Figure 2

InceptionV3 was chosen as the model's architecture focused on reducing the number of parameters at every inception module. In InceptionV3, the factorization of larger convolutions into smaller convolutions ended in a relative gain and resulted in efficient reduction in grid size. InceptionV3 model uses parallel convolutions rather than making the model deeper. To the model implemented here, custom classification layers were added preserving the original InceptionV3 architecture. The output was adapted to the required number of classes. A flattening layer was used to map the features into a one-dimensional space preceding the fully-connected dense layer. Batch normalization was used for normalization of input features on models which considerably helped in reducing the time taken for convergence. Finally, a dense layer was added with 32 neurons with ReLu activation rather than directly feeding into the output dense layer. The output/prediction dense layer had two neurons representing two classes and five neurons representing five classes with sigmoid and softmax as activation functions respectively and resulted in probabilities of each class label appropriately. Finally, argmax of the predicted probabilities obtained was taken after testing with the trained probabilities which resulted in the desired output class.

DenseNet-BC-121-32 was chosen on an architecture point of view. It was designed in such a way that it allows maximum flow of information across layers by following simple connectivity pattern. The minute features across the edges and boundaries of the cassava leaves were well learned by the layers and were useful in classification. With compression factor, it reduced the computational cost to a great extent. Xception was chosen for two main reasons. The first reason was that it followed depth wise separable convolutions rather than following the regular convolution which is more expensive computationally. Another reason was that, it had same parameters like InceptionV3 model indicating a better computational effectiveness. ResNet50V2 helped in maintaining a low error rate much deeper in the network. They solved the problem of the vanishing/exploding gradient and outperformed the shallower networks. The parameters of the proposed Ensemble Learning Algorithm are described as follows:• Let E={E1,E2,E3} be the set of ensemble learning architectures where E1 = {Xception, InceptionV3, DenseNet-BC-121-32}, E2 = {InceptionV3, DenseNet-BC-121-32, ResNet50V2}, E3 = {ResNet50V2, ResNet50} are the sets of transfer learning architectures used for ensemble learning.

• Consider M = {Xception, InceptionV3, DenseNet-BC-121-32, ResNet50V2, ResNet50} where individual network m ∈ M was trained to produce output of 5 class (D1) and 2 Class (D2); in Datasets Dk, Samples Ni, and labels Sb.

• Ni is resized to (256, 256, 3), and scaled in the [0, 1] interval. Sb indicates the disease present in D: {D1,D2}. The image class labels are represented as S1 and S2, where S1 ={healthy, CBSD, CMD, CGM, CBB} and S2 ={Healthy, Whitefly}.

• Let Aj = {vertical flip, horizontal flip, rotate, brightness range} be the set of data augmentation steps carried out on the input data.

The final output is predicted using a weighted average, where each individual model m ∈ M is given the best unique weights utilizing grid search optimization. These weights are then multiplied by the classifier's prediction, and used in the computation. The weighted average method is given by the following equation:(1) P=arg⁡max⁡∑i=1na(i)y→

where ai is the weight values that multiply with the weight vector y→ and n is the number of models in M.Algorithm 1 Algorithm.

Algorithm 1

4 Results and discussion

The proposed work was implemented in Google colaboratory notebooks which is a cloud computational environment. It provides a free graphics processing Unit (GPU) for building DL models which is powered by Intel Xeon CPU @2.20 GHz, 13 GB RAM, 12 GB DDR5 VRAM. Tesla K80 accelerator were used for this research. All necessary libraries with Tensorflow version 3.11.0, Keras API including Python version 3.19.16, Matplotlib, Scikit-learn, Numpy, Pandas were used.

4.1 Dataset

The dataset pertains to the cassava plant. In this study attempts have been made to classify the datasets into two classes and five classes. The study was performed using Python Software. The dataset shown in Table 4 consists of 2 classes (1081 healthy images and 771 whitefly images). The sample image from the database is given in Fig. 3(a,b). Whitefly infection in cassava is a viral disease caused by Whiteflies. The disease infects the entire plant and propagates easily as well.Table 4 Dataset Distribution for two class classification.

Table 4Type of Leaf	No of Images	
Training	Testing	Validation	
Healthy	647	217	217	
Whitefly disease	461	155	155	

Figure 3 (a) Healthy Cassava Leaf (b) Whitefly infected Cassava Leaf.

Figure 3

The dataset shown in Table 5 consists of five classes which include CBB, CBSD, CGM, CMD and healthy images. The sample images from the database are shown in Fig. 4(a-e). The impact of each disease in cassava plant has been discussed briefly in introduction section.Table 5 Dataset Distribution for five class classification.

