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Isinkaye FO, Olusanya MO, Singh PK. Deep learning and content-based filtering techniques for improving plant disease identification and treatment recommendations: A comprehensive review. Heliyon 2024; 10:e29583. [PMID: 38737274 PMCID: PMC11088271 DOI: 10.1016/j.heliyon.2024.e29583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 03/30/2024] [Accepted: 04/10/2024] [Indexed: 05/14/2024] Open
Abstract
The importance of identifying plant diseases has risen recently due to the adverse effect they have on agricultutal production. Plant diseases have been a big concern in agriculture, as they affect crop production, and constitute a major threat to global food security. In the domain of modern agriculture, effective plant disease management is vital to ensure healthy crop yields and sustainable practices. Traditional means of identifying plant disease are faced with lots of challenges and the need for better and efficient detection methods cannot be overemphazised. The emergence of advanced technologies, particularly deep learning and content-based filtering techniques, if integrated together can changed the way plant diseases are identified and treated. Such as speedy and correct identification of plant diseases and efficient treatment recommendations which are keys for sustainable food production. In this work, We try to investigate the current state of research, identified gaps and limitations in knowledge, and suggests future directions for researchers, experts and farmers that could help to provide better ways of mitigating plant disease problems.
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Affiliation(s)
- Folasade Olubusola Isinkaye
- Department of Computer Science and Information Technology, Sol Plaatje University Kimberley, 8301, South Africa
| | - Michael Olusoji Olusanya
- Department of Computer Science and Information Technology, Sol Plaatje University Kimberley, 8301, South Africa
| | - Pramod Kumar Singh
- Department of Computer Science and Engineering, ABV-Indian Institute of Information Technology and Management Gwalior, Gwalior, 474015, MP, India
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2
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Aldakheel EA, Zakariah M, Alabdalall AH. Detection and identification of plant leaf diseases using YOLOv4. FRONTIERS IN PLANT SCIENCE 2024; 15:1355941. [PMID: 38711603 PMCID: PMC11070553 DOI: 10.3389/fpls.2024.1355941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 03/27/2024] [Indexed: 05/08/2024]
Abstract
Detecting plant leaf diseases accurately and promptly is essential for reducing economic consequences and maximizing crop yield. However, farmers' dependence on conventional manual techniques presents a difficulty in accurately pinpointing particular diseases. This research investigates the utilization of the YOLOv4 algorithm for detecting and identifying plant leaf diseases. This study uses the comprehensive Plant Village Dataset, which includes over fifty thousand photos of healthy and diseased plant leaves from fourteen different species, to develop advanced disease prediction systems in agriculture. Data augmentation techniques including histogram equalization and horizontal flip were used to improve the dataset and strengthen the model's resilience. A comprehensive assessment of the YOLOv4 algorithm was conducted, which involved comparing its performance with established target identification methods including Densenet, Alexanet, and neural networks. When YOLOv4 was used on the Plant Village dataset, it achieved an impressive accuracy of 99.99%. The evaluation criteria, including accuracy, precision, recall, and f1-score, consistently showed high performance with a value of 0.99, confirming the effectiveness of the proposed methodology. This study's results demonstrate substantial advancements in plant disease detection and underscore the capabilities of YOLOv4 as a sophisticated tool for accurate disease prediction. These developments have significant significance for everyone involved in agriculture, researchers, and farmers, providing improved capacities for disease control and crop protection.
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Affiliation(s)
- Eman Abdullah Aldakheel
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Mohammed Zakariah
- Department of Computer Science, College of Computer and Information Science, King Saud University, Riyadh, Saudi Arabia
| | - Amira H. Alabdalall
- Department of Biology, College of Science, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
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3
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Liu L, Qiao S, Chang J, Ding W, Xu C, Gu J, Sun T, Qiao H. A multi-scale feature fusion neural network for multi-class disease classification on the maize leaf images. Heliyon 2024; 10:e28264. [PMID: 38689962 PMCID: PMC11059414 DOI: 10.1016/j.heliyon.2024.e28264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 03/14/2024] [Accepted: 03/14/2024] [Indexed: 05/02/2024] Open
Abstract
Maize is a globally important cereal crop, however, maize leaf disease is one of the most common and devastating diseases that afflict it. Artificial intelligence methods face challenges in identifying and classifying maize leaf diseases due to variations in image quality, similarity among diseases, disease severity, limited dataset availability, and limited interpretability. To address these challenges, we propose a residual-based multi-scale network (MResNet) for classifying multi-type maize leaf diseases from maize images. MResNet consists of two residual subnets with different scales, enabling the model to detect diseases in maize leaf images at different scales. We further utilize a hybrid feature weight optimization method to optimize and fuse the feature mapping weights of two subnets. We validate MResNet on a maize leaf diseases dataset. MResNet achieves 97.45% accuracy. The performance of MResNet surpasses other state-of-the-art methods. Various experiments and two additional datasets confirm the generalization performance of our model. Furthermore, thermodynamic diagram analysis increases the interpretability of the model. This study provides technical support for the disease classification of agricultural plants.
