This paper proposes a leaf identification system using convolutional neural network (CNN). This proposed system can identify five types of local Malaysia leaf which were acacia, papaya, cherry, mango and rambutan. By using CNN from deep learning, the network is trained from the database that acquired from leaf images captured by mobile phone for image classification. ResNet-50 was the architecture has been used for neural networks image classification and training the network for leaf identification. The recognition of photographs leaves requested several numbers of steps, starting with image pre-processing, feature extraction, plant identification, matching and testing, and finally extracting the results achieved in MATLAB. Testing sets of the system consists of 3 types of images which were white background, and noise added and random background images. Finally, interfaces for the leaf identification system have developed as the end software product using MATLAB app designer. As a result, the accuracy achieved for each training sets on five leaf classes are recorded above 98%, thus recognition process was successfully implemented.
Random Forest Algorithm - Random Forest Explained | Random Forest In Machine ...Simplilearn
This Random Forest Algorithm Presentation will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python.
Below are the topics covered in this Machine Learning Presentation:
1. What is Machine Learning?
2. Applications of Random Forest
3. What is Classification?
4. Why Random Forest?
5. Random Forest and Decision Tree
6. Comparing Random Forest and Regression
7. Use case - Iris Flower Analysis
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
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Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
Random Forest Algorithm - Random Forest Explained | Random Forest In Machine ...Simplilearn
This Random Forest Algorithm Presentation will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python.
Below are the topics covered in this Machine Learning Presentation:
1. What is Machine Learning?
2. Applications of Random Forest
3. What is Classification?
4. Why Random Forest?
5. Random Forest and Decision Tree
6. Comparing Random Forest and Regression
7. Use case - Iris Flower Analysis
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
how,when and why to perform Feature scaling?
Different type of feature scaling Technique.
when to perform feature scaling?
why to perform feature scaling?
MinMax feature scaling techniques.
Unit vector scaling.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Imagine someone hiking in the Swiss mountains, where he finds a weird leaf or flower. This person has always been bad in biology but would like to know more about that plant. What’s its name? What are their main features? Is it rare? Is it protected? etc. By simply taking a picture of the leaf with a Digital Camera, he or she could feed it to the database in his computer and then get all the information regarding the leaf image through an automatic leaf recognition application.
Even today, identification and classification of unknown plant species are performed manually by expert personnel who are very few in number. The important aspect is to develop a system which classifies the plants. This paper presents a new recognition approach based on Leaf Features Fusion and Random Forests (RF) Classification algorithms for classifying the different types of plants. The proposed approach consists of three phases that are pre-processing, feature extraction, and classification phases. Since most types of plants have unique leaves. Leaves are different from each other by characteristics such as the shape, color, texture and the margin.
This is an intelligent system which has the ability to identify tree species from photographs of their leaves and it provides accurate results in less time.
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
how,when and why to perform Feature scaling?
Different type of feature scaling Technique.
when to perform feature scaling?
why to perform feature scaling?
MinMax feature scaling techniques.
Unit vector scaling.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Imagine someone hiking in the Swiss mountains, where he finds a weird leaf or flower. This person has always been bad in biology but would like to know more about that plant. What’s its name? What are their main features? Is it rare? Is it protected? etc. By simply taking a picture of the leaf with a Digital Camera, he or she could feed it to the database in his computer and then get all the information regarding the leaf image through an automatic leaf recognition application.
Even today, identification and classification of unknown plant species are performed manually by expert personnel who are very few in number. The important aspect is to develop a system which classifies the plants. This paper presents a new recognition approach based on Leaf Features Fusion and Random Forests (RF) Classification algorithms for classifying the different types of plants. The proposed approach consists of three phases that are pre-processing, feature extraction, and classification phases. Since most types of plants have unique leaves. Leaves are different from each other by characteristics such as the shape, color, texture and the margin.
This is an intelligent system which has the ability to identify tree species from photographs of their leaves and it provides accurate results in less time.
Convolutional neural networks (CNN) trained using deep learning (DL) have advanced dramatically in recent years. Researchers from a variety of fields have been motivated by the success of CNNs in computer vision to develop better CNN models for use in other visually-rich settings. Successes in image classification and research have been achieved in a wide variety of domains throughout the past year. Among the many popularized image classification techniques, the detection of plant leaf diseases has received extensive research. As a result of the nature of the procedure, image quality is often degraded and distortions are introduced during the capturing of the image. In this study, we look into how various CNN models are affected by distortions. Corn-maze leaf photos from the 4,188-image corn or maize leaf Dataset (split into four categories) are under consideration. To evaluate how well they handle noise and blur, researchers have deployed pre-trained deep CNN models like visual geometry group (VGG), InceptionV3, ResNet50, and EfficientNetB0. Classification accuracy and metrics like as recall and f1-score are used to evaluate CNN performance.
Recently, plant identification has become an active trend due to encouraging
results achieved in plant species detection and plant classification fields
among numerous available plants using deep learning methods. Therefore,
plant classification analysis is performed in this work to address the problem
of accurate plant species detection in the presence of multiple leaves together,
flowers, and noise. Thus, a convolutional neural network based deep feature
learning and classification (CNN-DFLC) model is designed to analyze
patterns of plant leaves and perform classification using generated finegrained feature weights. The proposed CNN-DFLC model precisely estimates
which the given image belongs to which plant species. Several layers and
blocks are utilized to design the proposed CNN-DFLC model. Fine-grained
feature weights are obtained using convolutional and pooling layers. The
obtained feature maps in training are utilized to predict labels and model
performance is tested on the Vietnam plant image (VPN-200) dataset. This
dataset consists of a total number of 20,000 images and testing results are
achieved in terms of classification accuracy, precision, recall, and other
performance metrics. The mean classification accuracy obtained using the
proposed CNN-DFLC model is 96.42% considering all 200 classes from the
VPN-200 dataset.
A deep learning-based approach for early detection of disease in sugarcane pl...IAESIJAI
In many regions of the nation, agriculture serves as the primary industry. The farming environment now faces a number of challenges to farmers. One of the major concerns, and the focus of this research, is disease prediction. A methodology is suggested to automate a process for identifying disease in plant growth and warning farmers in advance so they can take appropriate action. Disease in crop plants has an impact on agricultural production. In this work, a novel DenseNet-support vector machine: explainable artificial intelligence (DNet-SVM: XAI) interpretation that combines a DenseNet with support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) interpretation has been proposed. DNet-SVM: XAI was created by a series of modifications to DenseNet201, including the addition of a support vector machine (SVM) classifier. Prior to using SVM to identify if an image is healthy or un-healthy, images are first feature extracted using a convolution network called DenseNet. In addition to offering a likely explanation for the prediction, the reasoning is carried out utilizing the visual cue produced by the LIME. In light of this, the proposed approach, when paired with its determined interpretability and precision, may successfully assist farmers in the detection of infected plants and recommendation of pesticide for the identified disease.
