Tuned weighted feature fusion with hybridized DNN-RNN framework for plant disease detection and classification-Reference-Cited by-同舟云学术

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Tuned weighted feature fusion with hybridized DNN-RNN framework for plant disease detection and classification

Published:2024-02-16 Issue:5 Volume:45 Page:1608-1639
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Chaitanya K P¹,Posonia A. Mary²

Affiliation:

1. Research Scholar, Sathyabama Institute of Science and Technology (Deemed to be University), Semmancheri, Chennai, TamilNadu, India

2. Computer science and Engineering, Sathyabama Institute of Science and Technology (Deemed to be University), Semmancheri, Chennai, TamilNadu, India

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2024.2313995

Reference41 articles.

1. Plant Disease Detection in Imbalanced Datasets Using Efficient Convolutional Neural Networks With Stepwise Transfer Learning

2. Research on Recognition Model of Crop Diseases and Insect Pests Based on Deep Learning in Harsh Environments

3. Performance Analysis of Deep Transfer Learning Models for the Automated Detection of Cotton Plant Diseases

4. Intelligent Monitoring of Stress Induced by Water Deficiency in Plants Using Deep Learning

5. Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture

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1. Review of deep learning-based methods for non-destructive evaluation of agricultural products;Biosystems Engineering;2024-09

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