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Affiliation(s)
- Liangliang Liu
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, PR China
| | - Shixin Qiao
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, PR China
| | - Jing Chang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, PR China
| | - Weiwei Ding
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, PR China
| | - Cifu Xu
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, PR China
| | - Jiamin Gu
- College of Agriculture, Shihezi University, Shihezi, Xinjiang 832061, PR China
| | - Tong Sun
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, PR China
| | - Hongbo Qiao
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, PR China
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Zhou H, Chen J, Niu X, Dai Z, Qin L, Ma L, Li J, Su Y, Wu Q. Identification of leaf diseases in field crops based on improved ShuffleNetV2. FRONTIERS IN PLANT SCIENCE 2024; 15:1342123. [PMID: 38529064 PMCID: PMC10961419 DOI: 10.3389/fpls.2024.1342123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 02/21/2024] [Indexed: 03/27/2024]
Abstract
Rapid and accurate identification and timely protection of crop disease is of great importance for ensuring crop yields. Aiming at the problems of large model parameters of existing crop disease recognition methods and low recognition accuracy in the complex background of the field, we propose a lightweight crop leaf disease recognition model based on improved ShuffleNetV2. First, the repetition number and the number of output channels of the basic module of the ShuffleNetV2 model are redesigned to reduce the model parameters to make the model more lightweight while ensuring the accuracy of the model. Second, the residual structure is introduced in the basic feature extraction module to solve the gradient vanishing problem and enable the model to learn more complex feature representations. Then, parallel paths were added to the mechanism of the efficient channel attention (ECA) module, and the weights of different paths were adaptively updated by learnable parameters, and then the efficient dual channel attention (EDCA) module was proposed, which was embedded into the ShuffleNetV2 to improve the cross-channel interaction capability of the model. Finally, a multi-scale shallow feature extraction module and a multi-scale deep feature extraction module were introduced to improve the model's ability to extract lesions at different scales. Based on the above improvements, a lightweight crop leaf disease recognition model REM-ShuffleNetV2 was proposed. Experiments results show that the accuracy and F1 score of the REM-ShuffleNetV2 model on the self-constructed field crop leaf disease dataset are 96.72% and 96.62%, which are 3.88% and 4.37% higher than that of the ShuffleNetV2 model; and the number of model parameters is 4.40M, which is 9.65% less than that of the original model. Compared with classic networks such as DenseNet121, EfficientNet, and MobileNetV3, the REM-ShuffleNetV2 model not only has higher recognition accuracy but also has fewer model parameters. The REM-ShuffleNetV2 model proposed in this study can achieve accurate identification of crop leaf disease in complex field backgrounds, and the model is small, which is convenient to deploy to the mobile end, and provides a reference for intelligent diagnosis of crop leaf disease.