Table 5Type of Leaf	No of Images	
Training	Testing	Validation	
CBB	279	94	93	
CBSD	865	290	288	
CGM	463	156	154	
CMD	1594	533	531	
Healthy	189	64	63	

Figure 4 Cassava Leaf Disease Dataset.

Figure 4

4.2 Data augmentation

Data augmentation Aj is a procedure of increasing the volume of data by creating new data points from existing data. Data augmentation was applied to increase the quality of the data and avoid overfitting. Data augmentation techniques used in this study include vertical flip, horizontal flip, random rotation and random brightness.

4.2.1 Vertical and horizontal flips

A vertical flip mirrors an image along its vertical axis. The top portion of the cassava image was swapped with the bottom portion facilitating the change in orientation of the cassava leaf images. A horizontal flip mirrors an image along its horizontal axis, swapping the left and right sides of the cassava leaf images. Flipping images created new examples for the model to learn from, helping to improve generalization. These techniques helped to increase the number of samples and achieve data balance.

4.2.2 Random rotation

Random rotation was performed to focus on specific parts of the leaves. Random rotation is used to rotate the cassava leaf images by a random angle. This helps to make the model more robust to variations in orientation. It generated new variations of the images, helping the model to learn from a more diverse dataset. By seeing rotated versions of the cassava leaf images, the model became better at generalizing to different orientations. The technique helped to prevent the model from memorizing the training data by providing different versions of each cassava leaf image, thereby reducing the risk of overfitting.

4.2.3 Random brightness

Random brightness operation was performed to increase the visibility of the desired information thereby reducing the impact of background information during the processing. Random brightness helps to randomly adjust the brightness of images. This technique helped the model become more robust to variations in lighting conditions. The morphology of the cassava leaves could be very well learnt even in darker backgrounds facilitating precise detection. This helped the model learn from a more diverse dataset. It made the model better at handling different brightness levels it might encounter in real-world scenarios. The various augmentation processes performed in the study are shown in Fig. 5.Figure 5 Sample results of data augmentation process.

Figure 5

4.3 Performance metrics

Performance indices play a vital role in determining the efficiency of the classifier. Sensitivity, Specificity, Accuracy, Precision and F1 score were computed to validate the effectiveness of the classifiers. True Positive (TP), True Negative (TN), False Positive (FP) and False Negative (FN) values obtained from the confusion matrix were used to determine these metrics.(2) Sensitivity=True Positives (TP)True Positives (TP) + False Negatives (FN)

(3) Specificity=True Negatives (TN)True Negatives (TN) + False Positives (FP)

(4) Precision=True Positives (TP)True Positives (TP) + False Positives (FP)

(5) Accuracy=True Positives (TP) + True Negatives (TN)Total Instances

(6) F-measure=2×Precision×RecallPrecision+Recall

If the values of the performance indices are closer to 100%, the effectiveness of the classification algorithms are indicated to be high.

4.3.1 Evaluation of two class classification

The models were trained with Dktrain, Dktest, Dkvalidation with 60%, 20%, 20% respectively to perform classification. In binary classification, 0 denotes the whitefly class and 1 denotes the healthy class. The test data consisted of 155 Whitefly diseased cassava leaf images and 217 healthy cassava leaf images. All the twelve models were trained using the training dataset and the results were analyzed with the help of confusion matrix.

From the confusion matrices shown in Fig. 6(a-l), it can be predicted that, with respect to Convolutional Autoencoder, there is a considerable agreement between the classification of healthy and whitefly diseased samples. In the VGGNet16 model, most of the healthy samples are classified correctly, but some whitefly diseased samples are also classified as healthy samples which results in ambiguity. In the case of ResNet50 model and DenseNet-BC-121-32 model, a considerable number of whitefly diseased samples are classified as healthy samples and vice versa. In the custom CNN with Dilated Convolution model and ResNet50V2 model more whitefly samples are classified as healthy, but a greater number of healthy samples are classified correctly. In the Parallel convolution model, almost all the healthy samples are classified correctly, but few whitefly samples are also classified as healthy. In ensemble model combining ResNet50V2 and ResNet50 there is a considerable agreement in classification of healthy and diseased samples. In the InceptionV3 model, ensemble model combining InceptionV3+DenseNet-BC-121-32+Xception and ResNet50V2+DenseNet-BC-121-32, most of the healthy and whitefly diseased samples are classified correctly which is highly satisfactory.Figure 6 Confusion matrix for Two Class classification.

