AI based rice leaf disease identification enhanced by Dynamic Mode Decomposition

K.M., Sudhesh; Sowmya, V.; P., Sainamole Kurian; Sikha, O. K.

doi:10.1016/j.engappai.2023.105836

Cited by 32 publications

(4 citation statements)

References 31 publications

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“…Subsequently, these extracted features are fed into a multi-layer perceptron (MLP) to make predictions. Sudhesh et al [12] presented an innovative approach for identifying rice leaf diseases using Dynamic Mode Decomposition (DMD) coupled with attention-driven preprocessing. They focused on four distinct categories of rice leaf diseases which comprises four sets of experiments, evaluated the effectiveness of ten pre-trained DCNN models.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid ViT-CNN Model Premeditated for Rice Leaf Disease Identification

P.,

2024

IJCMEM

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Rice, a global staple crop, plays a crucial role in feeding approximately half of the global population. Nevertheless, the persistent spread of diseases poses a significant threat to rice production. Therefore, accurately identifying rice diseases is of paramount practical importance. The proposed approach introduces an innovative hybrid architecture for image classification, harnessing the strengths of both Vision Transformers (ViT) and Convolutional Neural Networks (CNNs). This research investigates five primary diseases affecting rice crops: Blast, Brown Spot, Tungro, False smut, and Bacterial Sheath Blight. Approximately 8000 images of these specific rice leaf diseases were employed for training purposes in the study. What distinguishes this method is its unique integration of a CNN block within the transformer layers, deviating from the traditional ViT architecture. Vision Transformers (ViTs), recognized for their exceptional performance in image classification, excel in providing global insights through attention-based mechanisms. Nevertheless, their model complexity can obscure the decision-making process, and ambiguous attention maps can lead to erroneous correlations among image patches. The incorporation of CNNs in this approach serves to address these challenges by effectively capturing local patterns. This synergistic combination enhances the model's robustness to variations in input data, such as changes in scale, perspective, or context. With the utilization of the proposed hybrid ViT-CNN model architecture, the model achieves remarkable results, boasting 100 percent accuracy and top-5 accuracy, along with a precision of 93.84 percent. Through this hybrid model, we have obtained satisfactory outcomes, surpassing the performance of the latest transformer models in the realm of rice leaf disease identification.

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Section: Literature Reviewmentioning

confidence: 99%

A Hybrid ViT-CNN Model Premeditated for Rice Leaf Disease Identification

P.,

2024

IJCMEM

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“…Dynamic mode decomposition method has been explored as representation for saliency detection and machine learning tasks [2,8,40,41,42,58,59,61]. In this work, the use of DMD as a feature representation for covid images is explored.…”

Section: Feature Extraction Using Dmdmentioning

confidence: 99%

“…Each band is vectorized, and a snapshot matrix is created. It is then given as input to the DMD algorithm [58]. These snapshot matrices are then fed to the DMD algorithm to compute the eigen decomposition corresponding to the measured data.…”

mentioning

confidence: 99%

An Intelligent Computational Model with Dynamic Mode Decomposition and Attention Features for COVID-19 Detection from CT Scan Images

Babu,

Kumar,

Mohan

et al. 2023

Preprint

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Coronavirus (COVID-19), an air-borne disease, has affected the lifestyle of people all around the world. The World Health Organization (WHO) classified the disease as a pandemic due to its rapid spread of infection. Tracing patients infected with Coronavirus has become a steep uphill process supervened by the limited availability of tests based on reverse transcription-polymerase chain reaction (RT-PCR), which calls for efficient and highly responsive detection and diagnostic methods. Recently, methodologies based on image processing have been proposed by various researchers, especially using deep learning-based models. However, most models need millions of parameters to learn the complex input-output relationships and demand massive computational resources. This paper proposes the detection of COVID-19 from CT scan images using deep convolutional neural networks (CNN). A dynamic mode decomposition (DMD) based attention-driven image enhancement is proposed to extract localized enhanced features from CT scan images. Localized features can improve the model's performance by making inferences about the complete object. Pre-trained deep CNN models including VGGNet, ResNet50, and InceptionV3, are then transfer-learned on the DMD-enhanced CT scan images for COVID-19 detection. The paper proposes a custom shallow CNN architecture for detecting COVID-19 using DMD-enhanced CT scan images. The custom shallow CNN with significantly reduced learnable parameters improves the accuracy of the model and reduces the computational burden. The performance of the CNN architectures (custom shallow CNN and transfer learned deep CNNs) is evaluated using benchmark performance metrics, including accuracy, precision, recall, and F1 score. The experimental results demonstrate that the proposed shallow CNN network trained on DMD-enhanced images can better detect COVID-19 and outperform existing architectures in accuracy and computational complexity. The accuracy obtained for the proposed shallow CNN network trained on DMD-enhanced images is around 92.3%, with an F1-score of 0.918.

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“…Agricultural technology is felt to replace farmers' role in monitoring plant growth with Artificial Intelligence (AI). The existence of AI can change the paradigm of agriculture to become more modern with a touch of the latest technology [11].…”

Section: Introductionmentioning

confidence: 99%

Measuring Agricultural Area Using YOLO Object Detection and ArUco Markers

Masykur,

Adi,

Nurhayati

2024

ISI

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This paper discusses the use of drones in image acquisition of agricultural land to detect the presence of disease and calculate the area of infected agriculture. Calculation of the area of infected and healthy areas will be calculated by combining the You Only Look Once (Yolo) object detection algorithm version 4 with the ArUco Marker reference image. The image resulting from the detection from the Yolo v4 algorithm will be used as a reference to be referenced using a reference image in the form of an AruCo Marker to convert it to area units to determine the area of the infected area and calculate the ratio between the area of the infected area and the area of the healthy area. The coordinate points at each corner are used as the first stage in converting pixels into area units. Measuring the infected area is necessary to localize the infection so that it does not spread to healthy plant areas. Apart from that, to anticipate the spread of infection which could result in crop failure. Evaluation of the calculation of the area of the detection area with the actual area resulted in an accuracy of 97.05%.

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AI based rice leaf disease identification enhanced by Dynamic Mode Decomposition

Cited by 32 publications

References 31 publications

A Hybrid ViT-CNN Model Premeditated for Rice Leaf Disease Identification

A Hybrid ViT-CNN Model Premeditated for Rice Leaf Disease Identification

An Intelligent Computational Model with Dynamic Mode Decomposition and Attention Features for COVID-19 Detection from CT Scan Images

Measuring Agricultural Area Using YOLO Object Detection and ArUco Markers

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