Research on automatic identification system of tobacco diseases

Shao, Yuanyuan; Xuan, Guantao; Zhu, Yangyan; Zhang, Yanling; Peng, Hongxing; Liu, Zhongzheng; Hou, Jialin

doi:10.1080/13682199.2017.1319609

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…Our results when compared to the total nine functions/formula, specifically formula number 3 ( Table S1 ) reveal the highest classification accuracy used in the algorithm analysis and using this function it was revealed that the Daechan and Cheongja 3-ho varieties were classified with high accuracies of 97.19% and 95.69% at 2 and 3 DAI, respectively. Shao et al [ 61 ] used back propagation and genetic algorithm optimized back propagation neural network algorithm to determine bacterial wildfire in tobacco with classification accuracies of 78.00% and 94.00%, respectively. Similarly, Gui et al (2023) successfully demonstrated pest detection with 94.75% ± 0.19% accuracy in soybean plants based on HSI and the A-ResNet meta-learning model.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging

Lay

Lee

Tayade

et al. 2023

Plants

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Plant diseases that affect crop production and productivity harm both crop quality and quantity. To minimize loss due to disease, early detection is a prerequisite. Recently, different technologies have been developed for plant disease detection. Hyperspectral imaging (HSI) is a nondestructive method for the early detection of crop disease and is based on the spatial and spectral information of images. Regarding plant disease detection, HSI can predict disease-induced biochemical and physical changes in plants. Bacterial infections, such as Pseudomonas syringae pv. tabaci, are among the most common plant diseases in areas of soybean cultivation, and have been implicated in considerably reducing soybean yield. Thus, in this study, we used a new method based on HSI analysis for the early detection of this disease. We performed the leaf spectral reflectance of soybean with the effect of infected bacterial wildfire during the early growth stage. This study aimed to classify the accuracy of the early detection of bacterial wildfire in soybean leaves. Two varieties of soybean were used for the experiment, Cheongja 3-ho and Daechan, as control (noninoculated) and treatment (bacterial wildfire), respectively. Bacterial inoculation was performed 18 days after planting, and the imagery data were collected 24 h following bacterial inoculation. The leaf reflectance signature revealed a significant difference between the diseased and healthy leaves in the green and near-infrared regions. The two-way analysis of variance analysis results obtained using the Python package algorithm revealed that the disease incidence of the two soybean varieties, Daechan and Cheongja 3-ho, could be classified on the second and third day following inoculation, with accuracy values of 97.19% and 95.69%, respectively, thus proving his to be a useful technique for the early detection of the disease. Therefore, creating a wide range of research platforms for the early detection of various diseases using a nondestructive method such HSI is feasible.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging

Lay

Lee

Tayade

et al. 2023

Plants

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“…Shao et al [46] proposed a technique to identify and detect various tobacco diseases; their methodology comprises of three stages, firstly, Otsu method was used to obtain disease location information, and the GrabCut function was initialised for extracting diseased area efficiently. Further, colour moments, disease contour and grey‐level co‐occurrence matrix (GLCM) were used to get colour, multi‐contour and texture features.…”

Section: Categorical Classification Of Algorithmic Techniquesmentioning

confidence: 99%

Review of image processing approaches for detecting plant diseases

Sinha

Shekhawat

2020

IET Image Processing

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“…In recent years, deep learning has provided advanced and efficient solutions for image processing tasks, such as image classification ( Atila et al, 2021 ), image segmentation ( Kang et al, 2020 ), and object detection ( Janai et al, 2020 ). Its excellent feature extraction ability greatly reduces the workload of image processing tasks ( Shao et al, 2017 ; Xiao Q. et al, 2019 ; Afonso et al, 2020 ). In view of the differences in the application objects, researchers mostly adjust the network structure according to the practical problems ( Buiu et al, 2020 ; Liu et al, 2021 ; Gu et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision

et al. 2022

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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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Research on automatic identification system of tobacco diseases

Cited by 8 publications

References 4 publications

Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging

Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging

Review of image processing approaches for detecting plant diseases

Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision

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