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Affiliation(s)
- Hanmi Zhou
- College of Agricultural Engineering, Henan University of Science and Technology, Luoyang, China
| | - Jiageng Chen
- College of Agricultural Engineering, Henan University of Science and Technology, Luoyang, China
| | - Xiaoli Niu
- College of Agricultural Engineering, Henan University of Science and Technology, Luoyang, China
| | - Zhiguang Dai
- College of Agricultural Engineering, Henan University of Science and Technology, Luoyang, China
| | - Long Qin
- College of Agricultural Engineering, Henan University of Science and Technology, Luoyang, China
| | - Linshuang Ma
- College of Agricultural Engineering, Henan University of Science and Technology, Luoyang, China
| | - Jichen Li
- College of Agricultural Engineering, Henan University of Science and Technology, Luoyang, China
| | - Yumin Su
- College of Agricultural Engineering, Henan University of Science and Technology, Luoyang, China
| | - Qi Wu
- College of Water Resource, Shenyang Agricultural University, Shenyang, China
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5
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Prasannakumar M, Latha K. Plant disease identification using contextual mask auto-encoder optimized with dynamic differential annealed optimization algorithm. Microsc Res Tech 2024; 87:484-494. [PMID: 37921010 DOI: 10.1002/jemt.24451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 08/24/2023] [Accepted: 10/17/2023] [Indexed: 11/04/2023]
Abstract
Most of the food consumed worldwide is produced by plants. Plant disease is a major cause of reduced production, but can be managed with regular monitoring. Manually observing plant diseases takes more time and is error-prone. Early detection of plant diseases with the aid of artificial intelligence and computer vision can decrease the effects of disease and help plants withstand the downsides of continuing surveillance. In this manuscript, plant disease identification using contextual mask auto-encoder optimized with dynamic differential annealed optimization algorithm (PDI-CMAE-DDAOA) is proposed. The plant village dataset is used to collect the images. Then the image is fed to preprocessing. Using an adaptive self-guided filter approach, the noise is removed from the input images during the pre-processing phase. The result of the pre-processing section serves as input for the feature extraction segment. Four statistical features, including mean, variance, entropy, and kurtosis, are recovered from the cosine similarity hidden Markov model (CSHMM). The contextual mask auto-encoder (CMAE) is given the extracted features to accurately classify the healthy and unhealthy regions of the plant image. The issue of slow convergence affects the CMAE. However, it is noted that the CMAE converges more quickly with deep learning features than with texture features in this instance. The CMAE classifier generally does not exhibit any adaptation of optimization algorithms for determining the best parameters to ensure the precise classification of plant disease. Therefore, dynamic differential annealed optimization algorithm (DDAOA) is considered to enhance the CMAE classifier, which accurately distinguishes between healthy and diseased plants. The proposed PDI-CMAE-DDAOA is done in Python. The efficacy of PDI-CMAE-DDAOA is evaluated under some performance metrics, like accuracy, precision, sensitivity, F1-score, specificity, error rate, receiver operating characteristic curve (ROC), computational time. The proposed method provides higher accuracy 23.34%, 34.33%, and 32.07%; higher sensitivity 36.67%, 36.33%, and 23.21%; higher F1-score 46.67%, 57.56%, and 43.21%; higher specificity 56.67%, 67.56%, and 23.21% analyzed with existing models, like transfer learning-based deep ensemble neural network for plant leaf infection recognition (PDI-DENN), plant disease detection with hybrid model based on convolutional auto-encoder and convolutional neural network (PDI-CAE-CNN), and automatic and reliable leaf disease finding depending on deep learning methods (PDI-EN-CNN), respectively. RESEARCH HIGHLIGHTS: To find the plant disease at early stage. To present PDI-CMAE-DDAOA. To get better classification accuracy by extracting the optimal features with the help of efficient CSHMM. To minimize the error during classification process. To maximize high area under curve value.
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Affiliation(s)
- M Prasannakumar
- Research Scholar, Department of Computer Science and Engineering, University College of Engineering (BIT Campus), Anna University, Tiruchirappalli, Tamil Nadu, India
| | - K Latha
- Assistant professor (Sr.Grade), Department of Computer Science and Engineering, University College of Engineering (BIT Campus), Anna University, Tiruchirappalli, Tamil Nadu, India
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6
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Kini AS, Prema KV, Pai SN. Early stage black pepper leaf disease prediction based on transfer learning using ConvNets. Sci Rep 2024; 14:1404. [PMID: 38228767 PMCID: PMC10791634 DOI: 10.1038/s41598-024-51884-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/10/2024] [Indexed: 01/18/2024] Open
Abstract
Plants get exposed to diseases, insects and fungus. This causes heavy damages to crop resulting in various leaves diseases. Leaf diseases can be diagnosed at an early stage with the aid of a smart computer vision system and timely disease prevention can be targeted. Black pepper is a medicinal plant that is extensively used in Ayurvedic medicine because of its therapeutic properties. The proposed work represents an intelligent transfer learning technique through state-of-the-art deep learning implementation using convolutional neural network to predict the presence of prominent diseases in black pepper leaves. The ImageNet dataset available online is used for training deep neural network. Later, this trained network is utilized for the prediction of the newly developed black pepper leaf image dataset. The developed data set consist of real time leaf images, which are candidly taken from the fields and annotated under supervision of an expert. The leaf diseases considered are anthracnose, slow wilt, early stage phytophthora, phytophthora and yellowing. The hyperparameters chosen for tuning in to deep learning models are initial learning rates, optimization algorithm, image batches, epochs, validation and training data, etc. The accuracy obtained with 0.001 learning rate ranges from 99.1 to 99.7% for the Inception V3, GoogleNet, SqueezeNet and Resnet18 models. Proposed Resnet18 model outperforms all model with 99.67% accuracy. The resulting validation accuracy obtained using these models is high and the validation loss is low. This work represents improvement in agriculture and a cutting edge deep neural network method for early stage leaf disease identification and prediction. This is an approach using a deep learning network to predict early stage black pepper leaf diseases.
