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Gong X, Zhang S. An Analysis of Plant Diseases Identification Based on Deep Learning Methods. THE PLANT PATHOLOGY JOURNAL 2023; 39:319-334. [PMID: 37550979 PMCID: PMC10412967 DOI: 10.5423/ppj.oa.02.2023.0034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/25/2023] [Accepted: 06/12/2023] [Indexed: 08/09/2023]
Abstract
Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.
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Affiliation(s)
- Xulu Gong
- College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801,
China
- School of Software, Shanxi Agricultural University, Jinzhong 030801,
China
| | - Shujuan Zhang
- College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801,
China
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Rößle D, Prey L, Ramgraber L, Hanemann A, Cremers D, Noack PO, Schön T. Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0068. [PMID: 37456082 PMCID: PMC10348660 DOI: 10.34133/plantphenomics.0068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/19/2023] [Indexed: 07/18/2023]
Abstract
Fusarium head blight (FHB) is one of the most prevalent wheat diseases, causing substantial yield losses and health risks. Efficient phenotyping of FHB is crucial for accelerating resistance breeding, but currently used methods are time-consuming and expensive. The present article suggests a noninvasive classification model for FHB severity estimation using red-green-blue (RGB) images, without requiring extensive preprocessing. The model accepts images taken from consumer-grade, low-cost RGB cameras and classifies the FHB severity into 6 ordinal levels. In addition, we introduce a novel dataset consisting of around 3,000 images from 3 different years (2020, 2021, and 2022) and 2 FHB severity assessments per image from independent raters. We used a pretrained EfficientNet (size b0), redesigned as a regression model. The results demonstrate that the interrater reliability (Cohen's kappa, κ) is substantially lower than the achieved individual network-to-rater results, e.g., 0.68 and 0.76 for the data captured in 2020, respectively. The model shows a generalization effect when trained with data from multiple years and tested on data from an independent year. Thus, using the images from 2020 and 2021 for training and 2022 for testing, we improved the F1w score by 0.14, the accuracy by 0.11, κ by 0.12, and reduced the root mean squared error by 0.5 compared to the best network trained only on a single year's data. The proposed lightweight model and methods could be deployed on mobile devices to automatically and objectively assess FHB severity with images from low-cost RGB cameras. The source code and the dataset are available at https://github.com/cvims/FHB_classification.
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Affiliation(s)
- Dominik Rößle
- AImotion Bavaria, Technische Hochschule Ingolstadt, Ingolstadt, Germany
| | - Lukas Prey
- Hochschule Weihenstephan-Triesdorf, Weidenbach, Germany
| | | | - Anja Hanemann
- Saatzucht Josef Breun GmbH and Co. KG, Herzogenaurach, Germany
| | | | | | - Torsten Schön
- AImotion Bavaria, Technische Hochschule Ingolstadt, Ingolstadt, Germany
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Recent advances in plant disease severity assessment using convolutional neural networks. Sci Rep 2023; 13:2336. [PMID: 36759626 PMCID: PMC9911734 DOI: 10.1038/s41598-023-29230-7] [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: 07/28/2022] [Accepted: 01/31/2023] [Indexed: 02/11/2023] Open
Abstract
In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to effectively monitor and control the entire production process of plants, not only the type of disease, but also the severity of the disease must be clarified. In recent years, deep learning for plant disease species identification has been widely used. In particular, the application of convolutional neural network (CNN) to plant disease images has made breakthrough progress. However, there are relatively few studies on disease severity assessment. The group first traced the prevailing views of existing disease researchers to provide criteria for grading the severity of plant diseases. Then, depending on the network architecture, this study outlined 16 studies on CNN-based plant disease severity assessment in terms of classical CNN frameworks, improved CNN architectures and CNN-based segmentation networks, and provided a detailed comparative analysis of the advantages and disadvantages of each. Common methods for acquiring datasets and performance evaluation metrics for CNN models were investigated. Finally, this study discussed the major challenges faced by CNN-based plant disease severity assessment methods in practical applications, and provided feasible research ideas and possible solutions to address these challenges.