Figure 6

4.3.2 Evaluation of five class classification

The models were also trained for classifying the datasets into five classes. Here 0 denotes CBB, 1 denotes CBSD, 2 denotes CGM, 3 denotes CMD and 4 denotes healthy. The test data consisted of 94 CBB images, 290 CBSD images, 156 CGM images, 533 CMD images and 64 healthy images. All the twelve models were trained using the training dataset and the results were analyzed with the help of confusion matrix as shown in Fig. 7(a–l).Figure 7 Confusion matrix for Five Class classification.

Figure 7

4.3.3 Performance analysis of efficiency

Different performance metrices such as sensitivity, specificity, precision and F1 score were computed to understand the efficiency of the classifiers. Table 6 shows the values obtained for various performance indices. It clearly indicates that ensemble model which combines InceptionV3, DenseNet-BC-121-32 and Xception followed by InceptionV3 has the highest values for all the performance indices thus showing the highest performance.Table 6 Performance Analysis.

Table 6Model	Sensitivity	Specificity	Precision	F1-Score	
Two Class	Five Class	Two Class	Five Class	Two Class	Five Class	Two Class	Five Class	
Convolutional Autoencoder	98.6	77.04	94.19	94.26	96	78.3	97.3	77.1	
VGGNet16	97.23	76.3	96.12	94.08	97.23	76.9	97.23	74.3	
InceptionV3	98.92	84.2	98.1	96.04	99.06	83.7	98.37	83.81	
ResNet50	94.93	79.6	99.35	94.26	99.51	79.4	97.16	79.1	
Custom CNN with Dilated Convolution	96.77	65.43	89.67	91.35	92.92	60.22	94.80	62.72	
Parallel Convolution	99.53	59.45	90.96	89.86	93.91	52.68	96.64	55.86	
ResNet50V2	98.15	81.6	86.45	95.40	91.02	81.2	94.45	81.1	
Xception	96.31	81.6	92.90	95.40	95	81.40	95.62	81.4	
DenseNet-BC-121-32	92.62	84.1	96.77	96.02	97.57	85	95.03	83.9	
Ensemble(InceptionV3+ DenseNet-BC-121-32+Xception)	97.23	88.56	98.70	97.14	99.06	88.53	98.13	88.54	
Ensemble(ResNet50V2+ DenseNet-BC-121-32)	96.31	85.13	99.35	96.28	99.52	85.65	97.89	85.39	
Ensemble(ResNet50V2+ResNet50)	98.61	82.02	91.61	95.60	94.27	81.78	96.39	81.9	

Accuracy is a standard metric which exactly indicates the proportion of true results. Through this metric, a simple decision can be made by finding out the best model through its higher efficiency percentage. The accuracy of the model has been depicted in Fig. 8 for two class and five class classification.Figure 8 Model Accuracy.

Figure 8

It can be understood from Fig. 8 that ensemble model which combines InceptionV3, DenseNet-BC-121-32 and Xception followed by InceptionV3 yields the highest accuracy. When these three models were ensembled the results were improved to a greater extent.

It can be inferred from the Table 6 and Fig. 8 that ensembling InceptionV3, Xception, DenseNet-121-32 gave best results as compared to other models. Ensembling these three models gave best test accuracy with respect to both the datasets. The reason for the improved performance is that different models make incorrect predictions on different samples of data and there is a possibility that one model might learn a certain number of features really well while some might not. This brings in the fact that if the individual models are statistically different or diverse, then the ensembled model will turn out to be more robust and ends up reducing the variance in prediction errors. Therefore, information fusion improves classification performance as they have been trained on different distributions of data pertaining to same set of classes. Their ensembled predictions turn out to be more robust and bias free. In this study the information pertaining to the fine details of the diseased leaves and their background is taken by the network enabling efficient classification.

4.3.4 ROC curve

A receiver operating characteristic (ROC) curve of all the three ensemble models is drawn to demonstrate the efficiency of the classifier.

From Fig. 9, it can be understood that the ROC curve of the ensemble model which combines InceptionV3, DenseNet-BC-121-32 and Xception hugs towards the top left corner indicating a better efficiency. A broad comparison of the proposed method was made with all the existing approaches as shown in Table 7.Figure 9 ROC curve.

Figure 9

Table 7 Comparative analysis of proposed work with state of art methods.

