Research on optimal predicting model for the grading detection of rice blast

Luo, Yi; Jiang, Ping; Xie, Kai; Wang, Fu-jie

doi:10.1007/s10043-018-0487-3

Cited by 12 publications

(5 citation statements)

References 2 publications

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“…An accurate assessment of the disease severity for all six cultivars at measurements over 30 days was achieved (Thomas et al, 2018). Luo et al (2019) applied HSI to grade the severity of rice blast, and the probabilistic neural network obtained the best performance with the highest classification accuracy of 97.8%. For MIR, Zhang et al (2017) explored and validated the feasibility of using MIR to detect oilseed rape leaves infected with Sclerotinia stem rot.…”

Section: Introductionmentioning

confidence: 99%

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Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods

Liu

Zhu

et al. 2020

Front. Plant Sci.

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Section: Introductionmentioning

confidence: 99%

“…Several groups of scientists are involved in disease detection using spectral features and modeling (Alberto, 2018;Luo et al, 2019;Gaoqiang et al, 2020). However, these studies did not detect different diseases simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods

Liu

Zhu

et al. 2020

Front. Plant Sci.

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“…At present, rice disease-related research is mainly focused either on disease severity estimation and evaluation after disease manifestation or on the identification of different disease types and spot classification [77,85,86]. The methods of crop disease identification and severity estimation by scholars have evolved from statistical methods, such as discriminant and regression analyses for crop disease monitoring with simple forms and clear mechanisms, to more extensive crop disease studies incorporating computers, mathematical models, image enhancement, and deep-learning models [23,35,48,87,88].…”

Section: The Methods For Rice Diseases and Pests Monitoringmentioning

confidence: 99%

Remote Sensing Monitoring of Rice Diseases and Pests from Different Data Sources: A Review

et al. 2023

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Rice is an important food crop in China, and diseases and pests are the main factors threatening its safety, ecology, and efficient production. The development of remote sensing technology provides an important means for non-destructive and rapid monitoring of diseases and pests that threaten rice crops. This paper aims to provide insights into current and future trends in remote sensing for rice crop monitoring. First, we expound the mechanism of remote sensing monitoring of rice diseases and pests and introduce the applications of different commonly data sources (hyperspectral data, multispectral data, thermal infrared data, fluorescence, and multi-source data fusion) in remote sensing monitoring of rice diseases and pests. Secondly, we summarize current methods for monitoring rice diseases and pests, including statistical discriminant type, machine learning, and deep learning algorithm. Finally, we provide a general framework to facilitate the monitoring of rice diseases or pests, which provides ideas and technical guidance for remote sensing monitoring of unknown diseases and pests, and we point out the challenges and future development directions of rice disease and pest remote sensing monitoring. This work provides new ideas and references for the subsequent monitoring of rice diseases and pests using remote sensing.

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“…The dataset contained 120 images of infected leaves. Luo et al. (2019) categorized four diverse paddy illnesses using a model combining CNN and support vector machine (SVM).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The dataset contained 120 images of infected leaves. Luo et al (2019) categorized four diverse paddy illnesses using a model combining CNN and support vector machine (SVM). Using a self-collected dataset of 6,637 images, the authors achieved 96.8% accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

Lamba,

Kukreja,

Rashid

et al. 2023

Front. Plant Sci.

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IntroductionPaddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production.MethodsIn this paper, infections in paddy leaves are considered for the identification of illness severity. The dataset contains both primary and secondary data. The four online repositories used for secondary data resources are Mendeley, GitHub, Kaggle and UCI. The size of the dataset is 4,068 images. The dataset is first pre-processed using ImageDataGenerator. Then, a generative adversarial network (GAN) is used to increase the dataset size exponentially. The disease severity calculation for the infected leaf is performed using a number of segmentation methods. To determine paddy infection, a deep learning-based hybrid approach is proposed that combines the capabilities of a convolutional neural network (CNN) and support vector machine (SVM). The severity levels are determined with the assistance of a domain expert. Four degrees of disease severity (mild, moderate, severe, and profound) are considered.ResultsThree infections are considered in the categorization of paddy leaf diseases: bacterial blight, blast, and leaf smut. The model predicted the paddy disease type and intensity with a 98.43% correctness rate. The loss rate is 41.25%.DiscussionThe findings show that the proposed method is reliable and effective for identifying the four levels of severity of bacterial blight, blast, and leaf smut infections in paddy crops. The proposed model performed better than the existing CNN and SVM classification models.

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Research on optimal predicting model for the grading detection of rice blast

Cited by 12 publications

References 2 publications

Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods

Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods

Remote Sensing Monitoring of Rice Diseases and Pests from Different Data Sources: A Review

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

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