An Efficient Convolutional Neural Network for Paddy Leaf Disease and Pest Classification

Senan, Norhalina; Aamir, Muhammad; Ibrahim, Rosziati; Taujuddin, Nik Shahidah Afifi; Muda, W.H.N Wan

doi:10.14569/ijacsa.2020.0110716

Cited by 35 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…al., used a Kaggle dataset of 3355 paddy images to discriminate between healthy leaves and those damaged by brown spots, leaf blasts, and hispa. One convolutional layer, one pooling layer, and three fully connected layers were integrated to create a 5-layer convolutional neural network (CNN), displaying a well-organized architecture for efficient feature extraction and classification, reaching an astonishing 93% accuracy in detecting and categorizing varied paddy leaf states [18].…”

Section: Benefits Of the Paddy Disease Identification Modelmentioning

confidence: 99%

AI and Neural Network-Based Approach for Paddy Disease Identification and Classification

et al. 2024

Int. Res. J. multidiscip. Technovation

View full text Add to dashboard Cite

The purpose of this work is to use the artificial intelligence features of the ResNet50 architecture to provide a novel method of paddy disease identification. Farmers face numerous problems in raising paddy as its yield is affected by various factors like changing biodiversity, environment, weather pests, and disease. Traditional methods combined with smart farming, innovation, tools, and technology are needed for the mass production of food Here we develop a model using a convolutional neural network, ResNet50 that identifies disease in paddy leaf. The proposed model paddy disease identification model will give more precise results. The paddy disease identification model may be transformed into TensorFlow Lite (TFLite), which can be used for Android phones and drone applications, among other things. The Paddy model in this article obtained a training accuracy of almost 99% and a test accuracy of 92.83% when it was trained on 13,876 well-defined datasets. The loss function of 0.0014 at 100 epochs demonstrated that the model was effectively trained using ResNet50.

show abstract

Section: Benefits Of the Paddy Disease Identification Modelmentioning

confidence: 99%

AI and Neural Network-Based Approach for Paddy Disease Identification and Classification

et al. 2024

Int. Res. J. multidiscip. Technovation

View full text Add to dashboard Cite

show abstract

“…Also, unnecessary financing and wasting the resources should be reduced so as to have a risk-free production of rice. The advancement of computer vision applications in the field of agriculture provides better opportunity in enhancing the protection of rice crops (Senan et al, 2020). As rice serves as a staple food all over the world, the increase in population leads to greater demand for rice crops.…”

Section: Core Ideasmentioning

confidence: 99%

Detection of rice plant disease using AdaBoostSVM classifier

Kumar

Kannan

2022

Agronomy Journal

View full text Add to dashboard Cite

The research in the detection of plant diseases using plant images based on machine learning is widely increased in the field of agriculture. This could be done with the images of infected rice (Oryza sativa L.) plants. The changes in atmospheric condition cause changes in soil condition and in temperature. Both air temperature and soil temperature have distinct roles in crops, which can also lead to diseases in rice plants. In this paper, a prototype is developed for the detection of rice plant diseases like bacterial leaf blight, brown spot, and leaf smut. The proposed prototype is developed by undergoing experiments on image processing using machine learning algorithms. Several images of rice leaf which are infected by diseases had been captured and preprocessed using a median filtering technique. The important features are extracted by using Discrete Wavelet Transform (DWT) for the diseased part of the leaf images. Then the green parts of the leaf have been removed, so as to extract the diseased part. The features are extracted based on the attributes like color, shape, and texture. For multiclass classification process, Adaptive Boosting support vector machine (AdaBoostSVM) Classifier is used. The proposed prototype results in the accuracy of about 98.8% in detecting and classifying the rice leaf disease.

show abstract

“…Research [24] uses the XGBOOST method for disease detection in rice plants based on images with an accuracy of 93%. Research [25] uses the Convolutional Neural Network (CNN) method for disease detection in rice with as many as 3355 images with an accuracy of 93%. However, previous research has not resolved the problem of unbalanced data on rice diseases which affects the performance of the method.…”

Section: Introductionmentioning

confidence: 99%

Augmented Rice Plant Disease Detection with Convolutional Neural Networks

Hairani,

Widiyaningtyas

2024

intensif

View full text Add to dashboard Cite

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.

show abstract

An Efficient Convolutional Neural Network for Paddy Leaf Disease and Pest Classification

Cited by 35 publications

References 27 publications

AI and Neural Network-Based Approach for Paddy Disease Identification and Classification

AI and Neural Network-Based Approach for Paddy Disease Identification and Classification

Detection of rice plant disease using AdaBoostSVM classifier

Augmented Rice Plant Disease Detection with Convolutional Neural Networks

Contact Info

Product

Resources

About