Peanut leaf spot disease identification using pre-trained deep convolutional...IJECEIAES
Reduction of quality and quantity of agricultural products, particularly peanut or groundnut, is usually associated with disease. This could be solved through automatic identification and diagnoses using deep learning. However, this technology is not yet explored and examined in the case of peanut leaf spot disease due to some aspects, such as the availability of sufficient data to be used for training and testing the model. This study is intended to explore the use of pre-trained visual geometry group–16 (VGG16), visual geometry group–19 (VGG19), InceptionV3, MobileNet, DenseNet, Xception, InceptionResNetV2, and ResNet50 architectures and deep learning optimizers such as stochastic gradient descent (SGD) with Momentum, adaptive moment estimation (Adam), root mean square propagation (RMSProp), and adaptive gradient algorithm (Adagrad) in creating a model that can identify leaf spot disease by using a total of 1,000 images of leaves captured using a mobile camera. Confusion matrix was used to assess the accuracy and precision of the results. The result of the study shows that DenseNet-169 trained using SGD with momentum, Adam, and RMSProp attained the highest accuracy of 98%, while DenseNet-169 trained using RMSProp achieved the highest precision of 98% among pre-trained deep convolutional neural network architectures. Furthermore, this result could be beneficial in agricultural automation and disease identification systems for peanut or groundnut plants.
A Novel Leaf-fragment Dataset and ResNet for Small-scale Image AnalysisA. Hasib Uddin
This paper introduces a dataset of leaf vein images from four
different species (two from Monocotyledons and two from Dicotyledons).
Then multiple instances of the dataset containing 64x64, 32x32, 16x16, 8x8,
and 4x4 pixel single-channel center-focused images were created, and a
new Residual Neural Network (ResNet) based model has applied on each of
the instances for cotyledon (seed leaf or embryonic leaf) group
identification. Additionally, the same procedures were followed for plant
species classification. The aim was to make use of only the vein patterns for
the task. The results show that despite the difficulties of recognizing
patterns from small-scale images, it is likely to efficiently categorize seed
types and plant species by properly deploying residual blocks in neural
network models. Also, the applied ResNet was compared against
ResNet-152 V2 on the 64x64 pixel image set in terms of species
classification.
AN APPROACH FOR IRIS PLANT CLASSIFICATION USING NEURAL NETWORKijsc
Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on
the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years
the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning
algorithm.
An Approach for IRIS Plant Classification Using Neural Network ijsc
Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Automated disease detection in crops using CNNManojBhavihal
Identification and classification of leaf diseases in agriculture are crucial for crop
health. Traditionally, this task has been manual and error-prone. Recent advances in
computer vision and machine learning, specifically Convolutional Neural Networks
(CNNs), have enabled automated leaf disease detection and classification. Our
approach utilizes image data of various diseases (e.g., powdery mildew, rust) collected
from different crops.
Deep learning is a collection of machine learning algorithms utilizing multiple layers, with which higher levels of raw data are slowly removed. For example, lower layers can recognize edges in image processing whereas higher layers may define concepts for humans such as numbers or letters or faces. In this paper we have done a literature survey of some other papers to know how useful is Deep Learning and how to define other Artificial Intelligence things using Deep Learning. Anirban Chakraborty "A Study of Deep Learning Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31629.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31629/a-study-of-deep-learning-applications/anirban-chakraborty
Application of informative textural Law’s masks methods for processing space...IJECEIAES
Image processing systems are currently used to solve many applied problems. The article is devoted to the identification of negative factors affecting the growth of grain in different periods of harvesting, using a program implemented in the MATLAB software environment, based on aerial photographs. The program is based on the Law’s textural mask method and successive clustering. This paper presents the algorithm of the program and shows the results of image processing by highlighting the uniformity of the image. To solve the problem, the spectral luminance coefficient (SBC), normalized difference vegetation index (NDVI), Law’s textural mask method, and clustering are used. This approach is general and has great potential for identifying objects and territories with different boundary properties on controlled aerial photographs using groups of images of the same surface taken at different vegetation periods. That is, the applicability of sets of Laws texture masks with original image enhancement for the analysis of experimental data on the identification of pest outbreaks is being investigated.
Similar to Plant leaf identification system using convolutional neural network (20)
Square transposition: an approach to the transposition process in block cipherjournalBEEI
The transposition process is needed in cryptography to create a diffusion effect on data encryption standard (DES) and advanced encryption standard (AES) algorithms as standard information security algorithms by the National Institute of Standards and Technology. The problem with DES and AES algorithms is that their transposition index values form patterns and do not form random values. This condition will certainly make it easier for a cryptanalyst to look for a relationship between ciphertexts because some processes are predictable. This research designs a transposition algorithm called square transposition. Each process uses square 8 × 8 as a place to insert and retrieve 64-bits. The determination of the pairing of the input scheme and the retrieval scheme that have unequal flow is an important factor in producing a good transposition. The square transposition can generate random and non-pattern indices so that transposition can be done better than DES and AES.
Hyper-parameter optimization of convolutional neural network based on particl...journalBEEI
Deep neural networks have accomplished enormous progress in tackling many problems. More specifically, convolutional neural network (CNN) is a category of deep networks that have been a dominant technique in computer vision tasks. Despite that these deep neural networks are highly effective; the ideal structure is still an issue that needs a lot of investigation. Deep Convolutional Neural Network model is usually designed manually by trials and repeated tests which enormously constrain its application. Many hyper-parameters of the CNN can affect the model performance. These parameters are depth of the network, numbers of convolutional layers, and numbers of kernels with their sizes. Therefore, it may be a huge challenge to design an appropriate CNN model that uses optimized hyper-parameters and reduces the reliance on manual involvement and domain expertise. In this paper, a design architecture method for CNNs is proposed by utilization of particle swarm optimization (PSO) algorithm to learn the optimal CNN hyper-parameters values. In the experiment, we used Modified National Institute of Standards and Technology (MNIST) database of handwritten digit recognition. The experiments showed that our proposed approach can find an architecture that is competitive to the state-of-the-art models with a testing error of 0.87%.
Supervised machine learning based liver disease prediction approach with LASS...journalBEEI
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
A secure and energy saving protocol for wireless sensor networksjournalBEEI
The research domain for wireless sensor networks (WSN) has been extensively conducted due to innovative technologies and research directions that have come up addressing the usability of WSN under various schemes. This domain permits dependable tracking of a diversity of environments for both military and civil applications. The key management mechanism is a primary protocol for keeping the privacy and confidentiality of the data transmitted among different sensor nodes in WSNs. Since node's size is small; they are intrinsically limited by inadequate resources such as battery life-time and memory capacity. The proposed secure and energy saving protocol (SESP) for wireless sensor networks) has a significant impact on the overall network life-time and energy dissipation. To encrypt sent messsages, the SESP uses the public-key cryptography’s concept. It depends on sensor nodes' identities (IDs) to prevent the messages repeated; making security goals- authentication, confidentiality, integrity, availability, and freshness to be achieved. Finally, simulation results show that the proposed approach produced better energy consumption and network life-time compared to LEACH protocol; sensors are dead after 900 rounds in the proposed SESP protocol. While, in the low-energy adaptive clustering hierarchy (LEACH) scheme, the sensors are dead after 750 rounds.
