Rice Disease Detection and Monitoring Using CNN and Naive Bayes Classification

Mohapatra, Debaniranjan; Tripathy, Jogeswar; Patra, Tapas Kumar

doi:10.1007/978-981-15-7394-1_2

Cited by 19 publications

(10 citation statements)

References 10 publications

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“…In a study by Mohapatra D. et al [16], CNN has been used to classify rice diseases, but the authors did not mention…”

Section: Related Workmentioning

confidence: 99%

Rice Leaf Disease Recognition using Local Threshold Based Segmentation and Deep CNN

Islam¹,

Islam²,

Haque³

et al. 2021

IJISA

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Timely detection of rice diseases can help farmers to take necessary action and thus reducing the yield loss substantially. Automatic recognition of rice diseases from the rice leaf images using computer vision and machine learning can be beneficial over the manual method of disease recognition through visual inspection. During the recent years, deep learning, a very popular and efficient machine learning algorithm, has shown great promise in image classification task. In this paper, a segmentation-based method using deep neural network for classifying rice diseases from leaf images has been proposed. Disease-affected regions of the rice leaves have been segmented using local segmentation method and the Convolutional Neural Network (CNN) has been trained with those images. Proposed method has been applied on three different datasets including the one created by us which consists of the rice leaf images collected from Bangladesh Rice Research Institute (BRRI). Three state-of-the-art CNN architectures VGG, ResNet and DenseNet, used in the proposed method, have been trained with these three datasets for classifying the diseases. Classification performance of the proposed method using the said three CNN architectures for the three datasets have been analyzed and compared. These results show that this model is quite promising in classifying rice leaf diseases. Outcome of this research is an enhancement in the performance of rice disease classification which is quite significant for the viability of this work to be transformed into a real-time application for the farmers.

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“…In a study by Mohapatra D. et al [16], CNN has been used to classify rice diseases, but the authors did not mention…”

Section: Related Workmentioning

confidence: 99%

Rice Leaf Disease Recognition using Local Threshold Based Segmentation and Deep CNN

Islam¹,

Islam²,

Haque³

et al. 2021

IJISA

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“…Agriculture is a major component of the global economy and food supply, which is under strain as a result of the enormous rise in population. Digital agriculture is a new scientific field that strengthens the agricultural production [1]. One of the many factors that influences agricultural yield is the absence of soil nutrients.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Sharma¹,

Nath

Sharma

et al. 2022

Electronics

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Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%.

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“…Several kinds of diseases may develop in Oryza Sativa plant which is complex to identify by farmers. The diseases like BS, BLF, and LS are the common pathogens that occur in paddy leaf [3]. Rapid plant lesion automated identification model identification is necessary to address this.…”

Section: Introductionmentioning

confidence: 99%

Infectious diseases of Rice plants classified using a deep learning-powered Least Squares Support Vector Machine Model

Goluguri¹,

Devi²,

CH³

2022

INDJCSE

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In the field of agriculture, identifying and classifying Oryza Sativa diseases is always a hot research topic. Almost in all the states of India, Oryza Sativa (rice) is cultivated as a necessary food crop. However, rice plants are highly caused due to diseases that drastically affect the agricultural sectors. Researchers are searching for solutions for reliable and exact detection approaches for crop leaf diseases. A Oryza Sativa plant leaf disease classification system that is modelled relying on the concept of deep learning-driven Least Square Support Vector Machine is investigated in the present study. This work focus on the Oryza Sativa plant leaf diseases like Brown Leaf spot(BS), Bacterial leaf blight (BLB), Leaf smut(LS), Tungro(LT), and Leaf Blast(LB). To create a contrast-enhanced image dataset, the input Oryza Sativa plant disease image dataset is resized as well as histogram equalization and the Kuwahara filter are applied. The diseased part is segmented using spatial fuzzy clustering (SFC). Then, a multi-model LeNet5 is used in extracting features, and the least square support vector machine(LSSVM) is employed to classify the Oryza Sativa plant leaf diseases images. Further, to improve the accuracy of the disease classification Weighted Sparrow Search Optimization (WSSO) is utilized. The proposed multi-model LSSVM-WSSO outperformed the existing models on the two benchmark datasets when evaluated on the terms of accuracy, precision, recall, and F1-measure values.

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Rice Disease Detection and Monitoring Using CNN and Naive Bayes Classification

Cited by 19 publications

References 10 publications

Rice Leaf Disease Recognition using Local Threshold Based Segmentation and Deep CNN

Rice Leaf Disease Recognition using Local Threshold Based Segmentation and Deep CNN

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Infectious diseases of Rice plants classified using a deep learning-powered Least Squares Support Vector Machine Model

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