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Affiliation(s)
- Anita S Kini
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, India
| | - K V Prema
- Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education (MAHE), Manipal, India.
| | - Smitha N Pai
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, India
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7
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Huang Z, Jiang X, Huang S, Qin S, Yang S. An efficient convolutional neural network-based diagnosis system for citrus fruit diseases. Front Genet 2023; 14:1253934. [PMID: 37693316 PMCID: PMC10484339 DOI: 10.3389/fgene.2023.1253934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 08/11/2023] [Indexed: 09/12/2023] Open
Abstract
Introduction: Fruit diseases have a serious impact on fruit production, causing a significant drop in economic returns from agricultural products. Due to its excellent performance, deep learning is widely used for disease identification and severity diagnosis of crops. This paper focuses on leveraging the high-latitude feature extraction capability of deep convolutional neural networks to improve classification performance. Methods: The proposed neural network is formed by combining the Inception module with the current state-of-the-art EfficientNetV2 for better multi-scale feature extraction and disease identification of citrus fruits. The VGG is used to replace the U-Net backbone to enhance the segmentation performance of the network. Results: Compared to existing networks, the proposed method achieved recognition accuracy of over 95%. In addition, the accuracies of the segmentation models were compared. VGG-U-Net, a network generated by replacing the backbone of U-Net with VGG, is found to have the best segmentation performance with an accuracy of 87.66%. This method is most suitable for diagnosing the severity level of citrus fruit diseases. In the meantime, transfer learning is applied to improve the training cycle of the network model, both in the detection and severity diagnosis phases of the disease. Discussion: The results of the comparison experiments reveal that the proposed method is effective in identifying and diagnosing the severity of citrus fruit diseases identification.
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Affiliation(s)
- Zhangcai Huang
- Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China
| | - Xiaoxiao Jiang
- Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China
| | - Shaodong Huang
- Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China
| | - Sheng Qin
- Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China
| | - Su Yang
- Department of Computer Science, Swansea University, Swansea, United Kingdom
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Gul Z, Bora S. Exploiting Pre-Trained Convolutional Neural Networks for the Detection of Nutrient Deficiencies in Hydroponic Basil. SENSORS (BASEL, SWITZERLAND) 2023; 23:5407. [PMID: 37420572 DOI: 10.3390/s23125407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/31/2023] [Accepted: 06/04/2023] [Indexed: 07/09/2023]
Abstract
Due to the integration of artificial intelligence with sensors and devices utilized by Internet of Things technology, the interest in automation systems has increased. One of the common features of both agriculture and artificial intelligence is recommendation systems that increase yield by identifying nutrient deficiencies in plants, consuming resources correctly, reducing damage to the environment and preventing economic losses. The biggest shortcomings in these studies are the scarcity of data and the lack of diversity. This experiment aimed to identify nutrient deficiencies in basil plants cultivated in a hydroponic system. Basil plants were grown by applying a complete nutrient solution as control and non-added nitrogen (N), phosphorous (P) and potassium (K). Then, photos were taken to determine N, P and K deficiencies in basil and control plants. After a new dataset was created for the basil plant, pretrained convolutional neural network (CNN) models were used for the classification problem. DenseNet201, ResNet101V2, MobileNet and VGG16 pretrained models were used to classify N, P and K deficiencies; then, accuracy values were examined. Additionally, heat maps of images that were obtained using the Grad-CAM were analyzed in the study. The highest accuracy was achieved with the VGG16 model, and it was observed in the heat map that VGG16 focuses on the symptoms.
