Application of machine learning in detection of blast disease in South Indian rice crops

Ramesh, D

doi:10.25081/jp.2019.v11.5476

Cited by 28 publications

(8 citation statements)

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“…Tab. 4 represents the analysis of the comparative results of the DenseNet169-MLP model with recently proposed models [7][8][9] interms of different measures. Fig.…”

Section: Resultsmentioning

confidence: 99%

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Deep Transfer Learning Based Rice Plant Disease Detection Model

Narmadha¹,

Sengottaiyan²,

Kavitha³

2022

Intelligent Automation &Amp; Soft Computing

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In agriculture, plant diseases are mainly accountable for reduction in productivity and leads to huge economic loss. Rice is the essential food crop in Asian countries and it gets easily affected by different kinds of diseases. Because of the advent of computer vision and deep learning (DL) techniques, the rice plant diseases can be detected and reduce the burden of the farmers to save the crops. To achieve this, a new DL based rice plant disease diagnosis is developed using Densely Convolution Neural Network (DenseNet) with multilayer perceptron (MLP), called DenseNet169-MLP. The proposed model aims to classify the rice plant disease into three classes namely Bacterial Leaf Blight, Brown Spot, and Leaf Smut. Initially, preprocessing takes place in three levels namely channel separation, grayscale conversion, and noise removal using median filtering (MF). Then, the fuzzy c-means (FCM) based segmentation process identifies the diseased portion in the rice plant image. The pretrained DenseNet169 technique is used as a feature extractor and the final layer is replaced by the MLP to perform rice plant disease classification. The effectiveness of the proposed model has been validated against benchmark dataset and the simulation outcome is examined under diverse measures. The obtained results defined the superior results of the DenseNet169-MLP model over the recently presented methods with the maximum accuracy of 97.68%.

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“…Tab. 4 represents the analysis of the comparative results of the DenseNet169-MLP model with recently proposed models [7][8][9] interms of different measures. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The classification of diseases takes by the use of DNN-JOA. In [9], an ML-based rice plant disease diagnosis model to identify blast disease in South India has been presented. The presented model can identify blast diseases and minimize crop losses effectively.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Transfer Learning Based Rice Plant Disease Detection Model

Narmadha¹,

Sengottaiyan²,

Kavitha³

2022

Intelligent Automation &Amp; Soft Computing

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“…Akurasi adalah jumlah prediksi benar dari keseluruhan citra didalam dataset. Jika akurasi mendekati 1 dapat menjadi indikasi bahwa model memiliki performa yang bagus, dan 0 merepresentasikan sebaliknya [20]. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol.…”

Section: Hasil Dan Pembahasanunclassified

Klasifikasi Penyakit Padi berdasarkan Citra Daun Menggunakan Model Terlatih Resnet101

Oktaviana

Hendrawan

Annas

et al. 2021

RESTI

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Rice is a staple food source for most countries in the world, including Indonesia. The problem of rice disease is a problem that is quite crucial and is experienced by many farmers. Approximately 200,000 - 300,000 tons per year the amount of rice attacked by pests in Indonesia. Considerable losses are caused by late-diagnosed rice plant diseases that reach a severe stage and cause crop failure. The limited number of Agricultural Extension Officers (PPL) and the Lack of information about disease and proper treatment are some of the causes of delays in handling rice diseases. Therefore, with the development of information technology and computers, it is possible to identify diseases by utilizing Artificial Intelligence, one of which is by using recognition methods based on image processing and pattern recognition technology. The purpose of this research is to create a Machine Learning model by applying the model architecture from Resnet101 combined with the model architecture from the author. The model proposed in this study produces an accuracy of 98.68%.

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“…The prediction of disease resistant molecular markers in plants using a machine learning based approach can provide rapid insights into their identi cation and pathophysiology of plants [15,16]. There are several machine learning-based models developed to predict rice blast disease [17][18][19][20][21][22]. However, a rigorous approach of building machine learning models using the amino acid and dipeptide compositions from protein sequences of blast disease resistant versus susceptible genes that provides novel insights into the rice plant pathophysiology have not been researched till date based on our current awareness.…”

Section: Introductionmentioning

confidence: 99%

k-Nearest Neighbour machine method for predicting resistance gene against Magnaporthe oryzae in rice using proteomic markers

Villikudathil,

2024

Preprint

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Rice blast disease, caused by the fungal pathogen Magnaporthe oryzae, poses a severe threat to global rice cultivation, impacting over 3.5 billion people and the livelihoods of 200 million. Despite challenges in achieving sustainable resistance, our study focuses on identifying proteomic signatures in blast disease-resistant and susceptible genes using amino acid and dipeptide compositions. Leveraging machine learning, particularly a k-NN model, we identified 20 molecular markers distinguishing between resistant and susceptible genes with 90% accuracy. This research highlights the potential of protein sequence-based machine learning for predicting blast disease resistance, providing valuable insights for disease-resistant breeding programs and enhancing global food security through sustainable rice cultivation.

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Application of machine learning in detection of blast disease in South Indian rice crops

Cited by 28 publications

References 0 publications

Deep Transfer Learning Based Rice Plant Disease Detection Model

Deep Transfer Learning Based Rice Plant Disease Detection Model

Klasifikasi Penyakit Padi berdasarkan Citra Daun Menggunakan Model Terlatih Resnet101

k-Nearest Neighbour machine method for predicting resistance gene against Magnaporthe oryzae in rice using proteomic markers

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