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Sensors and Materials, Vol. 33, No. 11 (2021) 4031–4044
MYU Tokyo
S & M 2743

EfficientNet: A Low-bandwidth IoT Image Sensor Framework
for Cassava Leaf Disease Classification
Chih-Cheng Chen,1,4 Ju Yan Ba,2 Tie Jun Li,2 Christopher Chun Ki Chan,3*
Kun Ching Wang,5** and Zhen Liu2
1Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan

2School of Ocean Information Engineering, Jimei University, Fujian Province, Xiamen 361021, China

3Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan

4Department of Aeronautical Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan

5Department of Mechanical and Computer-Aided Engineering, Feng Chia University, Taichung 40724, Taiwan(Received July 1, 2021; accepted October 4, 2021; online published November 5, 2021)

Keywords: machine vision, leaf diseases, image enhancement, TTA enhancement

Following the cassava leaf disease classification process, we successfully design a novel
convolutional neural network (CNN) framework called EfficientNet using low-bandwidth image
sensors and a combination of image enhancement and image classification methods. For thisstudy, we have employed low-bandwidth, small-scale IoT image sensors in a farm to capture
images of cassava leaves at periodic intervals. We employ data enhancement techniques such as
test-time augmentation (TTA) and cutmix, cutout, and k-fold to accurately classify and evaluate
the pathology of cassava plants. We carry out multiple simulated experiments to classify andevaluate diseases found in five cassava leaf datasets. Our framework is capable of producing
relatively accurate classification results despite small differences between the test set images,
and we achieved a classification accuracy for our final test set of 89%, comparable with that insimilar studies. Experimental results obtained using a sensor show that EfficientNet significantly
outperforms a state-of-the-art cassava leaf disease classification model.

1.

Introduction

Cassava is a common plant in southern Africa and is the second largest source of foodcarbohydrates produced in small-scale farming. More than 80% of family farms grow thecassava crop, making it one of the major staple foods in the tropics. Cassava is subject to a few
common viral diseases that cause the crop to develop toxins that may cause serious harm ordeath when consumed. Computer vision techniques can visually identify common viral diseases
using sensors and allow farmers to precisely treat certain diseases and increase yields. It isdifficult to detect virus-infested cassava through visual inspection through human eyes, anddoing so often results in low accuracy rates and a high labor cost. A sensor-based crop inspection
system for developing countries must be designed under the constraint of footage from lowbandwidth cameras as developing countries have low access and/or no need for high-definition/*Corresponding author: e-mail: christopherckchan@cyut.edu.tw**Corresponding author: e-mail: kunching.wang@gmail.com

https://doi.org/10.18494/SAM.2021.3526

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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high-bandwidth cameras for farming. A low-bandwidth camera sensor is a type of low-energy
CMOS sensor with reasonably low power consumption that follows low-bandwidth sensor
network IEEE 802.1 protocols, and IoT image sensors are employed in various locations arounda greenhouse farm to capture images of cassava leaves.
A computer’s interpretation of an image is through pixel information, which is different froma human’s interpretation of an image through the human visual system. Detection via
computerized interpretation works by transforming an image to pixels, which have informationand can be input to a computer in a specific way, such as a vector, then processed in parallel orseries. Utilizing pixel-by-pixel information with mathematical operations such as convolution
can produce a computerized abstraction that produces unique ‘features’. These features are thencategorized into bins that are useful for performing a task. In industry and academia, there are
five types of image classification: (1) image classification based on the simultaneousinterpretation of different sensor information; (2) image classification based on the properties oftraining samples; (3) image classification based on various parameters used in data; (4) image
classification based on certain properties of pixels; and (5) image classification based on spatial
information.
Object detection determines the position of a target object in an image, whereas target
detection is the identification of a target object in an image of any natural scene. Target detection
is divided into three stages: image region selection, feature extraction, and image classification.
It is important to also understand edge extraction technology for computer vision as it isimportant for our enhancement methodology. In computer vision, an object’s edge contains asignal mutation, an irregular structure, and instability. Thus, objects in an image can beinterpreted via a signal that exists in an object’s contour. By determining the position of animage edge and its signal, we can obtain a sample that can be used for image recognition.
Performing signal processing and edge detection well can (1) improve image quality; (2) provide
information for the separation of targets; (3) provide information on a scene; (4) provide
information for further feature recognition. Edge extraction technology has made rapid progress,
increasing the accuracy of classification.
The dataset used in our study consists of more than 20000 photos of cassava plants, whichwere captured in Uganda. These cassava plants were identified and classified into four diseasetypes and healthy plants by the National Crop Resources Institute. We posit that, with the help ofEfficientNet, farmers can quickly identify the type of disease and treat the cassava plant more
effectively.
In this study, we designed a convolutional neural network (CNN) framework called
EfficientNet to classify cassava leaf diseases with high accuracy. We briefly introduce CNNs in
Sect. 2, as well as give a short introduction to pooling and fully connected layers. In Sect. 3, we
discuss related works in cassava leaf disease detection. Section 4 shows the structure andprinciple of data enhancement and label smoothing in detail. In Sect. 5, we describe the principle
and hierarchical structure of EfficientNet in detail. In Sect. 6, we describe our results throughexperiments, in which we discuss the k-fold and test-time augmentation (TTA) strategies for test
set training, and our process for verifying the accuracy of our method. We conclude our study in
Sect. 7.

