A rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture

Yang, Ning; Yu, Junjie; Wang, Aiying; Zhang, Rongbiao; Xie, Liangliang; Shu, Fangyu; Kwabena, Oppong Paul

doi:10.1002/jsfa.10383

Cited by 13 publications

(6 citation statements)

References 26 publications

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“…Unlike deep learning-based spore detection algorithms, the main focus of traditional machine learning algorithms was to detect a single spore class, which had relatively lower detection difficulty [5,6]. Although the lowest detection rate among the three previous studies reached 0.940 [28][29][30], there were many problems, such as the models could not be applied to detect mixed spore images directly and had much lower detection efficiency (Table 4). Therefore, according to the results of this study, for the task of monitoring conidia inoculum of rice blast fungus in the field, the deep object detection algorithms are more applicable and effective.…”

Section: Performance Comparison With Previous Studiesmentioning

confidence: 99%

“…For instance, Yang et al [28] detected the spores of rice false smut and rice blast pathogens separately by using the decision tree and the confusion matrix based on features of texture and shape. Wang et al [29] extracted the HOG features based on the shape of rice blast spores, after which an intersection kernel support vector machine classifier was used to detect rice blast spores. Qi et al [30] obtained the binary image of the rice blast pathogen spores firstly, and then adopted the improved watershed algorithm to separate the adhesive spores and count them.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis

Zhou,

Lai,

Huang

et al. 2024

Agriculture

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The severity of rice blast and its impacts on rice yield are closely related to the inoculum quantity of Magnaporthe oryzae, and automatic detection of the pathogen spores in microscopic images can provide a rapid and effective way to quantify pathogen inoculum. Traditional spore detection methods mostly rely on manual feature extraction and shallow machine learning models, and are mostly designed for the indoor counting of a single spore class, which cannot handle the interference of impurity particles in the field. This study achieved automatic detection of rice blast fungus spores in the mixture with other fungal spores and rice pollens commonly encountered under field conditions by using deep learning based object detection techniques. First, 8959 microscopic images of a single spore class and 1450 microscopic images of mixed spore classes, including the rice blast fungus spores and four common impurity particles, were collected and labelled to form the benchmark dataset. Then, Faster R-CNN, Cascade R-CNN and YOLOv3 were used as the main detection frameworks, and multiple convolutional neural networks were used as the backbone networks in training of nine object detection algorithms. The results showed that the detection performance of YOLOv3_DarkNet53 is superior to the other eight algorithms, and achieved 98.0% mean average precision (intersection over union > 0.5) and an average speed of 36.4 frames per second. This study demonstrated the enormous application potential of deep object detection algorithms in automatic detection and quantification of rice blast fungus spores.

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Section: Performance Comparison With Previous Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis

Zhou,

Lai,

Huang

et al. 2024

Agriculture

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“…Then, the color, shape, and texture characteristics of spores were extracted, and the classification models of the spores were built based on logistic regression (LR), K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), respectively. Yang et al [ 21 ] in order to achieve early detection of rice blast proposed a method to detect and identify rice blast based on crop disease spores’ diffraction fingerprint texture, which has certain advantages compared with the existing method of manual identification by microscope. Although the above methods can achieve early detection of crop airborne diseases, they still face many problems.…”

Section: Introductionmentioning

confidence: 99%

A Rapid Detection Method for Tomato Gray Mold Spores in Greenhouse Based on Microfluidic Chip Enrichment and Lens-Less Diffraction Image Processing

Wang

Mao

Zhang

et al. 2021

Foods

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It is of great significance to find tomato gray mold in time and take corresponding control measures to ensure the production of tomato crops. This study proposed a rapid detection method for spores of Botrytis cinerea in green-house based on microfluidic chip enrichment and lens-free diffraction image processing. Microfluidic chip with a regular triangular inner rib structure was designed to achieve the enrichment of Botrytis cinerea spores. In order to obtain the diffraction image of the diseased spores, a lens-less diffraction imaging system was built. Furthermore, the collected spore diffraction images were processed and counted. The simulation results showed that the collection efficiency of 16 μm particles was 79%, 100%, and 89% at the inlet flow rate of 12, 14 and 16 mL/min, respectively. The experimental verification results were observed under a microscope. The results showed that when the flow rate of the microfluidic chip was 12, 14 and 16 mL/min, the collection efficiency of Botrytis cinerea spores was 70.65%, 87.52% and 77.96%, respectively. The Botrytis cinerea spores collected in the experiment were placed under a microscope for manual counting and compared with the automatic counting results based on diffraction image processing. A total of 10 sets of experiments were carried out, with an error range of the experiment was 5.13~8.57%, and the average error of the experiment was 6.42%. The Bland–Altman method was used to analyze two methods based on diffraction image processing and manual counting under a microscope. All points are within the 95% consistency interval. Therefore, this study can provide a basis for the research on the real-time monitoring technology of tomato gray mold spores in the greenhouse.

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“…The rice samples quality is analyzed using Visible Near InfraRed (VNIR) hyperspectral imaging technology in [6]. Firstly, they cleaned the image samples from the scatter effect phenomena by carrying out five methods: SG1, SG2, MSC, SNV, and PMSC.…”

Section: Introductionmentioning

confidence: 99%

Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques

et al. 2021

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A correct food tray sealing is required to preserve food properties and safety for consumers. Traditional food packaging inspections are made by human operators to detect seal defects. Recent advances in the field of food inspection have been related to the use of hyperspectral imaging technology and automated vision-based inspection systems. A deep learning-based approach for food tray sealing fault detection using hyperspectral images is described. Several pixel-based image fusion methods are proposed to obtain 2D images from the 3D hyperspectral image datacube, which feeds the deep learning (DL) algorithms. Instead of considering all spectral bands in region of interest around a contaminated or faulty seal area, only relevant bands are selected using data fusion. These techniques greatly improve the computation time while maintaining a high classification ratio, showing that the fused image contains enough information for checking a food tray sealing state (faulty or normal), avoiding feeding a large image datacube to the DL algorithms. Additionally, the proposed DL algorithms do not require any prior handcraft approach, i.e., no manual tuning of the parameters in the algorithms are required since the training process adjusts the algorithm. The experimental results, validated using an industrial dataset for food trays, along with different deep learning methods, demonstrate the effectiveness of the proposed approach. In the studied dataset, an accuracy of 88.7%, 88.3%, 89.3%, and 90.1% was achieved for Deep Belief Network (DBN), Extreme Learning Machine (ELM), Stacked Auto Encoder (SAE), and Convolutional Neural Network (CNN), respectively.

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A rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture

Cited by 13 publications

References 26 publications

Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis

Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis

A Rapid Detection Method for Tomato Gray Mold Spores in Greenhouse Based on Microfluidic Chip Enrichment and Lens-Less Diffraction Image Processing

Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques

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