Table 7Type	Model	Accuracy	References	Train-Test Split	
CNN Architecture	Custom CNN Architecture	77%	[12]	80-20	
DRNN	52.87%	[11]	50-65	
PCNN	46.24%	[11]	50-65	
Pre-trained model	DenseNet-BC-121-32	80.52%	[17]	75-25	
MobileNetV2 model	65.6%	[15]	30-70	
Hybrid Ensemble Deep Transfer model	Ensemble(InceptionV3+DenseNet-BC-121-32+Xception)	88.83%	-	80-20	
Ensemble(ResNet50V2+DenseNet-BC-121-32)	85.13%	-	80-20	
Ensemble(ResNet50V2+ResNet50)	82.58%	-	80-20	

4.3.5 Comparison of proposed work with existing models (5 classes with same dataset)

The efficiency of the HEDTM was demonstrated by comparing the results with the existing methods that had used the same dataset for five class classification. The comparative analysis is done with the same Kaggle dataset that includes CBSD, CGM, CBB, and CMD and healthy classes as used in this work. The baseline approach models used in Cassava leaf disease classification include CNN model, DRNN, PCNN, DenseNet and MobileNet. The accuracy obtained for these models include 77%, 52.87%, 46.24%, 80.52% and 65.6% respectively. These models have used the same dataset that has been used in this study with an 80-20 Train-Test split. This uniform yardstick helped to compare the performance of the developed HEDTM with all these baseline models. A clear comparison is depicted in Table 7 and it indicates that the proposed HEDTM achieved a higher accuracy as compared to the baseline models. A study that reported on cassava leaf disease classification had less number of samples and the challenge of imbalanced dataset [11]. The present study addressed the challenge by using well defined data augmentation techniques. It helped to increase the number of datasets with appropriate balancing of datasets in each class. The outcome of it was trained and tested using the proposed three ensemble approaches, TL methods and custom CNN architectures. The accuracy of the HEDTM was also compared with existing custom CNN architectures [12], DRNN and PCNN [11], DenseNet-121 [17] and MobileNetV2 [15]. The Ensemble(InceptionV3+DenseNet-BC-121-32+Xception) approach shows the best result of 88.83% when compared with other two approaches which include Ensemble(ResNet50V2+DenseNet121-32-BC) and Ensemble(ResNet50V2+ResNet50). The proposed work has an improvement of 23.23% and 8.31% with TL approaches like MobileNetV2 [15] and DenseNet121 [17] respectively. Similarly, the comparison was done with different types of CNN architectures and it showed a prompt improvement of 42.59% with PCNN [11], 35.96% with DRNN [11] and 11.3% [12] with custom CNN. From the above results, it is proved that the proposed HEDTM shows a prompt improvement in accuracy when compared with TL and CNN based architectures. Few limitations of the HEDTM are increased complexity and memory usage, higher computational cost and latency in prediction. As HEDTM ensembles multiple models, it can significantly increase the complexity of the overall system. Training multiple models and combining their predictions require more computational resources and time compared to training a single model. Storing multiple models requires more memory, which can be a constraint in environments with limited storage capacity. Generating predictions with an ensemble model can be slower than using a single model, as it involves aggregating disease predictions from multiple models. Despite these limitations, HEDTM remains as a powerful classifier in cassava leaf disease classification, leading to superior performance. It has given the highest classification accuracy when compared to the existing models that have been trained with the same dataset and the same controlled conditions.

5 Conclusion and future work

An early cassava leaf disease detection and classification was attempted in this research. The imbalanced dataset was rectified by applying data augmentation techniques. TL approaches, CNN based architectures and ensemble based approaches were implemented. TL along with custom layers were implemented which yielded high number of trainable parameters. Also, the weighted average ensemble classifier was used to increase the performance optimally which was validated with ROC curve. The weighted ensemble also resolved the issue of the imbalance and inappropriateness that was existing in the dataset. The performance results of the work were analysed and it was proved that Ensemble(InceptionV3+DenseNet-BC-121-32+Xception) showed incredible accuracy improvement of 23.23%, 42.59%, 35.96%, 11.3% with MobileNetV2, PCNN, DRNN and custom CNN respectively. The ensembled model will help in classifying the various diseases very efficiently and enable the agriculturists in identifying the disease at an early stage thereby preventing the spread of the disease. Future work can include a Pre-processing layer which involves image segmentation into foreground and background to improve model performance. Hybrid ensembled models of vision transformers with other pretrained backbone architectures can be created and trained with different validation sets which might possibly lead to better learning and reduced variance.

CRediT authorship contribution statement

Kiruthika V: Validation, Software, Investigation. Shoba S: Supervision, Investigation. Madan Sendil: Writing – original draft, Resources, Methodology, Conceptualization. Kishore Nagarajan: Writing – review & editing, Writing – original draft, Visualization, Formal analysis. Deepak Punetha: Supervision, Project administration.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

The data used in this research work is publicly available.
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