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2.

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Convolutional Neural Networks

CNNs are widely used in image classification, knowledge mapping, recommendation
systems, target detection, target recognition, image recognition, speech recognition, and otherfields. After Hubel and Wiesel explored the cat’s visual nerve, they proposed the concept of a
receptive field(1) and further discovered that there is an association between the visual cortex of
the brain and the processing and classification of external input. They explained the concept of
the sensory field; visual input spaces and cells contribute to the whole visual cortex and can bemimicked to realize accurate computer vision techniques. CNNs(2) are then created from theseconcepts and can be divided into three parts. The first part is the image input layer, the second
part is the convolutional and pooling layers for feature extraction, and the third part is a joining
layer, which decomposes and merges image features into a column vector.
2.1

Convolutional layer

A convolutional layer(3) has an optimized backpropagation solution, and together these form a
convolution unit. A convolution layer comprises multiple convolutional units. A convolution
operation determines low-level features of an image, such as edges, lines, and faces. With each
additional convolution layer, a network can extract more complex features from low-levelfeatures. The recognition of objects is essentially performed by mathematical reasoning andabstraction from the features of points and edges. For example, in the first layer, the information
regarding points and edges is extracted from pixels and used as the input for the next layer. In thesecond layer, rectangles, ellipses, and other information are recognized from the points andedges, and used as the input for the next layer, and so forth. This process occurs until a level ofmathematical abstraction that is sufficient for the relevant matching task is obtained. Because
many features can be extracted, one can adjust the number of convolutions and features toextract according to the desired purpose. Figure 1 shows the convolution process for two
matrices, where the pink matrix is the resulting matrix.
In Fig. 1, the size of the input picture is 5 × 5. After convolution with the convolution core of
size 3 × 3, the original input space is mapped to a 3 × 3 area. Here, the convolution operation
extracts the features of the input image, so that every pixel in the image can be learned in thisway.

Fig. 1.

(Color online) Convolution process for CNNs.

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2.2

Pooling layer

We obtain abstract features through the preset convolution layer, which are prone tooverfitting.(4) Therefore, it is necessary to reduce the dimension of features by downsampling in
the pooling layer. The maximum or average pooling method is used to obtain the maximum or
average value in a fixed area to reduce the dimension of features. This method can not only
preserve image features but also prevent overfitting. The maximum pooling process is shown in
Fig. 2.
2.3

Fully connected layer

The function of the fully connected layer is to connect all the features in the form of column
vectors from the 2D feature map output by the front layer. The output value is sent to theclassifier, and the process is shown in Fig. 3.
The convolution and pooling layers map the original data to a feature space, which meansthat the accuracy of the feature matrix and the abstraction of the original data are increased. To
input this information into a classifier, we convert the feature space into a column vector, and thenext layer maps the convolution and pooling layer outputs to a linear separable space. The fully
connected layer can obtain high-order features from the convolution and pooling operations,simplify the parameter model, and reduce the numbers of neurons and training parameters.(4)3.

Related Works on Cassava Leaf Disease Detection

3.1

Deep models for cassava leaf disease detection

According to our comprehensive survey on cassava leaf disease detection methods using
image processing and artificial intelligence, current methodologies use techniques such as
artificial intelligence, IoT, algorithms based on rules, machine learning regression techniques,
image processing, transfer learning, hyperspectral imagery, leaf extraction, and segmentation.(5)There exist deep learning models that require substantial computational resources andsignificant postprocessing time to obtain high recognition rates. Studies have been reported on

Fig. 2.

(Color online) Maximum pooling process.

Fig. 3.

Fully connected layer.

