Application of machine learning techniques in rice leaf disease detection

Pallathadka, Harikumar; Ravipati, Pavankumar; Sajja, Guna Sekhar; Phasinam, Khongdet; Kassanuk, Thanwamas; Sanchez, Domenic T.; Prabhu, Puniethaa

doi:10.1016/j.matpr.2021.11.398

Cited by 36 publications

(13 citation statements)

References 17 publications

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“…This approach had been successfully employed in classifying various apple leaf conditions, showcasing its potential in disease detection. Furthermore, Pallathadka et al developed an integrated framework that combined Support Vector Machines (SVM), Naive Bayes, and CNN to automatically classify and assessed the severity of crop leaf diseases by using hyper-spectral images [31] . This comprehensive approach demonstrated promising results in disease identification.…”

Section: Related Workmentioning

confidence: 99%

A multi-scale feature fusion neural network for multi-class disease classification on the maize leaf images

Liu,

Qiao,

Chang

et al. 2024

Heliyon

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Section: Related Workmentioning

confidence: 99%

A multi-scale feature fusion neural network for multi-class disease classification on the maize leaf images

Liu,

Qiao,

Chang

et al. 2024

Heliyon

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“…However, the method of manual discrimination has high work intensity and a considerable risk of misjudgment (Dutot et al, 2013;Lin et al, 2022). With the development of computer technology, machine learning-based methods have been widely applied to recognize disease leaves (Guru et al, 2011;Majumdar et al, 2015;Xie et al, 2016;Pantazi et al, 2019;Hamdani et al, 2021;Ngugi et al, 2021;Pallathadka et al, 2022). For example, Pallathadka et al (2022) performed histogram equalization on the image, applied the principal component analysis algorithm to extract features, and then utilized the support vector machine to classify leaf diseases.…”

Section: Introductionmentioning

confidence: 99%

“…With the development of computer technology, machine learning-based methods have been widely applied to recognize disease leaves (Guru et al, 2011;Majumdar et al, 2015;Xie et al, 2016;Pantazi et al, 2019;Hamdani et al, 2021;Ngugi et al, 2021;Pallathadka et al, 2022). For example, Pallathadka et al (2022) performed histogram equalization on the image, applied the principal component analysis algorithm to extract features, and then utilized the support vector machine to classify leaf diseases. Majumdar et al (2015) extracted the characteristics of wheat diseases by Fuzzy C-Means and then identified disease spots employing the artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

EADD-YOLO: An efficient and accurate disease detector for apple leaf using improved lightweight YOLOv5

Zhu

Ma²,

Wang³

et al. 2023

Front. Plant Sci.

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IntroductionCurrent detection methods for apple leaf diseases still suffer some challenges, such as the high number of parameters, low detection speed and poor detection performance for small dense spots, which limit the practical applications in agriculture. Therefore, an efficient and accurate model for apple leaf disease detection based on YOLOv5 is proposed and named EADD-YOLO.MethodsIn the EADD-YOLO, the lightweight shufflenet inverted residual module is utilized to reconstruct the backbone network, and an efficient feature learning module designed through depthwise convolution is proposed and introduced to the neck network. The aim is to reduce the number of parameters and floating point of operations (FLOPs) during feature extraction and feature fusion, thus increasing the operational efficiency of the network with less impact on detection performance. In addition, the coordinate attention module is embedded into the critical locations of the network to select the critical spot information and suppress useless information, which is to enhance the detection accuracy of diseases with various sizes from different scenes. Furthermore, the SIoU loss replaces CIoU loss as the bounding box regression loss function to improve the accuracy of prediction box localization.ResultsThe experimental results indicate that the proposed method can achieve the detection performance of 95.5% on the mean average precision and a speed of 625 frames per second (FPS) on the apple leaf disease dataset (ALDD). Compared to the latest research method on the ALDD, the detection accuracy and speed of the proposed method were improved by 12.3% and 596 FPS, respectively. In addition, the parameter quantity and FLOPs of the proposed method were much less than other relevant popular algorithms.DiscussionIn summary, the proposed method not only has a satisfactory detection effect, but also has fewer parameters and high calculation efficiency compared with the existing approaches. Therefore, the proposed method provides a high-performance solution for the early diagnosis of apple leaf disease and can be applied in agricultural robots. The code repository is open-sourced at https://github.com/AWANWY/EADD-YOLO.

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“…It takes more time to traverse all the disease images, resulting in poor detection performance. In addition, the feature extraction of region proposal uses manual methods such as grey-scale co-occurrence matrix [4], textural descriptors [5] and local binary patterns [6], and the extracted features are more focused on the underlying features such as disease colour and shape, resulting in poor robustness of disease detection; the classifier uses support vector machines [7], Bayesian classifiers [8], unsupervised clustering [9] and other machine learning algorithms for disease recognition, with slow recognition speed and low accuracy rate.…”

Section: Introductionmentioning

confidence: 99%

A Copy Paste and Semantic Segmentation-Based Approach for the Classification and Assessment of Significant Rice Diseases

Chen

Shuai

et al. 2022

Plants

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The accurate segmentation of significant rice diseases and assessment of the degree of disease damage are the keys to their early diagnosis and intelligent monitoring and are the core of accurate pest control and information management. Deep learning applied to rice disease detection and segmentation can significantly improve the accuracy of disease detection and identification but requires a large number of training samples to determine the optimal parameters of the model. This study proposed a lightweight network based on copy paste and semantic segmentation for accurate disease region segmentation and severity assessment. First, a dataset for rice significant disease segmentation was selected and collated based on 3 open-source datasets, containing 450 sample images belonging to 3 categories of rice leaf bacterial blight, blast and brown spot. Then, to increase the diversity of samples, a data augmentation method, rice leaf disease copy paste (RLDCP), was proposed that expanded the collected disease samples with the concept of copy and paste. The new RSegformer model was then trained by replacing the new backbone network with the lightweight semantic segmentation network Segformer, combining the attention mechanism and changing the upsampling operator, so that the model could better balance local and global information, speed up the training process and reduce the degree of overfitting of the network. The results show that RLDCP could effectively improve the accuracy and generalisation performance of the semantic segmentation model compared with traditional data augmentation methods and could improve the MIoU of the semantic segmentation model by about 5% with a dataset only twice the size. RSegformer can achieve an 85.38% MIoU at a model size of 14.36 M. The method proposed in this paper can quickly, easily and accurately identify disease occurrence areas, their species and the degree of disease damage, providing a reference for timely and effective rice disease control.

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Application of machine learning techniques in rice leaf disease detection

Cited by 36 publications

References 17 publications

A multi-scale feature fusion neural network for multi-class disease classification on the maize leaf images

A multi-scale feature fusion neural network for multi-class disease classification on the maize leaf images

EADD-YOLO: An efficient and accurate disease detector for apple leaf using improved lightweight YOLOv5

A Copy Paste and Semantic Segmentation-Based Approach for the Classification and Assessment of Significant Rice Diseases

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