Customized moodle-based learning management system for socially disadvantaged...journalBEEI
This study aims to develop Moodle-based LMS with customized learning content and modified user interface to facilitate pedagogical processes during covid-19 pandemic and investigate how teachers of socially disadvantaged schools perceived usability and technology acceptance. Co-design process was conducted with two activities: 1) need assessment phase using an online survey and interview session with the teachers and 2) the development phase of the LMS. The system was evaluated by 30 teachers from socially disadvantaged schools for relevance to their distance learning activities. We employed computer software usability questionnaire (CSUQ) to measure perceived usability and the technology acceptance model (TAM) with insertion of 3 original variables (i.e., perceived usefulness, perceived ease of use, and intention to use) and 5 external variables (i.e., attitude toward the system, perceived interaction, self-efficacy, user interface design, and course design). The average CSUQ rating exceeded 5.0 of 7 point-scale, indicated that teachers agreed that the information quality, interaction quality, and user interface quality were clear and easy to understand. TAM results concluded that the LMS design was judged to be usable, interactive, and well-developed. Teachers reported an effective user interface that allows effective teaching operations and lead to the system adoption in immediate time.
Understanding the role of individual learner in adaptive and personalized e-l...journalBEEI
Dynamic learning environment has emerged as a powerful platform in a modern e-learning system. The learning situation that constantly changing has forced the learning platform to adapt and personalize its learning resources for students. Evidence suggested that adaptation and personalization of e-learning systems (APLS) can be achieved by utilizing learner modeling, domain modeling, and instructional modeling. In the literature of APLS, questions have been raised about the role of individual characteristics that are relevant for adaptation. With several options, a new problem has been raised where the attributes of students in APLS often overlap and are not related between studies. Therefore, this study proposed a list of learner model attributes in dynamic learning to support adaptation and personalization. The study was conducted by exploring concepts from the literature selected based on the best criteria. Then, we described the results of important concepts in student modeling and provided definitions and examples of data values that researchers have used. Besides, we also discussed the implementation of the selected learner model in providing adaptation in dynamic learning.
Prototype mobile contactless transaction system in traditional markets to sup...journalBEEI
One way to prevent and reduce the spread of the covid-19 pandemic is through physical distancing program. This research aims to develop a prototype contactless transaction system using digital payment mechanisms and QR code technology that will be applied in traditional markets. The method used in the development of electronic market systems is a prototype approach. The application of QR code and digital payments are used as a solution to minimize money exchange contacts that are common in traditional markets. The results showed that the system built was able to accelerate and facilitate the buying and selling transaction process in traditional market environment. Alpha testing shows that all functional systems are running well. Meanwhile, beta testing shows that the user can very well accept the system that was built. The results of the study also show acceptance of the usefulness of the system being built, as well as the optimism of its users to be able to take advantage of this system both technologically and functionally, so its can be a part of the digital transformation of the traditional market to the electronic market and has become one of the solutions in reducing the spread of the current covid-19 pandemic.
Wireless HART stack using multiprocessor technique with laxity algorithmjournalBEEI
The use of a real-time operating system is required for the demarcation of industrial wireless sensor network (IWSN) stacks (RTOS). In the industrial world, a vast number of sensors are utilised to gather various types of data. The data gathered by the sensors cannot be prioritised ahead of time. Because all of the information is equally essential. As a result, a protocol stack is employed to guarantee that data is acquired and processed fairly. In IWSN, the protocol stack is implemented using RTOS. The data collected from IWSN sensor nodes is processed using non-preemptive scheduling and the protocol stack, and then sent in parallel to the IWSN's central controller. The real-time operating system (RTOS) is a process that occurs between hardware and software. Packets must be sent at a certain time. It's possible that some packets may collide during transmission. We're going to undertake this project to get around this collision. As a prototype, this project is divided into two parts. The first uses RTOS and the LPC2148 as a master node, while the second serves as a standard data collection node to which sensors are attached. Any controller may be used in the second part, depending on the situation. Wireless HART allows two nodes to communicate with each other.
Implementation of double-layer loaded on octagon microstrip yagi antennajournalBEEI
A double-layer loaded on the octagon microstrip yagi antenna (OMYA) at 5.8 GHz industrial, scientific and medical (ISM) Band is investigated in this paper. The double-layer consist of two double positive (DPS) substrates. The OMYA is overlaid with a double-layer configuration were simulated, fabricated and measured. A good agreement was observed between the computed and measured results of the gain for this antenna. According to comparison results, it shows that 2.5 dB improvement of the OMYA gain can be obtained by applying the double-layer on the top of the OMYA. Meanwhile, the bandwidth of the measured OMYA with the double-layer is 14.6%. It indicates that the double-layer can be used to increase the OMYA performance in term of gain and bandwidth.
The calculation of the field of an antenna located near the human headjournalBEEI
In this work, a numerical calculation was carried out in one of the universal programs for automatic electro-dynamic design. The calculation is aimed at obtaining numerical values for specific absorbed power (SAR). It is the SAR value that can be used to determine the effect of the antenna of a wireless device on biological objects; the dipole parameters will be selected for GSM1800. Investigation of the influence of distance to a cell phone on radiation shows that absorbed in the head of a person the effect of electromagnetic radiation on the brain decreases by three times this is a very important result the SAR value has decreased by almost three times it is acceptable results.
Exact secure outage probability performance of uplinkdownlink multiple access...journalBEEI
In this paper, we study uplink-downlink non-orthogonal multiple access (NOMA) systems by considering the secure performance at the physical layer. In the considered system model, the base station acts a relay to allow two users at the left side communicate with two users at the right side. By considering imperfect channel state information (CSI), the secure performance need be studied since an eavesdropper wants to overhear signals processed at the downlink. To provide secure performance metric, we derive exact expressions of secrecy outage probability (SOP) and and evaluating the impacts of main parameters on SOP metric. The important finding is that we can achieve the higher secrecy performance at high signal to noise ratio (SNR). Moreover, the numerical results demonstrate that the SOP tends to a constant at high SNR. Finally, our results show that the power allocation factors, target rates are main factors affecting to the secrecy performance of considered uplink-downlink NOMA systems.
Design of a dual-band antenna for energy harvesting applicationjournalBEEI
This report presents an investigation on how to improve the current dual-band antenna to enhance the better result of the antenna parameters for energy harvesting application. Besides that, to develop a new design and validate the antenna frequencies that will operate at 2.4 GHz and 5.4 GHz. At 5.4 GHz, more data can be transmitted compare to 2.4 GHz. However, 2.4 GHz has long distance of radiation, so it can be used when far away from the antenna module compare to 5 GHz that has short distance in radiation. The development of this project includes the scope of designing and testing of antenna using computer simulation technology (CST) 2018 software and vector network analyzer (VNA) equipment. In the process of designing, fundamental parameters of antenna are being measured and validated, in purpose to identify the better antenna performance.