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Affiliation(s)
- Zeki Gul
- Department of Computer Engineering, Ege University, 35100 Izmir, Turkey
| | - Sebnem Bora
- Department of Computer Engineering, Ege University, 35100 Izmir, Turkey
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Shahoveisi F, Taheri Gorji H, Shahabi S, Hosseinirad S, Markell S, Vasefi F. Application of image processing and transfer learning for the detection of rust disease. Sci Rep 2023; 13:5133. [PMID: 36991013 PMCID: PMC10060580 DOI: 10.1038/s41598-023-31942-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 03/20/2023] [Indexed: 03/31/2023] Open
Abstract
Plant diseases introduce significant yield and quality losses to the food production industry, worldwide. Early identification of an epidemic could lead to more effective management of the disease and potentially reduce yield loss and limit excessive input costs. Image processing and deep learning techniques have shown promising results in distinguishing healthy and infected plants at early stages. In this paper, the potential of four convolutional neural network models, including Xception, Residual Networks (ResNet)50, EfficientNetB4, and MobileNet, in the detection of rust disease on three commercially important field crops was evaluated. A dataset of 857 positive and 907 negative samples captured in the field and greenhouse environments were used. Training and testing of the algorithms were conducted using 70% and 30% of the data, respectively where the performance of different optimizers and learning rates were tested. Results indicated that EfficientNetB4 model was the most accurate model (average accuracy = 94.29%) in the disease detection followed by ResNet50 (average accuracy = 93.52%). Adaptive moment estimation (Adam) optimizer and learning rate of 0.001 outperformed all other corresponding hyperparameters. The findings from this study provide insights into the development of tools and gadgets useful in the automated detection of rust disease required for precision spraying.
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Affiliation(s)
- Fereshteh Shahoveisi
- Department of Plant Pathology, North Dakota State University, Fargo, ND, USA.
- Department of Plant Sciences and Landscape Architecture, University of Maryland, College Park, MD, USA.
| | - Hamed Taheri Gorji
- Biomedical Engineering Program, College of Engineering and Mine, University of North Dakota, Grand Forks, ND, USA
- SafetySpect Inc., 10100 Santa Monica Blvd., Suite 300, Los Angeles, CA, USA
| | - Seyedmojtaba Shahabi
- School of Electrical Engineering and Computer Science, College of Engineering and Mine, University of North Dakota, Grand Forks, ND, USA
| | - Seyedali Hosseinirad
- Department of Plant Sciences and Landscape Architecture, University of Maryland, College Park, MD, USA
- Department of Plant Sciences, North Dakota State University, Fargo, ND, USA
| | - Samuel Markell
- Department of Plant Pathology, North Dakota State University, Fargo, ND, USA
| | - Fartash Vasefi
- SafetySpect Inc., 10100 Santa Monica Blvd., Suite 300, Los Angeles, CA, USA
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Kaya Y, Gürsoy E. A novel multi-head CNN design to identify plant diseases using the fusion of RGB images. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2023.101998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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11
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Zhu D, Feng Q, Zhang J, Yang W. Cotton disease identification method based on pruning. FRONTIERS IN PLANT SCIENCE 2022; 13:1038791. [PMID: 36589068 PMCID: PMC9795023 DOI: 10.3389/fpls.2022.1038791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
Deep convolutional neural networks (DCNN) have shown promising performance in plant disease recognition. However, these networks cannot be deployed on resource-limited smart devices due to their vast parameters and computations. To address the issue of deployability when developing cotton disease identification applications for mobile/smart devices, we compress the disease recognition models employing the pruning algorithm. The algorithm uses the γ coefficient in the Batch Normalization layer to prune the channels to realize the compression of DCNN. To further improve the accuracy of the model, we suggest two strategies in combination with transfer learning: compression after transfer learning or transfer learning after compression. In our experiments, the source dataset is famous PlantVillage while the target dataset is the cotton disease image set which contains images collected from the Internet and taken from the fields. We select VGG16, ResNet164 and DenseNet40 as compressed models for comparison. The experimental results show that transfer learning after compression overall surpass its counterpart. When compression rate is set to 80% the accuracies of compressed version of VGG16, ResNet164 and DenseNet40 are 90.77%, 96.31% and 97.23%, respectively, and the parameters are only 0.30M, 0.43M and 0.26M, respectively. Among the compressed models, DenseNet40 has the highest accuracy and the smallest parameters. The best model (DenseNet40-80%-T) is pruned 75.70% of the parameters and cut off 65.52% of the computations, with the model size being only 2.2 MB. Compared with the version of compression after transfer learning, the accuracy of the model is improved by 0.74%. We further develop a cotton disease recognition APP on the Android platform based on the model and on the test phone, the average time to identify a single image is just 87ms.
