Framework to Detect NPK Deficiency in Maize Plants Using CNN-Reference-Cited by-同舟云学术

Framework to Detect NPK Deficiency in Maize Plants Using CNN

Published:2020-11-10 Issue: Volume: Page:366-376
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
Short-container-title:

Author:

Jahagirdar Padmashri,Budihal Suneeta V.

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-15-6353-9_33

Reference10 articles.

1. Miyatra, A., Solanki, S.: Disease and nutrient deficiency detection in cotton plant. International Journal of Engineering Development and Research 2(2), 2801–2804 (2014)

2. X. Yao, W. Luo, and Z. Yuan, “An adaptive and quantitative rubber nutrient status analyzing system by digital foliar images” 3$$^{rd}$$International Congress on Image and Signal Processing (CISP), 2010 , vol. 5, pp. 2492-2495, IEEE, 2010

3. M. A. Hairuddin, N. M. Tahir, and S. R. S. Baki, “Overview of image processing approach for nutrient deficiencies detection in elaeis guineensis,” IEEE International Conference on System Engineering and Technology (ICSET), 2011, pp. 116-120, IEEE, 2011

4. A. Panwar, M. Al-Lami, P. Bharti, S. Chellappan, and J. Burken, “Determining the effectiveness of soil treatment on plant stress using smartphone cameras,” International Conference on Selected Topics in Mobile & Wireless Networking (MoWNeT), 2016, pp. 1-8, IEEE, 2016

5. L. Romualdo, P. H. d. C. Luz, F. Devechio, M. Marin, A. Z’uñniga, O. M. Bruno, and V. R. Herling, “Use of artificial vision techniques for diagnostic of nitrogen nutritional status in maize plants,” Computers and electronics in agriculture, vol. 104, pp. 63–70, 2014

Cited by 10 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. IncepV3Dense: Deep Ensemble Based Average Learning Strategy for Identification of Micro-Nutrient Deficiency in Banana Crop;IEEE Access;2024

2. Deep Transfer Learning for Image Classification of Phosphorus Nutrition States in Individual Maize Leaves;Electronics;2023-12-19

3. The ontology model for selecting quality melons uses hidden semantic data based on melon knowledge domains;2023 IEEE 13th Symposium on Computer Applications & Industrial Electronics (ISCAIE);2023-05-20

4. Computer Vision and Machine Learning in Agriculture: An Introduction;Computer Vision and Machine Learning in Agriculture, Volume 3;2023

5. Classification of Nutritional Deficiencies in Cabbage Leave Using Random Forest;2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART);2022-12-16