An Accurate Classification of Rice Diseases Based on ICAI-V4

Zeng, Nan; Gong, Gufeng; Zhou, Guoxiong; Hu, Chen

doi:10.3390/plants12112225

Cited by 19 publications

(3 citation statements)

References 43 publications

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“…(2) Leaky ReLU Activation Function: In deep learning, ReLU is a normally applied activation function [26]. The mathematical expression of the ReLU is represented as follows:…”

Section: Feature Extraction and Classification Phasementioning

confidence: 99%

Leaky ReLU-ResNet for Plant Leaf Disease Detection: A Deep Learning Approach

Padshetty,

Ambika

2023

RAiSE-2023

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“…(2) Leaky ReLU Activation Function: In deep learning, ReLU is a normally applied activation function [26]. The mathematical expression of the ReLU is represented as follows:…”

Section: Feature Extraction and Classification Phasementioning

confidence: 99%

Leaky ReLU-ResNet for Plant Leaf Disease Detection: A Deep Learning Approach

Padshetty,

Ambika

2023

RAiSE-2023

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“…Therefore, it is necessary to develop a system to recognize and classify rice plant diseases to help Indonesian rice farmers [6]. The recognition and classification of rice plant diseases require an accurate system to generate classification data [7]. Types of rice diseases can be identified in several ways, one of which is leaf characteristics [8].…”

Section: Introductionmentioning

confidence: 99%

Augmented Rice Plant Disease Detection with Convolutional Neural Networks

Hairani,

Widiyaningtyas

2024

intensif

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The recognition and classification of rice plant diseases require an accurate system to generate classification data. Types of rice diseases can be identified in several ways, one of which is leaf characterization. One method that has high accuracy in identifying plant disease types is Convolutional Neural Networks (CNN). However, the rice disease data used has unbalanced data which affects the performance of the method. Therefore, the purpose of this research was to apply data augmentation to handle unbalanced rice disease data to improve the performance of the Convolutional Neural Network (CNN) method for rice disease type detection based on leaf images. The method used in this research is the CNN method for detecting rice disease types based on leaf images. The result of this research was the CNN method with 100 epochs able to produce an accuracy of 99.7% in detecting rice diseases based on leaf images with a division of 80% training data (2438 data) and 20% testing data (608 data). The conclusion is that the CNN method with the augmentation process can be used in rice disease detection because it has very high accuracy.

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“…Though manual monitoring can indicate disease, it is tedious and erroneous for large fields [4]. In order to initiate disease treatment, individuals are required to bring the disease sample to specialists and await further instructions which is time-consuming and costly in agricultural field management [5,6].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Rice Crop Management: Disease Classification Using Convolutional Neural Networks and Mobile Application Integration

Hasan¹,

Rahman

Uddin³

et al. 2023

Agriculture

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Early diagnosis of rice disease is important because it poses a considerable threat to agricultural productivity as well as the global food security of the world. It is challenging to obtain more reliable outcomes based on the percentage of RGB value using image processing outcomes for rice disease detections and classifications in the agricultural field. Machine learning, especially with a Convolutional Neural Network (CNN), is a great tool to overcome this problem. But the utilization of deep learning techniques often necessitates high-performance computing devices, costly GPUs and extensive machine infrastructure. As a result, this significantly raises the overall expenses for users. Therefore, the demand for smaller CNN models becomes particularly pronounced, especially in embedded systems, robotics and mobile applications. These domains require real-time performance and minimal computational overhead, making smaller CNN models highly desirable due to their lower computational cost. This paper introduces a novel CNN architecture which is comparatively small in size and promising in performance to predict rice leaf disease with moderate accuracy and lower time complexity. The CNN network is trained with processed images. The image processing is performed using segmentation and k-means clustering to remove background and green parts of affected images. This technique proposes to detect rice disease of rice brown spot, rice bacterial blight and leaf smut with reliable outcomes in disease classifications. The model is trained using an augmented dataset of 2700 images (60% data) and validated with 1200 images of disease-affected samples to identify rice disease in agricultural fields. The model is tested with 630 images (14% data); testing accuracy is 97.9%. The model is exported into a mobile application to introduce the real-life application of the outcome of this work. The model accuracy is compared to others work associated with this type of problem. It is found that the performance of the model and the application are satisfactory compared to other works related to this work. The over-all accuracy is notable, showing the reliability and dependability of this model to classify rice leaf diseases.

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An Accurate Classification of Rice Diseases Based on ICAI-V4

Cited by 19 publications

References 43 publications

Leaky ReLU-ResNet for Plant Leaf Disease Detection: A Deep Learning Approach

Leaky ReLU-ResNet for Plant Leaf Disease Detection: A Deep Learning Approach

Augmented Rice Plant Disease Detection with Convolutional Neural Networks

Enhancing Rice Crop Management: Disease Classification Using Convolutional Neural Networks and Mobile Application Integration

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