Transforming data-centric eXtensible markup language into relational database...journalBEEI
eXtensible markup language (XML) appeared internationally as the format for data representation over the web. Yet, most organizations are still utilising relational databases as their database solutions. As such, it is crucial to provide seamless integration via effective transformation between these database infrastructures. In this paper, we propose XML-REG to bridge these two technologies based on node-based and path-based approaches. The node-based approach is good to annotate each positional node uniquely, while the path-based approach provides summarised path information to join the nodes. On top of that, a new range labelling is also proposed to annotate nodes uniquely by ensuring the structural relationships are maintained between nodes. If a new node is to be added to the document, re-labelling is not required as the new label will be assigned to the node via the new proposed labelling scheme. Experimental evaluations indicated that the performance of XML-REG exceeded XMap, XRecursive, XAncestor and Mini-XML concerning storing time, query retrieval time and scalability. This research produces a core framework for XML to relational databases (RDB) mapping, which could be adopted in various industries.
Key performance requirement of future next wireless networks (6G)journalBEEI
Given the massive potentials of 5G communication networks and their foreseeable evolution, what should there be in 6G that is not in 5G or its long-term evolution? 6G communication networks are estimated to integrate the terrestrial, aerial, and maritime communications into a forceful network which would be faster, more reliable, and can support a massive number of devices with ultra-low latency requirements. This article presents a complete overview of potential 6G communication networks. The major contribution of this study is to present a broad overview of key performance indicators (KPIs) of 6G networks that cover the latest manufacturing progress in the environment of the principal areas of research application, and challenges.
Noise resistance territorial intensity-based optical flow using inverse confi...journalBEEI
This paper presents the use of the inverse confidential technique on bilateral function with the territorial intensity-based optical flow to prove the effectiveness in noise resistance environment. In general, the image’s motion vector is coded by the technique called optical flow where the sequences of the image are used to determine the motion vector. But, the accuracy rate of the motion vector is reduced when the source of image sequences is interfered by noises. This work proved that the inverse confidential technique on bilateral function can increase the percentage of accuracy in the motion vector determination by the territorial intensity-based optical flow under the noisy environment. We performed the testing with several kinds of non-Gaussian noises at several patterns of standard image sequences by analyzing the result of the motion vector in a form of the error vector magnitude (EVM) and compared it with several noise resistance techniques in territorial intensity-based optical flow method.
Modeling climate phenomenon with software grids analysis and display system i...journalBEEI
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Plant leaf identification system using convolutional neural network
1. Bulletin of Electrical Engineering and Informatics
Vol. 10, No. 6, December 2021, pp. 3341~3352
ISSN: 2302-9285, DOI: 10.11591/eei.v10i6.2332 3341
Journal homepage: http://beei.org
Plant leaf identification system using convolutional neural
network
Amiruzzaki Taslim1
, Sharifah Saon2
, Abd Kadir Mahamad3
, Muladi Muladi4
, Wahyu Nur Hidayat5
1
Idependent Reseacher, Kanchong Darat, Banting, Selangor, Malaysia
2,3
Faculty of Electrical and Electronic Engineering (FKEE), Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia
4,5
Department of Electrical Engineering, Universitas Negeri Malang, Malang, Indonesia
Article Info ABSTRACT
Article history:
Received Mar 3, 2020
Revised May 10, 2021
Accepted Oct 15, 2021
This paper proposes a leaf identification system using convolutional neural
network (CNN). This proposed system can identify five types of local
Malaysia leaf which were acacia, papaya, cherry, mango and rambutan. By
using CNN from deep learning, the network is trained from the database that
acquired from leaf images captured by mobile phone for image classification.
ResNet-50 was the architecture has been used for neural networks image
classification and training the network for leaf identification. The recognition
of photographs leaves requested several numbers of steps, starting with image
pre-processing, feature extraction, plant identification, matching and testing,
and finally extracting the results achieved in MATLAB. Testing sets of the
system consists of 3 types of images which were white background, and noise
added and random background images. Finally, interfaces for the leaf
identification system have developed as the end software product using
MATLAB app designer. As a result, the accuracy achieved for each training
sets on five leaf classes are recorded above 98%, thus recognition process was
successfully implemented.
Keywords:
Convolutional neural network
Image classification
Leaf identification system
ResNet-50
This is an open access article under the CC BY-SA license.
Corresponding Author:
Sharifah Saon
Faculty of Electrical and Electronic Engineering (FKEE)
Universiti Tun Hussein Onn Malaysia (UTHM)
86400 Parit Raja, Batu Pahat, Malaysia
Email: sharifa@uthm.edu.my
1. INTRODUCTION
Over the last few decades, machine vision [1]-[3], digital image processing and analysis [4]-[6],
have been discussed and thrived. This point of view is an important element of artificial intelligence [7], [8]
and develop a strong correlation of human-machine technology. Such techniques were expected to be used in
agriculture or natural habitats [9]. Technology nowadays is advance enough to make people realise it was
easier to learn new things. With the popularity of smartphones, people can easily reach their phone to
discover more about the surrounding nature. The planet inherits a massive number of species of plants.
Current floral species estimates range from 220,000 to 420,000 [10]. Identifying plants was an essential and
challenging task. Leaf shape description was the key problem in leaf identification [11], [12]. To date, several
shape characteristics have been derived to explain the form of the leaf.
Plant recognition for plant species management was significant in agriculture; thus, we firmly
believe, botanists can use this system in honour of leaf for medicinal [13]. Most of the plants' leaf has
specific characteristics, and each of characteristics can be used for plants identification. Plant recognition and
identification [14], [15] were the most exciting topic in image processing, especially in the context of leaf
image retrieval. Nonetheless, leaves were natural objects whose morphological complexity was complicated.
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Thus it was necessary to distinguish between types of leaves [16]. The main target of the project was to
proposed a leaf recognition system based on specific characteristics from leaf images by using deep learning
technique from MATLAB, that contribute an approach in which the plant was classified based on the parts of
its leaves.
Healthy trends show lots of people; adults and kids were involved with healthy activities, such as
walking, jogging, and hiking. However, only a few people can recognise trees just by looking at their leaves,
while kids eager to know more about plants. So, with a leaf identification algorithm, hopefully, people can
realise trees quickly by identify the leaves around them. These educational purposes hopefully will attract
more people close to plants and nature, thus gain new knowledge. Moreover, plant identification was a
critical concern for botanists, agricultural researchers and environmentalists. Human experts can manually
identify plants, but it was a time-consuming and low-efficiency operation [17]. This project presents an
approach for plant recognition using leaf image. Besides, the combination of MATLAB with the leaf
recognition system [18] has several advantages caused this method was efficient and straightforward. From
this point of view, improving the classification rate has been regarded considerably.
