ArticleSensors (Basel, Switzerland)2022
Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Prediction of growth parameters in the medicinal plantFrontiers in plant science · 2026Article
- Antioxidant profiling and quality assessment of Lithocarpus polystachyus sweet tea using LC-ECD and LC-MS/MS.Scientific reports · 2025Article
- Evaluation of ground based spectral imaging for real time maize biomass monitoring.Frontiers in plant science · 2025Article
- A global dataset for assessing nitrogen-related plant traits using drone imagery in major field crop species.Scientific data · 2024Article
- Prediction of Needle Physiological Traits Using UAV Imagery for Breeding Selection of Slash Pine.Plant phenomics (Washington, D.C.) · 2023Article
- A New Approach to Optimize SVM for Insulator State Identification Based on Improved PSO Algorithm.Sensors (Basel, Switzerland) · 2022Article
- Integrated diagnosis and time-series sensitivity evaluation of nutrient deficiencies in medicinal plant (Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Sugarcane is the main industrial crop for sugar production, and its growth status is closely related to fertilizer, water, and light input. Unmanned aerial vehicle (UAV)-based multispectral imagery is widely used for high-throughput phenotyping, since it can rapidly predict crop vigor at field scale. This study focused on the potential of drone multispectral images in predicting canopy nitrogen concentration (CNC) and irrigation levels for sugarcane. An experiment was carried out in a sugarcane field with three irrigation levels and five fertilizer levels. Multispectral images at an altitude of 40 m were acquired during the elongating stage. Partial least square (PLS), backpropagation neural network (BPNN), and extreme learning machine (ELM) were adopted to establish CNC prediction models based on various combinations of band reflectance and vegetation indices. The simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and normalized green-blue difference index (NGBDI) were selected as model inputs due to their higher grey relational degree with the CNC and lower correlation between one another. The PLS model based on the five-band reflectance and the three vegetation indices achieved the best accuracy (
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Registered trials
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