A Deep Neural Network based disease detection scheme for Citrus fruits

Kukreja, Vinay; Dhiman, Poonam

doi:10.1109/icosec49089.2020.9215359

Cited by 137 publications

(27 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the results of the experiments, the suggested ensemble beat other competing CNN algorithms with an accuracy of 99.04 percent. Kukreja et al [37] suggested a robust CNN algorithm for identifying and providing an effective approach for identifying apparent citrus fruit problems. The suggested method is compared to a dense model that does not employ data augmentation or preprocessing methods.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

et al. 2021

View full text Add to dashboard Cite

Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. As a result, designing an automated detection system for citrus plant diseases is important. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, leading us to apply them to the challenge of recognizing citrus fruit and leaf diseases. In this paper, an integrated approach is used to suggest a convolutional neural networks (CNNs) model. The proposed CNN model is intended to differentiate healthy fruits and leaves from fruits/leaves with common citrus diseases such as Black spot, canker, scab, greening, and Melanose. The proposed CNN model extracts complementary discriminative features by integrating multiple layers. The CNN model was checked against many state-of-the-art deep learning models on the Citrus and PlantVillage datasets. The experimental results indicate that the CNN Model outperforms the competitors on a number of measurement metrics. The CNN Model has a test accuracy of 94.55 percent, making it a valuable decision support tool for farmers looking to classify citrus fruit/leaf diseases.

show abstract

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Kukreja et al [49] used a dense CNN model was used without doing preprocessing and data augmentation on 150 images and achieved an accuracy of 67 percent but the proposed model has used data augmentation and preprocessing to enhance the CNN performance and have used 1200 images. The overall accuracy of the proposed model is 89.1%.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Spinal Cord MRI Segmentation Techniques and Algorithms: A Survey

Garg¹,

Bhagyashree²

2021

SN COMPUT. SCI.

View full text Add to dashboard Cite

Spinal cord tumour is an abnormal growth of cells in and around the spinal cord. Detecting spinal cord tumours is a very crucial process. Identifying the tumour from MRI is difficult because of the shape size and flexible nature of the spinal cord. The cross-sectional area of the cord is also very less. The boundary of tumour can be identified in the MRI by various segmentation techniques. Segmentation is one of the necessary steps. Spinal cord segmentation techniques are not as developed as brain segmentation techniques. In this paper, we review a number of spinal cord segmentation techniques like Intensity-based, Surface-Based Image-based techniques, and Machine learning techniques. A detailed analysis of the various segmentation techniques is proposed.

show abstract

“…Along with the GMM-HMM system, a hybrid DNN-HMM based Punjabi-ASR system was developed. The posterior probabilities of the GMM based system were replaced by the DNN-HMM based Punjabi-ASR system [27]. Our DNN based system is built using 4 -8 hidden layers with 1K-2K hidden units in each hidden layer.…”

Section: Gmm-hmm and Dnn-hmm Acoustic Modelmentioning

confidence: 99%

Usage of Prosody Modification and Acoustic Adaptation for Robust Automatic Speech Recognition (ASR) System

Bhardwaj¹,

Kukreja²,

Singh³

2021

RIA

Self Cite

View full text Add to dashboard Cite

Most of the automatic speech recognition (ASR) systems are trained using adult speech due to the less availability of the children's speech dataset. The speech recognition rate of such systems is very less when tested using the children's speech, due to the presence of the inter-speaker acoustic variabilities between the adults and children's speech. These inter-speaker acoustic variabilities are mainly because of the higher pitch and lower speaking rate of the children. Thus, the main objective of the research work is to increase the speech recognition rate of the Punjabi-ASR system by reducing these inter-speaker acoustic variabilities with the help of prosody modification and speaker adaptive training. The pitch period and duration (speaking rate) of the speech signal can be altered with prosody modification without influencing the naturalness, message of the signal and helps to overcome the acoustic variations present in the adult's and children's speech. The developed Punjabi-ASR system is trained with the help of adult speech and prosody-modified adult speech. This prosody modified speech overcomes the massive need for children's speech for training the ASR system and improves the recognition rate. Results show that prosody modification and speaker adaptive training helps to minimize the word error rate (WER) of the Punjabi-ASR system to 8.79% when tested using children's speech.

show abstract

A Deep Neural Network based disease detection scheme for Citrus fruits

Cited by 137 publications

References 12 publications

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

Spinal Cord MRI Segmentation Techniques and Algorithms: A Survey

Usage of Prosody Modification and Acoustic Adaptation for Robust Automatic Speech Recognition (ASR) System

Contact Info

Product

Resources

About