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pest damage recognition utilizing deep transfer learning (93% classification accuracy rate),(6) the
resolution of class imbalances using a synthetic minority oversampling technique forunderrepresented classes,(7) and a deep learning model with no augmentation processes (85%accuracy rate).(8) Other related studies include those on deep residual CNNs with distinct block
processing(9) and the real-time embedded system MobileNetV2 deep learning model (65.6%
accuracy rate).(10) Preliminary cassava leaf disease detection was performed with a cubic support
vector machine (SVM) model and a coarse Gaussian SVM, which yielded accuracies of 83.9 and61.6%, respectively.(11)3.2Lightweight models for cassava leaf disease detection

Lightweight models, similar to our framework, which are more practical for developing
countries, include a mobile implementation using a single-shot detector called MobileNet to
identify foliar symptoms of cassava leaf diseases(12) and a recognition model using low-quality
images that are augmented with enhanced data techniques.(13)The mobile implementation(12) requires sufficient training examples from diverse data
sources to improve its detection accuracy and better capture the diversity of cassava leaf diseases
that occur in the real world. The methodology using low-quality images(13) attempts to solve therecognition problem by using a novel image color histogram transformation technique togenerate synthetic images, which is based on the convolution of Chebyshev orthogonal functions
with the probability distribution functions of image color histograms. Our method is similar inthat we use color transformations for normalization but do not rely on color histograms.

4.

Preprocessing of Training Data

4.1

Applied data enhancement techniques

Data enhancement technology refers to reusing and learning each image through processing
without increasing the number of images in the expanded dataset.(14) In our study, we employedkey data enhancement techniques to achieve an effect similar to that of increasing the number ofimages in the expanded dataset. A geometric transformation of the image is carried out as shownin Fig. 4. As shown in Fig. 5, we applied a color transformation to the image.(15)
4.2

Label smoothing

In this paper, we utilize label smoothing, which is a regularization method(16) that improves
generalization and model calibration. Smoothing the labels prevents the network from being
overconfident and overfitting results after training. In the loss function, where the model applies
the softmax function to the penultimate layer’s logit vectors to compute its output probabilities,
the gradient is bounded between 0 and 1. These boundaries are too far apart and we adjusted
them to 0.3 and 0.7, so that gradient descent and smoothed labels will compute better probabilities
with small logit gaps. Doing so will partly address the problem of poor generalization.

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Fig. 4.

(Color online) Geometric transformation of the image.

Fig. 5.

(Color online) Color transformation.

In the classification problem, there are generally two values of tags in the training data, 0 and
1, where 0 represents a certain category and 1 represents the opposite category. Thus, intraditional one-hot coding, the label vector yi is
1, i = target ,yi = 
0, i ≠ target.

(1)

When the model is deployed for training, its minimum loss function isK

H ( y, p ) = −∑ yi log pi ,i(2)

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where parameters pi correspond to the output logic vector z of the penultimate layer in the model,
which can be calculated by the softmax functionpi = −

exp ( zi )

∑

K
exp
j

( zi ).

(3)

For the past models, the aim was for the target tag value predicted at the end to be close to 1
and the non-target tag value to be close to 0. However, if the logic vector is too large, the network
will be overfitted and the generalization ability will be poor. Therefore, the cross-entropy
function is not necessarily the best choice as the objective function.
Label smoothing combines a uniform distribution with updated label vectors,i.e.,=yˆi yhot (1 − a ) + a / k ,(4)where k is the total number of categories and a is a small superparameter (usually 0.1),
determined astarget ,1 − a , i =yˆi = a / K , i ≠ target.

(5)

Label smoothing can be understood as adding interference noise to the label distribution of
the original real dataset. In this way, the overfitting of the model for labels can be avoided andthe difference between labels is not too large, which also improves the generalization ability.
Cassava leaf classification can benefit from label smoothing as it changes the representations
learned by the penultimate layer of the network by encouraging the representations of training
examples from the same class to group in tight clusters.
4.3

Mixup, cutout, and cutmix

For our study, we also applied three preprocessing methods to the data to improve theaccuracy of our results. Cassava leaves captured by low-resolution sensors are often subject tonoise and environmental interference, and by utilizing simple preprocessing methods, we
improve the model accuracy without adding additional data. The three methods are mixup,
cutout, and cutmix.
Mixup: Mix any two samples according to the desired proportion. The results of theclassification are distributed according to the proportion.
Cutout: Arbitrarily delete part of the region in the sample and fill the region with a pixel valueof 0. The result of classification remains unchanged.
Cutmix: Delete part of an image and randomly fill the part with nonzero pixel values, which
are allocated according to a certain proportion. These functions are illustrated in Fig.
6.

