Rice Leaf Disease Prediction Using Machine Learning

Bhartiya, Varun Pramod; Janghel, Rekh Ram; Rathore, Yogesh

doi:10.1109/icpc2t53885.2022.9776692

Cited by 12 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The classifier is able to classify the area, roundness, and lesion ratio. The results are good and the accuracy was estimated to be 81.6% [26].…”

Section: Support Vector Machines (Svm)mentioning

confidence: 91%

Detection of Rice Blast Disease (Magnaporthe grisea) Using Different Machine Learning Techniques

Chakraborty,

Banerjee,

Samanta

et al. 2023

IJECC

View full text Add to dashboard Cite

Rice is one of the most important staple food crops in the world. Most Asian countries are dependent on rice and huge quantities of rice are grown every year. However, there are many categories of diseases (e.g., blast) which affect rice production and can ultimately lead to huge financial loss to rice growers. Yield loss due to rice blast disease about 10 to 30 percent annually and under favourable condition, this disease can destroy the rice plant within 15 to 20 days and cause yield loss up to 100%.Therefore to ensure better quality, quantity and better productivity early disease detection should be done so that the right amount of pesticides can be as administered at right time to curb the infection. Nowadays Machine Learning has been integrated into the agriculture sector. The aim of this review paper is to identify which Machine Learning algorithms work best in rice blast disease detection. The algorithms reviewed here include Naive Bayes, LSTM RNN, Random Forest Classifiers, Support Vector Machines, K Means, Decision Tree and Convolutional Neural Networks. This review paper also covers the future scope of improvement of some Machine Learning algorithms like Naive Bayes and Recurrent Neural Networks.

show abstract

“…The classifier is able to classify the area, roundness, and lesion ratio. The results are good and the accuracy was estimated to be 81.6% [26].…”

Section: Support Vector Machines (Svm)mentioning

confidence: 91%

Detection of Rice Blast Disease (Magnaporthe grisea) Using Different Machine Learning Techniques

Chakraborty,

Banerjee,

Samanta

et al. 2023

IJECC

View full text Add to dashboard Cite

show abstract

“…In contrast to earlier techniques, Pallathadka et al [27] developed a comprehensive machine learning framework that made use of histograms for image processing, principal component analysis for extracting features and SVM, Naive Bayes, and CNN models for classification. Finally, Bhartiya et al [28] used a quadratic SVM classifier for extracting shape features and classified rice leaf disease with an accuracy of 81.8%. In Table 1 different algorithms that have been utilized for identifying various rice leaf diseases are discussed.…”

Section: Related Studymentioning

confidence: 99%

Lightweight Federated Learning for Rice Leaf Disease Classification Using Non Independent and Identically Distributed Images

et al. 2023

View full text Add to dashboard Cite

Rice (Oryza sativa L.) is a vital food source all over the world, contributing 15% of the protein and 21% of the energy intake per person in Asia, where most rice is produced and consumed. However, bacterial, fungal, and other microbial diseases that have a negative effect on the health of plants and crop yield are a major problem for rice farmers. It is challenging to diagnose these diseases manually, especially in areas with a shortage of crop protection experts. Automating disease identification and providing readily available decision-support tools are essential for enabling effective rice leaf protection measures and minimising rice crop losses. Although there are numerous classification systems for the diagnosis of rice leaf disease, no reliable, secure method has been identified that meets these needs. This paper proposes a lightweight federated deep learning architecture while maintaining data privacy constraints for rice leaf disease classification. The distributed client–server design of this framework protects the data privacy of all clients, and by using independent and identically distributed (IID) and non-IID data, the validity of the federated deep learning models was examined. To validate the framework’s efficacy, the researchers conducted experiments in a variety of settings, including conventional learning, federated learning via a single client, as well as federated learning via multiple clients. The study began by extracting features from various pre-trained models, ultimately selecting EfficientNetB3 with an impressive 99% accuracy as the baseline model. Subsequently, experimental results were conducted using the federated learning (FL) approach with both IID and non-IID datasets. The FL approach, along with a dense neural network trained and evaluated on an IID dataset, achieved outstanding training and evaluated accuracies of 99% with minimal losses of 0.006 and 0.03, respectively. Similarly, on a non-IID dataset, the FL approach maintained a high training accuracy of 99% with a loss of 0.04 and an evaluation accuracy of 95% with a loss of 0.08. These results indicate that the FL approach performs nearly as well as the base model, EfficientNetB3, highlighting its effectiveness in handling both IID and non-IID data. It was found that federated deep learning models with multiple clients outperformed conventional pre-trained models. The unique characteristics of the proposed framework, such as its data privacy for edge devices with limited resources, set it apart from the existing classification schemes for rice leaf diseases. The framework is the best alternative solution for the early classification of rice leaf disease because of these additional features.

show abstract

An efficient corn leaf disease prediction using Adaptive Color Edge Segmentation with Resnext101 model

Sachin Patil,

Kannan

2024

Journal of the Saudi Society of Agricultural Sciences

View full text Add to dashboard Cite

Rice Leaf Disease Prediction Using Machine Learning

Cited by 12 publications

References 19 publications

Detection of Rice Blast Disease (Magnaporthe grisea) Using Different Machine Learning Techniques

Detection of Rice Blast Disease (Magnaporthe grisea) Using Different Machine Learning Techniques

Lightweight Federated Learning for Rice Leaf Disease Classification Using Non Independent and Identically Distributed Images

An efficient corn leaf disease prediction using Adaptive Color Edge Segmentation with Resnext101 model

Contact Info

Product

Resources

About