Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks

Fernández-Campos, Mariela; Huang, Yu‐Ting; Jahanshahi, Mohammad R.; Wang, Tao; Jin, Jian Xun; Telenko, Darcy E. P.; Gongora-Canul, Carlos; Cruz, C. D.

doi:10.3389/fpls.2021.673505

Cited by 13 publications

(7 citation statements)

References 69 publications

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“…The imbalance problem can have a serious impact on the performance of the model, for example, the misclassification rate becomes higher which has been demonstrated in the experiments of many studies 45,46,71 . This problem can be well mitigated by data augmentation and weighted loss functions 81 . www.nature.com/scientificreports/ A server with supercomputing power is needed to ensure that the plant disease severity model built by CNN in the lab is widely used.…”

Section: Datasets and Evaluation Metricsmentioning

confidence: 99%

Recent advances in plant disease severity assessment using convolutional neural networks

Shi

Liu

Zheng

et al. 2023

Sci Rep

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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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Section: Datasets and Evaluation Metricsmentioning

confidence: 99%

Recent advances in plant disease severity assessment using convolutional neural networks

Shi

Liu

Zheng

et al. 2023

Sci Rep

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“…Vegetation indices (VIs), texture, thermal, and morphological features (e.g., canopy cover and volume and contour) are extracted from data for plant disease monitoring ( Zhang et al., 2019a ; Lee et al., 2021 ; Vishnoi et al., 2021 ). Machine learning algorithms are commonly applied to data collected or features extracted to automatically identify, classify, and quantify plant diseases ( Johannes et al., 2017 ; Wang et al., 2020 ; Fernández-Campos et al., 2021 ; Gao et al., 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning

et al. 2023

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IntroductionTar 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.MethodsUAS-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 discussionThe 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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“…In recent years, machine vision-based deep learning methods have provided advanced and efficient image processing solutions in agriculture. Deep learning methods, combined with machine vision technology, have been widely used in plant disease and pest classification, including the classification of fresh tobacco leaves of various maturity levels ( Chen et al., 2021 ); the classification of tobacco plant diseases ( Lin et al., 2022 ); the classification of wheat spike blast ( Fernández-Campos et al., 2021 ); the classification of rice pests and diseases ( Yang et al., 2021 ); the detection of plant parts such as tobacco leaves and stems ( Li et al., 2021 ); the detection of tomato diseases ( Liu et al., 2022 ); the detection of wheat head diseases ( Gong et al., 2020 ); the detection of brown planthoppers in rice ( He et al., 2020 ); plant image segmentation, such as tobacco planting areas segmentation ( Huang et al., 2021 ); field-grown wheat spikes segmentation ( Tan et al., 2020 ); rice ear segmentation ( Bai-yi et al., 2020 ; Shao et al., 2021 ); rice lodging segmentation ( Su et al., 2022 ); photosynthetic and non-photosynthetic vegetation segmentation ( He et al., 2022 ); weed and crop segmentation ( Hashemi-Beni et al., 2022 ); and wheat spike segmentation ( Wen et al., 2022 ). Deep learning methods combined with machine vision technology have been utilized in research focused on the classification of tobacco shred images.…”

Section: Introductionmentioning

confidence: 99%

Overlapped tobacco shred image segmentation and area computation using an improved Mask RCNN network and COT algorithm

Wang

Jia

et al. 2023

Front. Plant Sci.

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IntroductionThe 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.MethodsThis 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.ResultsThe 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.DiscussionThis 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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Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks

Cited by 13 publications

References 69 publications

Recent advances in plant disease severity assessment using convolutional neural networks

Recent advances in plant disease severity assessment using convolutional neural networks

Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning

Overlapped tobacco shred image segmentation and area computation using an improved Mask RCNN network and COT algorithm

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