Convolutional neural network (CNN), plant classification was a technique used to classify or
grouping plants into several numbers of ranks and groups with similarity properties. This group of plants then
categories into sub-group to identify the similarity components of the plants. It was a vital process thus useful
for the researchers to study behaviour, properties and similarity of each plant and a group of plants [19].
Classification of plants was considered a challenging task even for expert biologists. Thus, the technology of
computer vision valuable to simplify this activity to improve the biologists’ research and to be a non-expert
educational tool in the field. Many studies on automatic plant classification have been carried out for many
years.
Previously, plants identification was totally based on human experience and knowledge to identify
the plants for daily intake, medical resolve, nourishment and into a lot of industries, in the other hand to
reduce the risk of using the wrong plant for extraction of medicinal products and to avoid the fatal errors that
may cause a problem to the patients. Botanists and computer scientists were inspired by the advent of digital
devices and computer vision possibilities to create computerized systems or semi-automatic systems for plant
classification or recognition based on different features. A lot of different techniques can be used to classify
plants using the leaves of the plant. Research on leaf classification has a lot of opportunities to discover
rather than flowers. Leaf identification proses based on different biometric qualities of leaf takes much longer
and expensive. This biometric quality can be categories as the colour, venation, tissue and shape of the leaf.
For example, leaf classification as on colour that composition resemblance among both of two images was
related impression of sunlight on season [20].
The CNN was a class of deep learning neural networks, that represent a vast potential in the
identification of pictures. They were most commonly used for the analysis of visual images and often work
behind the scenes in the classification of images. CNN structured based three main layers, which are input,
output, and hidden layers. The hidden layers usually consist of convolutional, ReLU, pooling, and fully
connected layers;
a. Convolutional layers transferring the information to the next layer by applying a convolution procedure
from the input layers
b. Pooling blends the neuron cluster outputs in the next layer into a single neuron.
c. Fully connected layers link each neuron in one layer to the next layer of each neuron.
CNN was an in-depth learning approach that was used extensively to solve complex problems, that
able to surpasses the limits of traditional approaches to machine learning. A CNN works by eliminates the
need for manual feature extraction, and it does not train the features, yet the images features were extracted
automatically. Feature detection was to train and learn through tens to hundreds of hidden layers, thus makes
CNN models extremely accurate for machine learning tasks. Each layer increases the complexity of the
features learned. There are three learning method of machine learning, which were supervised, without
supervision and semi-supervised learning [21].
Starting with an input image, a CNN has a lot of different filters used to develop a function map,
then to increase non-linearity the ReLU function was applied, and a pooling layer was added to each function
map. The pooled images are flattening into one long vector and fed into a fully connected artificial neural
network. The features then process through the system. The final fully connected layer provides the “voting”
of the classes. It trains through forwarding propagation and backpropagation for a lot of epochs and should be
replicated until a well-defined neural network with trained weights and detectors function. The neural
network [22], [23] can be used to take out models and notice fashions these are overly complex found by
individuals or other computer techniques. A trained neural network can be thought of as an “expert” in the
form of information to be evaluated. This expert can then be used to make forecasts of new conditions of
importance and address “what if”. The upside on choosing CNN for this project is based on;
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a. Adapt learning: a potential to learn how to perform tasks based on instruction or first trial data
b. Self-organization: neural network represents the information for the duration of the learning process.
c. Coding: partial failure of a network results in subsequent performance degradation.
ResNet architecture, ResNet-50 CNN [24] was 50 layers deep and trained on more than a million
images from the ImageNet database and able to classify images into 1000 object categories, such as animals,
pen, and computer. The network has acquired rich representations of features for a wide range of images as a
result. The network able to train an image input size up to 224-by-224. A simple idea of ResNet-50: skip the
data to the next layers by feed the output of two successive convolutional layers. Yet they cross two layers
here and extend to large scales.
Figure 1 shows a ResNet residual block which convolutional layer and bypass the input to the next
layers. Bypassing two layers was a key insight, since bypassing one layer did not give much change. By two
layers can be thought as a small classifier or a Network-In-Network. It's also the very first time a network of
over 100, perhaps 1000 layers has been equipped.
Figure 1. ResNet residual block
This layer reduces the number of features at each layer by first using a 1x1 convolution with a
smaller output (usually 1/4 of the input), and then a 3x3 layer, and then again, a 1x1 convolution to a larger
number of features. As with Inception modules, this allows the calculation to be kept low while delivering a
rich combination of features. ResNet also uses a layer of pooling plus softmax as the final grouping.
GoogleNet architecture, researchers at Google introduced the Inception network, which took first
place in the 2014 ImageNet competition for detection and classification challenges. Figure 2 shows the
inception module that was used in GoogleNet architecture for neural network. The model consists of a basic
unit called an "inception cell" in which a sequence of convolutions was carried out at different scales, and the
effects are then aggregated. To save computation, 1x1 convolutions were used to reduce the input channel
depth. For each cell, a set of 1x1, 3x3, and 5x5 filters was learned to extract features at different scales from
the input. Max pooling was also used to maintain the proportions of "same" padding so that the output can be
connected properly.
GoogleNet used a stem without inception modules as initial layers, and an average pooling plus
softmax classifier was like network-in-network (NiN). This architecture was split into 22 thick layers. It
reduces the number of parameters to 4 million from 60 million (AlexNet). This classifier was also extremely
low compared to AlexNet and VGG operations, that helped to create a very efficient network.
Figure 2. GoogleNet Inception module
ResNet and GoogleNet comparison, in 2015, ResNet won ILSVRC with a 152-layer architecture
and reached a top-5 classification error of 3.6%, 5.1% higher than human performance [25]. CNN's provide
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encouraging and continuously improving results on automatic plant species identification as regards other
object classification problems. After comparing, since ResNet-50 gives better results and less overfitting, the
suitable architecture for this project was ResNet. Assume that this selection due to ResNet50 being broader
but still less nuanced. At the same time, it produces a lower-dimensional feature vector, which was likely due
to the use of a more robust Average Pooling with a pool size of 7x7. It saves the effort to reduce the
dimension.
2. RESEARCH METHOD
Leaf identification system was a system used for plant recognition to identify the species of plant,
thus expecting to optimise the botanists' task and can be used as a kids education platform in recognising the
plants. Figure 3 shows the basic flow of automatically or digital leaf identification system. Input images can
be from a camera or pictures from the internet and sent to the database for image classification. Deep
learning machine will learn to cluster and classify the images then process the images through CNN. Once
the process complete, the system can recognize the leaf images, which differed from the images used at the
database. Finally, a graphical user interface (GUI) was created to assist the leaf identification process.
