Plant Nutrient Deficiency Detection and Classification - A Review-Reference-Cited by-同舟云学术

Plant Nutrient Deficiency Detection and Classification - A Review

Published:2023-08-03 Issue: Volume: Page:
ISSN:
Container-title:2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA)
language:
Short-container-title:

Author:

J Aiswarya¹,K Mariammal¹,K Veerappan¹

Affiliation:

1. Madras Institute of Technology Campus, Anna University,Department of Electronics Engineering,Chennai,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10220576/10220579/10220778.pdf?arnumber=10220778

Reference20 articles.

1. Classification of nutrient deficiencies in rice crop using denseNet-BC

2. Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images

3. Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images

4. Using EfficientNet and transfer learning for image-based diagnosis of nutrient deficiencies

5. 2020 8th international conference on information and communication technology (ICoICT);bahtiar;2020 8th International Conference on Information and Communication Technology (ICoICT),0

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. PND-Net: plant nutrition deficiency and disease classification using graph convolutional network;Scientific Reports;2024-07-05

2. Augmented Insights: A Qualified Evaluation of Deep Learning Representations for Enhancing Banana Leaf Spot Disease Detection;2024 International Conference on Social and Sustainable Innovations in Technology and Engineering (SASI-ITE);2024-02-23