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Fig. 6. (Color online) Illustration of the mixup, cutout, and cutmix functions.

5.

EfficientNet Algorithm

5.1

Convolutional neural network model

The depth and width of the model network and the resolution of the input image correspondto the number of layers, the channel size, and the input tensor size in each stage, respectively.
Because the network depth and width and the resolution are adjusted continuously, the accuracyof the model tends to be stable. The general framework of the EfficientNet model is shown in
Fig. 7.
As each image in the dataset passes through the neural network, it obtains a feature map,
similarly to that in encoding, as shown in Fig. 8. Each dimension vector of the feature map canbe understood as the feature vector corresponding to part of the original image, namely, a local
feature. Finally, the features in the decoding part are input to the fully connected layer thenclassified by softmax. The principle of the EfficientNet model is shown in Fig. 9.
The EfficientNet network layer size and channel number are shown in Table 1. Through thecoefficients h, W, and R, EfficientNet can be extended to other EfficientNet networks, as shownin Table 2.
5.2

Depthwise separable convolution

The depth can be separated by convolution shown in Fig. 10, which can reduce the number ofconvolution parameters. The parameter quantity shown in Fig. 11 is 3 × 3 × 10 × 4, that is, 360
parameters are required. EfficientNet has seven stages, each of which has the same structure but
different superparameters.
The number of parameters required is 3 × 3 × 10 + 1 × 1 × 10 × 6 = 150. After processing bya single 1 × 1 convolution kernel, the features of these channels can be combined linearly, so thatthe features are related to each other. A diagram of depthwise separable convolution is shown in
Fig. 11.

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Fig. 7.

Fig. 8.

Fig. 9.

4039(Color online) Framework of EfficientNet model.

(Color online) Schematic diagram of encoding principle.

(Color online) Schematic of EfficientNet principle.

Table 1Size and number of channels of each network layer of EfficientNet.
Stage
Operator
Resolution
Channels1
Conv3 × 3
224 × 224
32
2
MBConVt k3 × 3
112 × 11216
3
MBConV6, k3 × 3
112 × 11224
4
MBConV6, k5 × 5
56 × 5640
5
MBCOnV6, k3 × 3
28 × 28
80
6
MBeOnV6, k5 × 5
14 × 14112
7MBConV6, k5 × 5
14 × 14
192
7×7
320
8
MBConV6t k3 × 3
9
ConVIXI & PooIing & FC
7×7
1280

Layers
1
1
2
2
3
3
4
1
1

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Table 2
Parameter values of different EfficientNet versions.
Width
Depth
EfficientNet-b0
1.0
1.0
EfficientNet-b1
1.0
1.1
EfficientNet-b2
1.1
1.1
EfficientNet-b3
1.2
1.4
EfficientNet-b4
1.4
1.8
EfficientNet-b5
1.6
2.2
EfficientNet-b6
1.8
2.6
EfficientNet-b7
2.0
3.1

Fig. 10. (Color on li ne) Diag ra m of ord i na r y
convolution.

Resolution
224
240
240
300
380
456
528
600

Fig. 11. (Color online) Diagram of depthwise
separable convolution.

6.

Experiment and Analysis

6.1

Setting experimental parameters for k-fold and TTA

There are four functions of data enhancement. The first is to avoid overfitting. When adataset has some special feature preferences, such as the pictures in the dataset are taken fromthe same natural scene, data enhancement methods such as cutout and cutmix are used toprevent the model from learning information unrelated to the target. The second is to improve
the robustness of the model and reduce its sensitivity to the data. When the training data
encounters special situations, such as occlusion, brightness, and fuzziness, it is easy to identify
errors. The model data can be improved by adding noise and concealment. The third is toincrease the amount of training data and improve the generalization ability of the model. The
fourth is to avoid sample imbalance. In industrial defect detection, there is often a large
difference between the numbers of positive and negative samples. For example, in the training
samples, the number of good samples is far greater than that of poor samples, which results inthe network being more inclined to learn the characteristics of good samples than those of poor
samples, reducing the effectiveness of the network. By using data enhancement and modification
techniques, the information shown in the data can be enhanced.
6.2

Evaluation of image classification accuracy

With k set to 5 in the k-fold method, the image set used for testing is separated from the image
set used to train the model network parameters. The accuracy of the test results is acceptable. In

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the test, the TTA parameter is set to 3, that is, one test image is judged three times. The results
for the first six batches are shown in Fig. 12 and the results for the last five batches are shown in
Fig. 13. The results of the test set are shown in Fig. 14. In the 12 rounds of deep learning training,
the loss rate of the verification set is as low as 0.27 and the correct rate is 89.73% in the final test
set, which is not involved in the training.
6.3

Experimental comparison

The datasets with and without data enhancement were trained and tested. The experimental
results obtained using a low-bandwidth camera sensor are shown in Figs. 15 and 16. After fiveFig. 12. (Color online) Results for the first six batches.