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Affiliation(s)
- Dongqin Zhu
- School of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou, China
| | - Quan Feng
- School of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou, China
| | - Jianhua Zhang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China
- National Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya, China
| | - Wanxia Yang
- School of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou, China
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Saleem MH, Potgieter J, Arif KM. A weight optimization-based transfer learning approach for plant disease detection of New Zealand vegetables. FRONTIERS IN PLANT SCIENCE 2022; 13:1008079. [PMID: 36388538 PMCID: PMC9641257 DOI: 10.3389/fpls.2022.1008079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
Deep learning (DL) is an effective approach to identifying plant diseases. Among several DL-based techniques, transfer learning (TL) produces significant results in terms of improved accuracy. However, the usefulness of TL has not yet been explored using weights optimized from agricultural datasets. Furthermore, the detection of plant diseases in different organs of various vegetables has not yet been performed using a trained/optimized DL model. Moreover, the presence/detection of multiple diseases in vegetable organs has not yet been investigated. To address these research gaps, a new dataset named NZDLPlantDisease-v2 has been collected for New Zealand vegetables. The dataset includes 28 healthy and defective organs of beans, broccoli, cabbage, cauliflower, kumara, peas, potato, and tomato. This paper presents a transfer learning method that optimizes weights obtained through agricultural datasets for better outcomes in plant disease identification. First, several DL architectures are compared to obtain the best-suited model, and then, data augmentation techniques are applied. The Faster Region-based Convolutional Neural Network (RCNN) Inception ResNet-v2 attained the highest mean average precision (mAP) compared to the other DL models including different versions of Faster RCNN, Single-Shot Multibox Detector (SSD), Region-based Fully Convolutional Networks (RFCN), RetinaNet, and EfficientDet. Next, weight optimization is performed on datasets including PlantVillage, NZDLPlantDisease-v1, and DeepWeeds using image resizers, interpolators, initializers, batch normalization, and DL optimizers. Updated/optimized weights are then used to retrain the Faster RCNN Inception ResNet-v2 model on the proposed dataset. Finally, the results are compared with the model trained/optimized using a large dataset, such as Common Objects in Context (COCO). The final mAP improves by 9.25% and is found to be 91.33%. Moreover, the robustness of the methodology is demonstrated by testing the final model on an external dataset and using the stratified k-fold cross-validation method.
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Affiliation(s)
- Muhammad Hammad Saleem
- Department of Mechanical and Electrical Engineering, School of Food and Advanced Technology, Massey University, Auckland, New Zealand
| | - Johan Potgieter
- Massey AgriFood Digital Lab, Massey University, Palmerston North, New Zealand
| | - Khalid Mahmood Arif
- Department of Mechanical and Electrical Engineering, School of Food and Advanced Technology, Massey University, Auckland, New Zealand
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13
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Maize crop disease detection using NPNet-19 convolutional neural network. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07722-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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14
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Leaf species and disease classification using multiscale parallel deep CNN architecture. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07521-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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15
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Li C, Li M, Zhu X, Chen Y, Wu Y, Deng N, Fang K. Identification Method of Grape Leaf Diseases Based on Improved CCT Model. INT J PATTERN RECOGN 2022. [DOI: 10.1142/s0218001422500379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Lu Y, Du J, Liu P, Zhang Y, Hao Z. Image Classification and Recognition of Rice Diseases: A Hybrid DBN and Particle Swarm Optimization Algorithm. Front Bioeng Biotechnol 2022; 10:855667. [PMID: 35573246 PMCID: PMC9091375 DOI: 10.3389/fbioe.2022.855667] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Rice blast, rice sheath blight, and rice brown spot have become the most popular diseases in the cold areas of northern China. In order to further improve the accuracy and efficiency of rice disease diagnosis, a framework for automatic classification and recognition of rice diseases is proposed in this study. First, we constructed a training and testing data set including 1,500 images of rice blast, 1,500 images of rice sheath blight, and 1,500 images of rice brown spot, and 1,100 healthy images were collected from the rice experimental field. Second, the deep belief network (DBN) model is designed to include 15 hidden restricted Boltzmann machine layers and a support vector machine (SVM) optimized with switching particle swarm (SPSO). It is noted that the developed DBN and SPSO-SVM can simultaneously learn three proposed features including color, texture, and shape to recognize the disease type from the region of interest obtained by preprocessing the disease images. The proposed model leads to a hit rate of 91.37%, accuracy of 94.03%, and a false measurement rate of 8.63%, with the 10-fold cross-validation strategy. The value of the area under the receiver operating characteristic curve (AUC) is 0.97, whose accuracy is much higher than that of the conventional machine learning model. The simulation results show that the DBN and SPSO-SVM models can effectively extract the image features of rice diseases during recognition, and have good anti-interference and robustness.
