Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms
Nitrogen is the most important nutritional element during the vegetative growth phase of the pineapple crop; however, its presence in the soil is insufficient to meet plant demands. In this study, nine machine learning techniques were validated to estimate the total nitrogen (TN) content in MD2 pine...
Saved in:
| Main Authors: | Jorge Enrique Chaparro, José Edinson Aedo, Felipe Lumbreras Ruiz |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Journal of Agriculture and Food Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266615432400245X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhancing sustainable Chinese cabbage production: a comparative analysis of multispectral image instance segmentation techniques
by: Xinru Yuan, et al.
Published: (2024-11-01) -
Assessing the Impact of UAV Flight Altitudes on the Accuracy of Multispectral Indices
by: Stamenković Zoran, et al.
Published: (2024-12-01) -
Near-Optimal Efficient MIMO Receiver for Miniature UAV-IoT Communications
by: Anas Saci, et al.
Published: (2024-01-01) -
Minimizing Seam Lines in UAV Multispectral Image Mosaics Utilizing Irradiance, Vignette, and BRDF
by: Hoyong Ahn, et al.
Published: (2025-01-01) -
Blockchain-based energy consumption approaches in IoT
by: Sk Md. Habibullah, et al.
Published: (2024-11-01)