Figure 3. Basic flow of the leaf identification system
2.1. Images for data collection
All the images that acquired were stored in a database for data collection. Each image in dataset
having a white background and with no leafstalk. Then the current images were augmented such as rotate or
flip. Besides, filter from photo editor also applied to the raw images. This process was to increase the number
of data collection to increase accuracy for the leaf identification system. The resolution of each image was
1024 × 960. The total number of the leaf images of these five trees were up to 375. More specifically, there
were 75 images for mango, 75 images for acacia (as in Figure 4), 75 images of papaya, 75 images of
rambutan and 75 images of cherry. The training sets and test sets were generated randomly with 70% images
for training and 30% images for testing.
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Plant leaf identification system using convolutional neural network (Amiruzzaki Taslim)
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Figure 4. Data collection of acacia leaves
2.2. Convolutional neural network method
This method considers the colour and shape features of the leaf. Leaves of different plants were
invariably similar in colour and shape; therefore, a single feature alone may not produce expected results.
The method of image search and retrieval mainly focuses on the generation of the colour feature vector by
calculating the average means. Then for each plane row mean and column mean of colours were calculated.
The average of all row means and all columns means was calculated for each plane. The features of all three
planes were combined to form a feature vector. Once the feature vectors were generated for an image, they
are stored in a feature database. Neural network class encapsulates all the layers of the network. It has
methods to train the network using mini-batch gradient descent, to compute the result of input, perform cross-
validation of the network, reset the weights of the network, and write the network to a file [26], [27].
CNN's main building block was the convolutional layer. Three hyperparameters control the size of
the convolution layer output volume: the depth, stride and zero-padding. The spatial volume of the output, O
can be determined. As shown in (1) used to determine the total number of neurons that "fit" in a given
volume. With the input volume size of W, the kernel field size of the convolutional layer neurons of K, stride
applied of S and amount of zero paddings of P.
1
2
+
+
−
=
S
P
K
W
O (1)
2.3. Training and test sets
The general statistical classification was the method of defining a set of classes or categories to
which a new observation belongs, based on preliminary information such as a data set for training.
Specifically, the procedure used to allocate a specific plant species to an image, based on its feature set, have
been listed in this research. It was also a subset of the more general statistical and machine learning
classification problem, namely supervised learning.
From the database that has been collected, which total up to 375 images from 5 classes then run a
neural network process for classification. The network was trained and tested on leaf images from the
database. The dataset only included pictures of leaves on a white background. All networks are trained using
minibatch gradient descent with a learning rate of 0.01. Each network was trained until the validation
accuracy reach maximum accuracy. Thus, the number of times taken differ with each training and test sets.
Furthermore, some epochs also differ with each leaf classes. This model was trained by an
inconstant number of iterations on the training sets of 375 images by utilizing an NVIDIA GTX 1650 4GB
GPU. Simplify as a block diagram in Figure 5.
2.4. Classification
As for classification, CNN algorithm has been calculated and classified the leaves from the database
and recognized the new leaf images from the input. The ResNet-50 was a CNN that was trained on more than
a million images from the ImageNet database. Thus, the identification system was able to recognize more
than 90% accuracy from the images in the database. During the classification method, accuracy was checked
to measure how accurate the machine works during the process. Accuracy was a metric used to evaluate
models of classification. The formal definition of accuracy was stated in (2);
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Bulletin of Electr Eng & Inf, Vol. 10, No. 6, December 2021 : 3341 – 3352
3346
s
prediction
of
number
Total
s
prediction
correct
of
Number
Accuracy = (2)
For binary classification in terms of positives and negatives, accuracy can also be measured using
(3), with TP, TN, FP and FN are for true positives, true negatives, false positive and false negatives,
respectively.
FN
FP
TN
TP
TN
TP
Accuracy
+
+
+
+
= (3)
Figure 5. Block diagram for training and testing
2.5. Graphical user interfaces
GUI or Graphical User Interfaces were the final presentations of this leaf identification system. As
the system can recognize the images, this interface has been guided the user to insert the desired leaf image
for recognition purpose, and the interface started to run the program to identify the leaves thus completed the
output of this project which was able to identify the leaves. For this part, the suitable method was also by
using MATLAB interface creator which called App designer. By combining the coding of a neural network
into the App designer, the interface was completed as per design.
3. RESULTS AND DISCUSSION
The result and analysis that has been done for this project were data collection and the algorithm of
CNN through MATLAB to classify the leaves types. There were several steps in order to achieve the desired
results as the output of this project. The main result was to ensure the successfulness of the system to identify
the leaves from input images.
3.1. Training results
The leaves identification for this project required several steps. For the result, the output obtained
from the MATLAB command window. By deep learning machine with an application of CNN, the system
determined and classified the leaves from data collection by Resnet-50, which was the feature extraction. In
this case, 50 layers, with 49 Convolution layers and one FC layer on top. Except for the first Convolution
layer, the rest 48 composes 16 “residual” blocks in 4 stages. The block has a familiar architecture within each
level, i.e. the same input and output shape, as shown in Figure 6.
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Figure 6. Architecture of Resnet-50
After simulating in MATLAB, each layer of the feature extraction completes the task to identify
leaves and classify it accordingly. Resnet50 architecture was used in the training progress. For epoch, the
system stops trained until the validation accuracy achieve its maximum accuracy, to ensure perfect reading of
accuracy that has been trained on the network. The limit of the epoch can be up to 100 epochs and base
learning rate of constant 0.01. Figure 7 shows the training images from the database of acacia.
Figure 7. Example of training results (Acacia)
By running the training process for simulation, several functions were read, and the data was
tabulated for each leaf classes. Those functions include validation accuracy, elapsed time, epoch, iteration,
iterations per epoch and learning rate. Figure 8 clearly shows that all the accuracy was above 98 % because
of the training network used was pictures/images that suitable for the learning process in deep learning. The
best validation accuracy among five leaf classes were papaya leaves with 99.70% while the lowest was
rambutan leaves with 98.60%. Means that, papaya leaves were more comfortable to learn by the network due
to the unique shape that differs from most of the other leaves. For an elapsed time, the quickest elapsed time
was mango leaves with 8 seconds while the longest elapsed time was cherry leaves with 23 seconds. Epoch
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used for each training progress may differ as it depends on elapsed time. Thus, the shortest epoch was on
mango leaves with eight reading while the highest epoch was on papaya, cheery and rambutan leaves
altogether. Same with iteration, it depends on the epoch used, so the highest iteration was on papaya, cherry
and rambutan leave with 403 iterations.
Figure 8. Validation accuracy of 5 leaf classes
3.2. Testing results
For the input image used for testing, there are three types of images involved, which were image
with white background noise and image with two different random background. Noise can be added using
MATLAB coding. Figure 9 and 10, show several numbers of sample of input image used for testing and GUI
of leaf identification, respectively. Figure 10 shows that all five leaves are successfully recognized by the
system.
Figure 9. Sample of leaves used for testing
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(a) (b)
(c)
(d) (e)
Figure 10. Results on GUI interface of, (a) mango, (b) rambutan, (c) cherry, (d) papaya, (e) acacia
3.3. Comparison of training sets on different neural network architecture
For results comparison, there is another architecture of neural network used, which was GoogleNet.