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Affiliation(s)
- Yang Lu
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, China
- *Correspondence: Yang Lu,
| | - Jiaojiao Du
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, China
| | - Pengfei Liu
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, China
| | - Yong Zhang
- School of Physics and Electronic Engineering, Northeast Petroleum University, Daqing, China
| | - Zhiqiang Hao
- Key Laboratory for Metallurgical Equipment and Control of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
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17
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AlexNet Convolutional Neural Network for Disease Detection and Classification of Tomato Leaf. ELECTRONICS 2022. [DOI: 10.3390/electronics11060951] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
With limited retrieval of reserves and restricted capability in plant pathology, automation of processes becomes essential. All over the world, farmers are struggling to prevent various harm from bacteria or pathogens such as viruses, fungi, worms, protozoa, and insects. Deep learning is currently widely used across a wide range of applications, including desktop, web, and mobile. In this study, the authors attempt to implement the function of AlexNet modification architecture-based CNN on the Android platform to predict tomato diseases based on leaf image. A dataset with of 18,345 training data and 4,585 testing data was used to create the predictive model. The information is separated into ten labels for tomato leaf diseases, each with 64 × 64 RGB pixels. The best model using the Adam optimizer with a realizing rate of 0.0005, the number of epochs 75, batch size 128, and an uncompromising cross-entropy loss function, has a high model accuracy with an average of 98%, a strictness rate of 0.98, a recall value of 0.99, and an F1-count of 0.98 with a loss of 0.1331, so that the classification results are good and very precise.
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18
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Ghosh D, Chakraborty S, Kodamana H, Chakraborty S. Application of machine learning in understanding plant virus pathogenesis: trends and perspectives on emergence, diagnosis, host-virus interplay and management. Virol J 2022; 19:42. [PMID: 35264189 PMCID: PMC8905280 DOI: 10.1186/s12985-022-01767-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 02/27/2022] [Indexed: 12/24/2022] Open
Abstract
Background Inclusion of high throughput technologies in the field of biology has generated massive amounts of data in the recent years. Now, transforming these huge volumes of data into knowledge is the primary challenge in computational biology. The traditional methods of data analysis have failed to carry out the task. Hence, researchers are turning to machine learning based approaches for the analysis of high-dimensional big data. In machine learning, once a model is trained with a training dataset, it can be applied on a testing dataset which is independent. In current times, deep learning algorithms further promote the application of machine learning in several field of biology including plant virology. Main body Plant viruses have emerged as one of the principal global threats to food security due to their devastating impact on crops and vegetables. The emergence of new viral strains and species help viruses to evade the concurrent preventive methods. According to a survey conducted in 2014, plant viruses are anticipated to cause a global yield loss of more than thirty billion USD per year. In order to design effective, durable and broad-spectrum management protocols, it is very important to understand the mechanistic details of viral pathogenesis. The application of machine learning enables precise diagnosis of plant viral diseases at an early stage. Furthermore, the development of several machine learning-guided bioinformatics platforms has primed plant virologists to understand the host-virus interplay better. In addition, machine learning has tremendous potential in deciphering the pattern of plant virus evolution and emergence as well as in developing viable control options. Conclusions Considering a significant progress in the application of machine learning in understanding plant virology, this review highlights an introductory note on machine learning and comprehensively discusses the trends and prospects of machine learning in the diagnosis of viral diseases, understanding host-virus interplay and emergence of plant viruses.
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Affiliation(s)
- Dibyendu Ghosh
- Molecular Virology Laboratory, School of Life Sciences, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Srija Chakraborty
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India
| | - Hariprasad Kodamana
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India.,School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, 110016, India
| | - Supriya Chakraborty
- Molecular Virology Laboratory, School of Life Sciences, Jawaharlal Nehru University, New Delhi, 110067, India.
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19
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Enhanced Convolutional Neural Network Model for Cassava Leaf Disease Identification and Classification. MATHEMATICS 2022. [DOI: 10.3390/math10040580] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Cassava is a crucial food and nutrition security crop cultivated by small-scale farmers because it can survive in a brutal environment. It is a significant source of carbohydrates in African countries. Sometimes, Cassava crops can be infected by leaf diseases, affecting the overall production and reducing farmers’ income. The existing Cassava disease research encounters several challenges, such as poor detection rate, higher processing time, and poor accuracy. This research provides a comprehensive learning strategy for real-time Cassava leaf disease identification based on enhanced CNN models (ECNN). The existing Standard CNN model utilizes extensive data processing features, increasing the computational overhead. A depth-wise separable convolution layer is utilized to resolve CNN issues in the proposed ECNN model. This feature minimizes the feature count and computational overhead. The proposed ECNN model utilizes a distinct block processing feature to process the imbalanced images. To resolve the color segregation issue, the proposed ECNN model uses a Gamma correction feature. To decrease the variable selection process and increase the computational efficiency, the proposed ECNN model uses global average election polling with batch normalization. An experimental analysis is performed over an online Cassava image dataset containing 6256 images of Cassava leaves with five disease classes. The dataset classes are as follows: class 0: “Cassava Bacterial Blight (CBB)”; class 1: “Cassava Brown Streak Disease (CBSD)”; class 2: “Cassava Green Mottle (CGM)”; class 3: “Cassava Mosaic Disease (CMD)”; and class 4: “Healthy”. Various performance measuring parameters, i.e., precision, recall, measure, and accuracy, are calculated for existing Standard CNN and the proposed ECNN model. The proposed ECNN classifier significantly outperforms and achieves 99.3% accuracy for the balanced dataset. The test findings prove that applying a balanced database of images improves classification performance.
