Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms
Authors:
- Jorge Enrique ChaparroJorge Enrique Chaparro
- José Edinson AedoJAJosé Edinson Aedo
- Felipe Lumbreras RuizFelipe Lumbreras Ruiz
Open Access
- 6 July 2024
- journal article
- research article
- Published by Elsevier in Journal of Agriculture and Food Research
- Vol. 18
Abstract
No abstract availableKeywords
This publication has 13 references indexed in Scilit:
- Influence of Silver Nanoparticles on Photosynthetic Pigment Content and Mineral Uptake in Pineapple Seedlings Grown In Vitro under Aluminum StressAgronomy, 2023
- Innovative non-destructive technologies for quality monitoring of pineapples: Recent advances and applicationsTrends in Food Science & Technology, 2023
- In-situ fruit analysis by means of LiDAR 3D point cloud of normalized difference vegetation index (NDVI)Computers and Electronics in Agriculture, 2023
- Why do we need more research on pineapple (Ananas comosus L. Merr.)? A discussion based on a bibliometric reviewRevista Brasileira de Fruticultura, 2023
- An assessment of background removal approaches for improved estimation of rice leaf nitrogen concentration with unmanned aerial vehicle multispectral imagery at various observation timesField Crops Research, 2022
- Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithmsComputers and Electronics in Agriculture, 2022
- Performance of soil moisture sensors in gypsiferous and salt-affected soilsBiosystems Engineering, 2021
- Nitrogen2021
- Diagnosis and management of nutrient constraints in pineapple2020
- Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocolsISPRS Journal of Photogrammetry and Remote Sensing, 2019