Thus, the comparison was made between ResNet50 and GoogleNet neural network architecture. The
database used for this training sets were the same as the existing database used in leaf classification for this
system. The comparison was for research purpose in order to achieve more output for the project and analysis
of the results.
The system uses ResNet-50 architecture in a neural network which has high accuracy in the
network; meanwhile, GoogleNet architecture has high accuracy in a neural network. So, comparison of
results between these two architectures has been made to compare the main criteria in a neural network such
as validation accuracy, loss, elapsed time, epoch, iteration, frequency and learning rate. Based on the results,
these two architectures do not differ much, and the accuracy was still high, which was above 98%, and the
elapsed time was acceptable, which was below 20 seconds. Both architectures were suitable for the image
classification purpose.
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3.4. Project analysis
Based on the project results, the created system was able to identify the leaves from the database
that has been created from the first phase of the project. These leaves classes which were acacia, papaya [28],
cherry, mango [29] and rambutan has successfully identified when testing with different image and
environment with the accuracy of more than 98%. The accuracy was high based on related works of the leaf
identification system using CNN. When tested with a random environment, sometimes the system cannot
identify the correct leaves due to the same colour of the environment might interrupt the identification
system. Nevertheless, when testing with white background, the system can successfully detect the leaves
without error, although the images were added with noise (speckle).
4. CONCLUSION
Recognition of leaves has been explored in various scientific articles and studies. It can make a
significant contribution to plant classification research. This work has been proposed in order to introduce an
automatic identification or classification of leaves using CNN. A method of leaf identification address two
essential points: the fundamental characteristics of the leaf and the recognition or classification of these
leaves. In neural networks, the networks seek to identify leaf sets is based on their colour concentration
without carrying out quantitative or computational studies. The use of a neural network for leaf identification
and the plant's classification has been conducted successfully. Several set of experiments are arranged in this
work, including training and testing of CNN. The results reported in this work proved that the use of CNN
for classification of plants based on the images of their leaves is a promising idea, shows that the ability to
use neural networks in the classification process for leaving recognition tasks and machine vision application.
The different architecture of CNN differs the result of the validation of accuracy. However, for classification,
it still can identify images without high loss which are acceptable and valid for the identification process.
Besides, there is a lot of other neural network architecture besides ResNet and GoogleNet.
ACKNOWLEDGEMENTS
Communication of this research is made possible through monetary assisstance by Universiti Tun
Hussein Onn Malaysia and the UTHM Publisher’s Office via Publication Fund E15216.
REFERENCES
[1] D. Bambil et al., "Plant species identification using color learning resources, shape, texture, through machine
learning and artificial neural networks," Environment Systems and Decisions, vol. 40, no. 4, pp. 480-484, 2020, doi:
10.1007/s10669-020-09769-w.
[2] H. U. Rehman, S. Anwar and M. Tufail, "Machine vision-based plant disease classification through leaf
imagining," Ingenierie Des Systemes d'Information, vol. 25, no. 4, pp. 437-444, 2020, doi: 10.18280/isi.250405.
[3] A. Soleimanipour and G. R. Chegini, "A vision-based hybrid approach for identification of anthurium flower
cultivars," Computers and Electronics in Agriculture, vol. 174, p. 105460, 2020, doi:
10.1016/j.compag.2020.105460.
[4] R. A. Asmara, M. Mentari, N. S. Herawati Putri and A. Nur Handayani, "Identification of Toga Plants Based on
Leaf Image Using the Invariant Moment and Edge Detection Features," 2020 4th International Conference on
Vocational Education and Training (ICOVET), 2020, pp. 75-80, doi: 10.1109/ICOVET50258.2020.9230343.
[5] I. Ariawan, Y. Herdiyeni and I. Z. Siregar, "Geometric morphometric analysis of leaf venation in four shorea
species for identification using digital image processing," Biodiversitas, vol. 21, no. 7, pp. 3303-3309, 2020, doi:
10.13057/biodiv/d210754.
[6] M. Y. Braginskii and D. V. Tarakanov, "Identification of plants condition using digital images," Journal of
Physics: Conference Series, vol. 1353, no. 1, pp. 1-4, 2019, doi: 10.1088/1742-6596/1353/1/012108.
[7] Y. Ampatzidis et al., "Vision-based system for detecting grapevine yellow diseases using artificial intelligence,"
Acta Horticulturae, vol. 1279, pp. 225-230, 2020, doi: 10.17660/ActaHortic.2020.1279.33.
[8] G. Ramesh, D. W. Albert and G. Ramu, "Detection of plant diseases by analyzing the texture of leaf using ANN
classifier," International Journal of Advanced Science and Technology, vol. 29, no. 8, pp.1656-1664, 2020.
[9] B. Vijaya, "Leaf Recognition and Matching with Matlab," Thesis. Faculty of San Diego State University; 2014.
[10] W. Jana, M. Rzanny, M. Seeland, P. Mader, "Automated plant species identification-Trends and future directions,"
PLOS Computional Biology, pp. 1-19, 2018, doi: 10.1371/journal.pcbi.1005993.
[11] D. A. Kumar, P. S. Chakravarthi and K. S. Babu, "Multiclass Support Vector Machine based Plant Leaf Diseases
Identification from Color, Texture and Shape Features," 2020 Third International Conference on Smart Systems
and Inventive Technology (ICSSIT), 2020, pp. 1220-1226, doi: 10.1109/ICSSIT48917.2020.9214142.
[12] M. Keivani, J. Mazloum, E. Sedaghatfar and M. Tvakoli, "Automated analysis of leaf shape, texture and color
features for plant classification," Traitement Du Signal, vol. 37, no. 1, pp. 17-28, 2020, doi: 10.18280/ts.370103.
11. Bulletin of Electr Eng & Inf ISSN: 2302-9285
Plant leaf identification system using convolutional neural network (Amiruzzaki Taslim)
3351
[13] B. Wang, D. Brown, Y. Gao and J. L. Salle, "Mobile plant leaf identification using smart-phones," 2013 IEEE
International Conference on Image Processing, 2013, pp. 4417-4421, doi: 10.1109/ICIP.2013.6738910.
[14] Y. Li, J. Nie and X. Chao, "Do we really need deep CNN for plant diseases identification?," Computers and
Electronics in Agriculture, vol. 178, p. 105803, 2020, doi: 10.1016/j.compag.2020.105803.
[15] G. Singh, N. Aggarwal, K. Gupta and D. K. Misra, "Plant Identification Using Leaf Specimen," 2020 11th
International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2020, pp. 1-7,
doi: 10.1109/ICCCNT49239.2020.9225683.
[16] G. Cerutti, L. Tougne, J. Mille, A. Vacavant and D. Coquin, "A model-based approach for compound leaves
understanding and identification," 2013 IEEE International Conference on Image Processing, 2013, pp. 1471-1475,
doi: 10.1109/ICIP.2013.6738302.