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20
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Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant. ELECTRONICS 2022. [DOI: 10.3390/electronics11010148] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%.
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21
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Tripathi A, Chourasia U, Dixit P, Chang V. Plant Disease Detection Using Sequential Convolutional Neural Network. INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2022. [DOI: 10.4018/ijdst.303672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The main warning in the area of food preservation and care is on topmost are crop diseases. It has been recognized speedily, but it is not as easy as in any area of the world because no required framework exists. Both the healthy and diseased plant leaves were gathered and collected under the condition and circumstances. For this purpose, a public set of information was used. It was 20,639 images of plants that were infected and healthy. In order to recognize three different crops and 12 diseases, a sequential convolutional neural network from Keras was trained and applied. The perfection and exactness was 98.18 % onset of information of the above trained mentioned model using CNN . It has also indicated the probability and possibility of this strategy and procedure. The over-fitting occurs and neutralizes by putting the dropout value to 0.25.
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Affiliation(s)
- Anshul Tripathi
- University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, India
| | - Uday Chourasia
- University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, India
| | - Priyanka Dixit
- University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, India
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22
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Alshahrani HM, Al-Wesabi FN, Al Duhayyim M, Nemri N, Kadry S, Alqaralleh BA. An automated deep learning based satellite imagery analysis for ecology management. ECOL INFORM 2021. [DOI: 10.1016/j.ecoinf.2021.101452] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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23
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Jung M, Song JS, Hong S, Kim S, Go S, Lim YP, Park J, Park SG, Kim YM. Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images. FRONTIERS IN PLANT SCIENCE 2021; 12:738685. [PMID: 34659305 PMCID: PMC8511822 DOI: 10.3389/fpls.2021.738685] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 08/31/2021] [Indexed: 05/23/2023]
Abstract
Efficient and accurate methods of analysis are needed for the huge amount of biological data that have accumulated in various research fields, including genomics, phenomics, and genetics. Artificial intelligence (AI)-based analysis is one promising method to manipulate biological data. To this end, various algorithms have been developed and applied in fields such as disease diagnosis, species classification, and object prediction. In the field of phenomics, classification of accessions and variants is important for basic science and industrial applications. To construct AI-based classification models, three types of phenotypic image data were generated from 156 Brassica rapa core collections, and classification analyses were carried out using four different convolutional neural network architectures. The results of lateral view data showed higher accuracy compared with top view data. Furthermore, the relatively low accuracy of ResNet50 architecture suggested that definition and estimation of similarity index of phenotypic data were required before the selection of deep learning architectures.
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Affiliation(s)
- Minah Jung
- Department of Functional Genomics, KRIBB School of Biological Science, Korea University of Science and Technology, Daejeon, South Korea
- Euclidsoft Co., Ltd, Daejeon, South Korea
| | | | - Seongmin Hong
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, South Korea
- Molecular Genetics and Genomics Laboratory, Department of Horticulture, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
| | - SunWoo Kim
- Department of Bio-AI Convergence, Chungnam National University, Daejeon, South Korea
| | - Sangjin Go
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, South Korea
- Department of Horticulture, Gyeongsang National University, Jinju, South Korea
| | - Yong Pyo Lim
- Molecular Genetics and Genomics Laboratory, Department of Horticulture, College of Agriculture and Life Science, Chungnam National University, Daejeon, South Korea
| | - Juhan Park
- Euclidsoft Co., Ltd, Daejeon, South Korea
| | - Sung Goo Park
- Department of Functional Genomics, KRIBB School of Biological Science, Korea University of Science and Technology, Daejeon, South Korea
- Disease Target Structure Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, South Korea
| | - Yong-Min Kim
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, South Korea
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