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Zhang C, Lane B, Fernández-Campos M, Cruz-Sancan A, Lee DY, Gongora-Canul C, Ross TJ, Da Silva CR, Telenko DEP, Goodwin SB, Scofield SR, Oh S, Jung J, Cruz CD. Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning. FRONTIERS IN PLANT SCIENCE 2023; 13:1077403. [PMID: 36756236 PMCID: PMC9900023 DOI: 10.3389/fpls.2022.1077403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 12/21/2022] [Indexed: 06/18/2023]
Abstract
INTRODUCTION Tar spot is a high-profile disease, causing various degrees of yield losses on corn (Zea mays L.) in several countries throughout the Americas. Disease symptoms usually appear at the lower canopy in corn fields with a history of tar spot infection, making it difficult to monitor the disease with unmanned aircraft systems (UAS) because of occlusion. METHODS UAS-based multispectral imaging and machine learning were used to monitor tar spot at different canopy and temporal levels and extract epidemiological parameters from multiple treatments. Disease severity was assessed visually at three canopy levels within micro-plots, while aerial images were gathered by UASs equipped with multispectral cameras. Both disease severity and multispectral images were collected from five to eleven time points each year for two years. Image-based features, such as single-band reflectance, vegetation indices (VIs), and their statistics, were extracted from ortho-mosaic images and used as inputs for machine learning to develop disease quantification models. RESULTS AND DISCUSSION The developed models showed encouraging performance in estimating disease severity at different canopy levels in both years (coefficient of determination up to 0.93 and Lin's concordance correlation coefficient up to 0.97). Epidemiological parameters, including initial disease severity or y0 and area under the disease progress curve, were modeled using data derived from multispectral imaging. In addition, results illustrated that digital phenotyping technologies could be used to monitor the onset of tar spot when disease severity is relatively low (< 1%) and evaluate the efficacy of disease management tactics under micro-plot conditions. Further studies are required to apply and validate our methods to large corn fields.
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Affiliation(s)
- Chongyuan Zhang
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | - Brenden Lane
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | | | - Andres Cruz-Sancan
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | - Da-Young Lee
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | - Carlos Gongora-Canul
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
- Tecnológico Nacional de México, Instituto Tecnológico de Conkal, Yucatán, Mexico
| | - Tiffanna J. Ross
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | - Camila R. Da Silva
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | - Darcy E. P. Telenko
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | - Stephen B. Goodwin
- USDA-Agricultural Research Service, Crop Production and Pest Control Research Unit, West Lafayette, IN, United States
| | - Steven R. Scofield
- USDA-Agricultural Research Service, Crop Production and Pest Control Research Unit, West Lafayette, IN, United States
| | - Sungchan Oh
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, United States
| | - Jinha Jung
- Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, United States
| | - C. D. Cruz
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
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Wang L, Jia K, Fu Y, Xu X, Fan L, Wang Q, Zhu W, Niu Q. Overlapped tobacco shred image segmentation and area computation using an improved Mask RCNN network and COT algorithm. FRONTIERS IN PLANT SCIENCE 2023; 14:1108560. [PMID: 37139110 PMCID: PMC10150031 DOI: 10.3389/fpls.2023.1108560] [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: 11/26/2022] [Accepted: 02/13/2023] [Indexed: 05/05/2023]
Abstract
Introduction The classification of the four tobacco shred varieties, tobacco silk, cut stem, expanded tobacco silk, and reconstituted tobacco shred, and the subsequent determination of tobacco shred components, are the primary tasks involved in calculating the tobacco shred blending ratio. The identification accuracy and subsequent component area calculation error directly affect the composition determination and quality of the tobacco shred. However, tiny tobacco shreds have complex physical and morphological characteristics; in particular, there is substantial similarity between the expanded tobacco silk and tobacco silk varieties, and this complicates their classification. There must be a certain amount of overlap and stacking in the distribution of tobacco shreds on the actual tobacco quality inspection line. There are 24 types of overlap alone, not to mention the stacking phenomenon. Self-winding does not make it easier to distinguish such varieties from the overlapped types, posing significant difficulties for machine vision-based tobacco shred classification and component area calculation tasks. Methods This study focuses on two significant challenges associated with identifying various types of overlapping tobacco shreds and acquiring overlapping regions to calculate overlapping areas. It develops a new segmentation model for tobacco shred images based on an improved Mask region-based convolutional neural network (RCNN). Mask RCNN is used as the segmentation network's mainframe. Convolutional network and feature pyramid network (FPN) in the backbone are replaced with Densenet121 and U-FPN, respectively. The size and aspect ratios of anchors parameters in region proposal network (RPN) are optimized. An algorithm for the area calculation of the overlapped tobacco shred region (COT) is also proposed, which is applied to overlapped tobacco shred mask images to obtain overlapped regions and calculate the overlapped area. Results The experimental results showed that the final segmentation accuracy and recall rates are 89.1% and 73.2%, respectively. The average area detection rate of 24 overlapped tobacco shred samples increases from 81.2% to 90%, achieving high segmentation accuracy and overlapped area calculation accuracy. Discussion This study provides a new implementation method for the type identification and component area calculation of overlapped tobacco shreds and a new approach for other similar overlapped image segmentation tasks.