Fig. 13. (Color online) Results for the last five batches.

Fig. 14. (Color online) Test set results.

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Fig. 15. (Color online) Training results of the last five batches without data enhancement training.

Fig. 16. (Color online) Training results of the last five batches after data enhancement training.

training epochs, it can be seen that in the 10th batch of training, the loss is reduced to 0.26 in the
training and 0.42 in the testing. However, in the 10th batch of training with data enhancement,
the loss is reduced to 0.38 in the training and 0.35 in the testing. We can see that the training
with data enhancement significantly reduces the loss compared with the training without dataenhancement, and that data enhancement can reduce the image loss in testing. Without dataenhancement training, the generalization ability is poor and the performance of the training set
is overfitted.

7.

Conclusion

We developed a Cassava leaf classification neural network called EfficientNet to classify
diseases from images of cassava leaves. In our proposed classification process, we enhance a
specific cassava leaf dataset, but our framework can theoretically be applied to other cassava leaf
datasets in future work. We set up IoT image sensors at various locations in a greenhouse farmcontaining cassava leaves to capture closeup images of cassava leaves. We utilized dataenhancement techniques to improve the generalization ability of the network and provide higher
recognition rates for cassava leaves in natural scenes. However, performance characteristics may
differ according to environmental conditions, such as natural light and fog (which can cause
colors in the image to be imbalanced), or the camera capability. When images captured by acamera are used, the details of leaves cannot be seen because of the low resolution of the images;

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therefore, it is important to employ novel postprocessing techniques for images taken in naturalscenes.

Acknowledgments
This work was supported by the Natural Science Foundation of Fujian Province, China(Grant No. 2020J01714, 2020J01713, and MOST 110-2622-E-035-006).

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About the Authors
Chih Cheng Chen has been a professor at Jimei University, China, since 2017.
He became a member of IEEE in 2011 and a senior member in 2016. He earned
his M.S. degree in 2005 and recently earned his Ph.D. degree from the
Department of Mechatronics Engineering, National Changhua University of
Education. He has published 43 academic articles and owns three patents. He
has also implemented several projects funded by the National Natural Science
Foundation of China, Fujian Province. His research interests include AIoT
technology, machine learning, information security, and RFID applications.
(chenccheng@fcu.edu.tw)4044

Sensors and Materials, Vol. 33, No. 11 (2021)

Ju Yan Ba has been a student at Jimei University, China, since 2017. He
earned his C.E. degree in 2021 and is continuing his study in computer science
toward earning a master’s degree. (896729202@qq.com)

Tie Jun Li has been an associate professor at Jimei University, China, since2015. He received his B.S. degree from Chongqing University, China, in 2000
and his M.S. and Ph.D. degrees from Chongqing University in 2005 and 2018,
respectively. He has published 21 academic articles and owns 11 patents. He
also implemented several projects supported by the Natural Science
Foundation of China, Fujian Province. His research interests are in machinelearning, terahertz technology, and sensors. (litiejun@jmu.edu.cn)

		Christopher Chun Ki Chan has been an assistant professor at Chaoyang
University of Technology, Taiwan, since 2019. He is an IEEE member and an
active reviewer for computer science journals and conferences such as TEMS.
He earned his M.S. and Ph.D. degrees from the Department of Computer
Science, Ryerson University, Canada, where he specialized in deep
reinforcement learning, robotics, and Industry 4.0.
(christopherckchan@cyut.edu.tw)
Kun Ching Wang is a professor of the Department of Mechanical andComputer-Aided Engineering, Feng Chia University, Taiwan. He became amember of IEEE in 2012 and a senior member in 2019. He received his Ph.D.
degree in control engineering from National Chiao-Tung University (NCTU),
Hsinchu, Taiwan, in 2005. He has published over 40 journal and conference
papers. His research interests include speech/audio processing and recognition,
machine learning, computer vision, advanced driver assistance systems, andIoT application.
Zhen Liu is currently studying for a master’s degree at Jimei University,
China. His research interests include deep learning, signal processing, image
recognition, and bearing fault diagnosis.