[17] D. C. Nadine Jaan et al., "A Leaf Recognition of Vegetables Using Matlab," International Journal of Scientific &
Technology Research, vol. 5, no. 2, pp. 38-45, 2016.
[18] S. Ramya and R. Jeevitha, "Identification of Plant Species by Embedded Technology Using MatLab," 2020
International Conference on Computer Communication and Informatics (ICCCI), 2020, pp. 1-5, doi:
10.1109/ICCCI48352.2020.9104187.
[19] S. Boran and I. Yücel, "Leaves recognition system using a neural network," 12th International Conference on
Application of Fuzzy Systems and Soft Computing, ICAFS 2016, vol. 102, pp. 578-582, doi:
10.1016/j.procs.2016.09.445.
[20] İ. Yücel, "Leaves Recognition Systems Using Artificial Neural Network," Thesis. Graduate School of Applied
Sciences of Near East University, pp. 1-5, 2015.
[21] I. Sakshi, A. K. Goswami, S. P. Mishra and P. Asopa"Conceptual Understanding of Convolutional Neural
Network- A Deep Learning Approach," International Conference on Computational Intelligence and Data Science,
ICCIDS 2018, vol. 132, pp. 679-688, 2018, doi: 10.1016/j.procs.2018.05.06_9.
[22] G. Garg and M. Biswas, "Improved neural network-based plant diseases identification," Lecture Notes in Electrical
Engineering, vol. 668, pp. 79-89, 2021, doi: 10.1007/978-981-15-5341-76.
[23] A. Ahmed and S. E. Hussein, "Leaf identification using radial basis function neural networks and SSA based
support vector machine," PLoS ONE 15, vol 15, no. 8, p. e0237645, 2020, doi: 10.1371/journal.pone.0237645.
[24] Olga R. et al., "ImageNet large scale visual recognition challenge," International Journal of Computer Vision, vol.
115, no. 3, pp. 211-252, 2015, doi: 10.1007/s11263-015-0816-y.
[25] S. Vijay, A. Satya and S. Sharma, "An Automatic Leaf Recognition System for Plant Identification Using Machine
Vision Technology," International Journal of Engineering Science and Technology (IJEST), vol. 5, no. 4, pp. 874-
879, 2013.
[26] S. Parvatikar and D. Parasar, "Categorization of plant leaf using CNN," Lecture Notes in Networks and Systems,
vol. 146, pp. 79-89, 2021, doi: 10.1007/978-981-15-7421-4_7.
[27] M. Maheswari, P. Daniel, R. Srinivash and N. Radha, "Detection of diseased plants by using convolutional neural
network," Lecture Notes on Data Engineering and Communications Technologies, vol. 53, pp. 659-671, 2021, doi:
10.1007/978-981-15-5258-8_61.
[28] R. K. Veeraballi, M. S. Nagugari, C. S. R. Annavarapu and E. V. Gownipuram "Deep learning based approach for
classification and detection of papaya leaf diseases," Advances in Intelligent Systems and Computing, vol. 940, pp.
291-302, 2020, doi: 10.1007/978-3-030-16657-1_27.
[29] K. Pankaja and V. Suma, "Mango leaves recognition using deep belief network with MFO and multi-feature
fusion," Smart Innovation, Systems and Technologies, vol. 160, pp. 557-565, 2020, doi: 10.1007/978-981-32-9690-
9_61.
BIOGRAPHIES OF AUTHORS
Amiruzzaki Taslim received his Diploma in Electronic Engineering and Degree in Electronic
Engineering from Universiti Teknologi Malaysia and Universiti Tun Hussein Onn Malaysia, in
2015 and 2020, respectively. His research interest includes telecommunication, designing
circuit or plan, and machine learning software. Currently, he is working as Project Engineer at
Zenetac Group Sdn Bhd, a telecommunications company which is a panel vendor for TIME
dotCom Berhad. He mainly handles projects for fiber optic installation for TIME projects
around KL and Selangor, Malaysia.
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Sharifah Saon is currently a Senior Lecturer in the Faculty of Electrical and Electronic
Engineering, Universiti Tun Hussein Onn Malaysia, Malaysia and registered Professional
Technologists. She received the Bachelor of Science in Electrical Engineering and Master of
Electrical Engineering from Universiti Teknologi Malaysia, and Kolej Universiti Tun Hussein
Onn Malaysia, Malaysia, in 2001, and 2004, respectively. Her research interest is in the area of
theoretical digital signal processing, visible light communication and digital & data
communication, including the application to the Internet of Things (IoT) and big data analysis.
She is a member of IEEE, Institute of Engineering Malaysia (IEM), Board of Engineering
Malaysia (BEM), and Professional Technologist of Malaysia Board of Technologists (MBOT).
Abd Kadir Mahamad received his Bachelor of Science in Electrical Engineering (2002) and
Master of Electrical Engineering (2005) from University Tun Hussein Onn Malaysia before
pursuing Doctor of Philosophy (Computer Science and Electrical Engineering) at Kumamoto
University, Japan (2010). He currently an Associate Professor at Faculty of Electrical and
Electronic Engineering UTHM and registered as Professional Engineer. During the period of
May 2015 through May 2016, he was doing industrial attachment at Melaka ICT Holdings Sdn
Bhd, as Executive Assistant Manager. He was involved in the Smart City project in Melaka.
He currently leads a research team in Video Analytic and Internet of Things (IoT). His
research interests include Deep Learning, Smart City, Intelligent System applications and
embedded system. He is also a Senior Member of IEEE, Institute of Engineering Malaysia
(IEM) and Board of Engineering Malaysia (BEM).
Muladi received his Bachelor and Master degree from Institut Teknology Sepuluh Nopember
(ITS) Surabaya Indonesia both in Electrical Engineering at 1994 and 2002 respectively. He
received Doctor of Philosophy in Electrical Engineering from Universiti Teknologi Malaysia
at 2007. He is currently an Associate Professor at Department of Electrical Engineering, State
University of Malang (UM) where he currently leads the Telematics and Internet of Things
Research Group. He was appointed as the head of the Information Technology Center which
was responsible for designing and implementing the computer network system and
applications within the campus from 2008-2012. His research interest includes wireless
communication and network, signal and image processing, embedded and intelligent system,
and Internet of Things. He is a member of IEEE and Indonesia Electrical Engineering Forum
(FORTEI).
Wahyu Nur Hidayat received his Master of vocational education from Department of
Electrical Engineering, Universitas Negeri Malang. His research focuses on the technology-
enhanced learning which contain Artificial Intelligence. Some of his research focuses are on
personalize learning systems, intelligent tutorial systems, adaptive learning systems, online and
mobile learning. He is currently developing an artificial intelligence-based learning system
which is named personalize online training system and also mobile learning for several taught
courses. The whole system utilizes artificial intelligence to provide solutions for user learning
style preferences. His dedication in education research has earned him more than 20 copyrights
and several publications. He is a member of the IEEE, a certification manager at the
Professional Certification Body Universitas Negeri Malang and also as a computer and
network engineering assessor.