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Affiliation(s)
- Li Wang
- School of Electrical Engineering, Henan University of Technology, Zhengzhou, ;China
| | - Kunming Jia
- School of Electrical Engineering, Henan University of Technology, Zhengzhou, ;China
| | - Yongmin Fu
- Xuchang Cigarette Factory, China Tobacco Henan Industry Co, Ltd, Xuchang, ;China
| | - Xiaoguang Xu
- Xuchang Cigarette Factory, China Tobacco Henan Industry Co, Ltd, Xuchang, ;China
| | - Lei Fan
- Xuchang Cigarette Factory, China Tobacco Henan Industry Co, Ltd, Xuchang, ;China
| | - Qiao Wang
- School of Electrical Engineering, Henan University of Technology, Zhengzhou, ;China
- Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education, Zhengzhou, ;China
| | - Wenkui Zhu
- Zhengzhou Tobacco Research Institute of China National Tobacco Company (CNTC), Zhengzhou, ;China
| | - Qunfeng Niu
- School of Electrical Engineering, Henan University of Technology, Zhengzhou, ;China
- *Correspondence: Qunfeng Niu,
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Niu Q, Liu J, Jin Y, Chen X, Zhu W, Yuan Q. Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision. FRONTIERS IN PLANT SCIENCE 2022; 13:962664. [PMID: 36061766 PMCID: PMC9433752 DOI: 10.3389/fpls.2022.962664] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 07/25/2022] [Indexed: 05/21/2023]
Abstract
The primary task in calculating the tobacco shred blending ratio is identifying the four tobacco shred types: expanded tobacco silk, cut stem, tobacco silk, and reconstituted tobacco shred. The classification precision directly affects the subsequent determination of tobacco shred components. However, the tobacco shred types, especially expanded tobacco silk and tobacco silk, have no apparent differences in macro-scale characteristics. The tobacco shreds have small size and irregular shape characteristics, creating significant challenges in their recognition and classification based on machine vision. This study provides a complete set of solutions aimed at this problem for screening tobacco shred samples, taking images, image preprocessing, establishing datasets, and identifying types. A block threshold binarization method is used for image preprocessing. Parameter setting and method performance are researched to obtain the maximum number of complete samples with acceptable execution time. ResNet50 is used as the primary classification and recognition network structure. By increasing the multi-scale structure and optimizing the number of blocks and loss function, a new tobacco shred image classification method is proposed based on the MS-X-ResNet (Multi-Scale-X-ResNet) network. Specifically, the MS-ResNet network is obtained by fusing the multi-scale Stage 3 low-dimensional and Stage 4 high-dimensional features to reduce the overfitting risk. The number of blocks in Stages 1-4 are adjusted from the original 3:4:6:3 to 3:4:N:3 (A-ResNet) and 3:3:N:3 (B-ResNet) to obtain the X-ResNet network, which improves the model's classification performance with lower complexity. The focal loss function is selected to reduce the impact of identification difficulty for different sample types on the network and improve its performance. The experimental results show that the final classification accuracy of the network on a tobacco shred dataset is 96.56%. The image recognition of a single tobacco shred requires 103 ms, achieving high classification accuracy and efficiency. The image preprocessing and deep learning algorithms for tobacco shred classification and identification proposed in this study provide a new implementation approach for the actual production and quality detection of tobacco and a new way for online real-time type identification of other agricultural products.
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Affiliation(s)
- Qunfeng Niu
- School of Electrical Engineering, Henan University of Technology, Zhengzhou, China
| | - Jiangpeng Liu
- School of Electrical Engineering, Henan University of Technology, Zhengzhou, China
| | - Yi Jin
- Anyang Cigarette Factory, China Tobacco Henan Industrial Co., Ltd., Anyang, China
| | - Xia Chen
- Anyang Cigarette Factory, China Tobacco Henan Industrial Co., Ltd., Anyang, China
| | - Wenkui Zhu
- Zhengzhou Tobacco Research Institute of China National Tobacco Corporation (CNTC), Zhengzhou, China
| | - Qiang Yuan
- School of Electrical Engineering, Henan University of Technology, Zhengzhou, China
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Cheng J, Xu Y, Zhao Y. Prediction of protein secondary structure based on deep residual convolutional neural network. BIOTECHNOL BIOTEC EQ 2022. [DOI: 10.1080/13102818.2022.2026815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Affiliation(s)
- Jinyong Cheng
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, PR China
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, PR China
| | - Ying Xu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, PR China
| | - Yunxiang Zhao
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, PR China
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Fernández-Campos M, Huang YT, Jahanshahi MR, Wang T, Jin J, Telenko DEP, Góngora-Canul C, Cruz CD. Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks. FRONTIERS IN PLANT SCIENCE 2021; 12:673505. [PMID: 34220894 PMCID: PMC8248543 DOI: 10.3389/fpls.2021.673505] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 05/10/2021] [Indexed: 05/21/2023]
Abstract
Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification. Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity. Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach to classify images in the three wheat blast severity categories. However, the models trained on non-matured and matured spikes images showing the highest precision, recall, and F1 score when classifying the images. The high classification accuracy could serve as a basis to facilitate wheat spike blast phenotyping in the future.
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Affiliation(s)
| | - Yu-Ting Huang
- Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, United States
| | - Mohammad R. Jahanshahi
- Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, United States
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States
| | - Tao Wang
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, United States
| | - Jian Jin
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, United States
| | - Darcy E. P. Telenko
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | - Carlos Góngora-Canul
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
- Tecnológico Nacional de México/IT Conkal, Conkal, Yucatán, Mexico
| | - C. D. Cruz
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
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