A novel framework for image-based plant disease detection using hybrid deep learning approach

Chug, Anuradha; Bhatia, Anshul; Singh, Amit Prakash; Singh, Dinesh

doi:10.1007/s00500-022-07177-7

Cited by 56 publications

(18 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed model contains 40 different hybrid deep learning models that incorporate eight distinct pre-trained deep learning architecture variants, namely EfficientNet (B0-B7) as feature extractors and five machine learning techniques, namely k-nearest neighbors (kNNs), AdaBoost, RF, LR, and stochastic gradient boosting (SGB) as classifiers. The current study optimized these classifiers’ hyperparameters using the Optuna framework 5 …”

Section: Literature Reviewmentioning

confidence: 99%

“…The current study optimized these classifiers' hyperparameters using the Optuna framework. 5 An assessment of traditional machine learning [classification and regression tree (CART), SVM, NB, RF, LDA, and KNN] and deep transfer learning [Visual Geometry Group 16 (VGG16), VGG19, InceptionV3, Residual neural networks50 (ResNet50), and convolutional neural network (CNN)] models to find performance evaluation on a dataset from the Plant Village Dataset that includes infected and good crop leaves for the classification of binary data. 6 The prior research, an overview of several disease detection works, is discussed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ELDA: enhanced linear discriminant analysis for cashew crop disease detection using precision agriculture

Palaniappan,

Pazhamalai

2024

J. Electron. Imag.

View full text Add to dashboard Cite

This work provides a method for reducing dimensionality using a median-based discriminatory analysis, which considers the adverse impact on the class mean caused by outliers, referred to as an enhanced linear discriminant analysis (ELDA). ELDA is based on a point-to-median distance, used as a measure metric to describe the within-class and between-class median scatter. The standard LDA algorithm may not be effective when the class distribution is uneven, and the limited sample size for inequality datasets and outliers persists. To address all issues, the proposed ELDA method is applied for cashew leaf disease identification on the cashew crop disease database (CCDDB), and it is more accurate at identifying and classifying cashew leaf illnesses in different sets of training and testing samples on three datasets. Research exhibits greater accuracy on CCDDB with 97.7%, on image database of plant disease symptoms dataset with 99.8%, and on dataset for crop pest and disease detection with 81.7%.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ELDA: enhanced linear discriminant analysis for cashew crop disease detection using precision agriculture

Palaniappan,

Pazhamalai

2024

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

“…The PCCDL-PSCT approach proposed by the authors performed best, obtaining an impressive accuracy of 98.14% with a smaller model size of about 10 MB. ( Chug et al., 2023 ) presented an innovative framework that combines the strengths of both machine learning and deep learning. The proposed framework comprises 40 diverse Hybrid Deep Learning (HDL) models.…”

Section: Literature Reviewmentioning

confidence: 99%

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

Uddin,

Mazumder,

Prity

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

Cauliflower cultivation plays a pivotal role in the Indian Subcontinent’s winter cropping landscape, contributing significantly to both agricultural output, economy and public health. However, the susceptibility of cauliflower crops to various diseases poses a threat to productivity and quality. This paper presents a novel machine vision approach employing a modified YOLOv8 model called Cauli-Det for automatic classification and localization of cauliflower diseases. The proposed system utilizes images captured through smartphones and hand-held devices, employing a finetuned pre-trained YOLOv8 architecture for disease-affected region detection and extracting spatial features for disease localization and classification. Three common cauliflower diseases, namely ‘Bacterial Soft Rot’, ‘Downey Mildew’ and ‘Black Rot’ are identified in a dataset of 656 images. Evaluation of different modification and training methods reveals the proposed custom YOLOv8 model achieves a precision, recall and mean average precision (mAP) of 93.2%, 82.6% and 91.1% on the test dataset respectively, showcasing the potential of this technology to empower cauliflower farmers with a timely and efficient tool for disease management, thereby enhancing overall agricultural productivity and sustainability

show abstract

“…Singh, A.P. et al [52] employed kNN in deep learning architectures for image classification which shows a higher classification accuracy. In [53], a label driven latent subspace learning for multi-view image classification model was developed which produces an improved classification result.…”

Section: Literature Surveymentioning

confidence: 99%

Map-Reduce based Distance Weighted k-Nearest Neighbor Machine Learning Algorithm for Big Data Applications

Gothai¹,

Muthukumaran

Valarmathi³

et al. 2022

SCPE

View full text Add to dashboard Cite

With the evolution of Internet standards and advancements in various Internet and mobile technologies, especially since web 4.0, more and more web and mobile applications emerge such as e-commerce, social networks, online gaming applications and Internet of Things based applications. Due to the deployment and concurrent access of these applications on the Internet and mobile devices, the amount of data and the kind of data generated increases exponentially and the new era of Big Data has come into existence. Presently available data structures and data analyzing algorithms are not capable to handle such Big Data. Hence, there is a need for scalable, flexible, parallel and intelligent data analyzing algorithms to handle and analyze the complex massive data. In this article, we have proposed a novel distributed supervised machine learning algorithm based on the MapReduce programming model and Distance Weighted k-Nearest Neighbor algorithm called MR-DWkNN to process and analyze the Big Data in the Hadoop cluster environment. The proposed distributed algorithm is based on supervised learning performs both regression tasks as well as classification tasks on large-volume of Big Data applications. Three performance metrics, such as Root Mean Squared Error (RMSE), Determination coefficient (R2) for regression task, and Accuracy for classification tasks are utilized for the performance measure of the proposed MR-DWkNN algorithm. The extensive experimental results shows that there is an average increase of 3% to 4.5% prediction and classification performances as compared to standard distributed k-NN algorithm and a considerable decrease of Root Mean Squared Error (RMSE) with good parallelism characteristics of scalability and speedup thus, proves its effectiveness in Big Data predictive and classification applications.

show abstract

A novel framework for image-based plant disease detection using hybrid deep learning approach

Cited by 56 publications

References 50 publications

ELDA: enhanced linear discriminant analysis for cashew crop disease detection using precision agriculture

ELDA: enhanced linear discriminant analysis for cashew crop disease detection using precision agriculture

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

Map-Reduce based Distance Weighted k-Nearest Neighbor Machine Learning Algorithm for Big Data Applications

Contact Info

Product

Resources

About