Advances in Remote Sensing

Advances in Remote Sensing

ISSN Print: 2169-267X
ISSN Online: 2169-2688
www.scirp.org/journal/ars
E-mail: ars@scirp.org
"A Novel Approach for Sugarcane Yield Prediction Using Landsat Time Series Imagery: A Case Study on Bundaberg Region"
written by Muhammad Moshiur Rahman, Andrew J. Robson,
published by Advances in Remote Sensing, Vol.5 No.2, 2016
has been cited by the following article(s):
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[1] Data-driven, early-season forecasts of block sugarcane yield for precision agriculture
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[2] Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana
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[3] Sugarcane yields prediction at the row level using a novel cross-validation approach to multi-year multispectral images
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[4] Combined Use of Landsat 8 and Sentinel 2A Imagery for Improved Sugarcane Yield Estimation in Wonji-Shoa, Ethiopia
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[5] An investigation on the best-fit models for sugarcane biomass estimation by linear mixed-effect modelling on unmanned aerial vehicle-based multispectral …
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[6] Regional Yield Estimation for Sugarcane Using MODIS and Weather Data: A Case Study in Florida and Louisiana, United States of America
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[7] Quantifying Hail Damage in Crops Using Sentinel-2 Imagery
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[8] A new sugarcane yield model using the SiPAR model
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[9] Design and Development of Sugarcane Maturity Identifier through Phenotypes via Image Processing
… on Cybernetics and …, 2022
[10] Sugarcane Yield Prediction Using Vegetation Indices in Northern Karnataka, India
2022
[11] High-resolution data for mapping the spatio-temporal variability of sugarcane fields
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[12] High Accuracy Pre-Harvest Sugarcane Yield Forecasting Model Utilizing Drone Image Analysis, Data Mining, and Reverse Design Method
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[13] Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique. Remote Sens. 2021, 13, 232
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[14] High-resolution unmanned aircraft systems imagery for stay-green characterization in grain sorghum (Sorghum bicolor L.)
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[15] Integrating satellite imagery and environmental data to predict field-level cane and sugar yields in Australia using machine learning
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[16] Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
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[17] Predição de dados agronômicos em goiabeiras e separação de alvos por meio de Veículo Aéreo Não TripuladoPredição de dados agronômicos em goiabeiras e …
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[18] Predição de dados agronômicos em goiabeiras e separação de alvos por meio de Veículo Aéreo Não Tripulado
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[19] A Satellite-Based Methodology for Harvest Date Detection and Yield Prediction in Sugarcane
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[20] Relationship of vegetation indices and SPAD meter readings with sugarcane leaf nitrogen under Pampanga Mill District, Philippines condition
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[21] An improved workflow for calibration and downscaling of GCM climate forecasts for agricultural applications–A case study on prediction of sugarcane yield in Australia
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[22] Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging
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[23] Prediction of Antioxidant Activity of Cherry Fruits from UAS Multispectral Imagery Using Machine Learning
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[24] High-resolution UAS multispectral imaging for cultivar selection in grain sorghum breeding trials
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[25] Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level
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[26] Remote Sensing for Sugarcane Crop Yield Estimation in Eswatini: Case of Lower Usuthu Smallholder Irrigation Project Sugarcane Farms
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[27] Prediction of Yield and Lignocellulosic Composition in Energy Cane Using Unmanned Aerial Systems
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[28] High-resolution imagery data to assess the spatial variability of sugarcane fields
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[29] Evaluation of Vegetation Indices for Sugarcane Yield Modeling with Emphasis on Growth Pattern Based on Satellite Imagery:(Case Study: Khouzestan Imam …
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[30] ESTIMATIVA DA PRODUTIVIDADE DE CANA-DE-AÇÚCAR UTILIZANDO IMAGENS LANDSAT E RANDOM FOREST
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[31] PREDICTING SUGARCANE YIELDS IN KHUZESTAN USING A LARGE TIME-SERIES OF REMOTE SENSING IMAGERY REGION
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[32] PREDICTING CROP YIELD AND FIELD ENERGY OUTPUT FOR OIL PALM USING GENETIC ALGORITHM AND NEURAL NETWORK MODELS
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[33] Sugarcane Yield Estimation Using LANDSAT Time-Series Imagery:(Case Study-MianAB Region in Khouzestan Province)
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[34] Literature review of adoption of precision system technologies in vegetable production
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[35] Precision agriculture and sugarcane production–a case study from the Burdekin region of Australia RGV Bramley, CSIRO, Australia; TA Jensen, University …
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[36] Using GeoEye-1 Imagery for Multi-Temporal Object-Based Detection of Canegrub Damage in Sugarcane Fields in Queensland, Australia
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[37] Precision agriculture and sugarcane production–a case study from the Burdekin region of Australia RGV Bramley, CSIRO, Australia; TA Jensen, University of Southern …
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[38] Sugar cane monitoring and analysis using remote sensing
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[39] Completing yearly land cover maps for accurately describing annual changes of tropical landscapes
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[40] Remote Sensing-Based Yield Forecasting for Sugarcane (Saccharum officinarum L.) Crop in India
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[41] Exploring the Potential of High Resolution WorldView-3 Imagery for Estimating Yield of Mango
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[42] 'Sugar from space': Using satellite imagery to predict cane yield and variability
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[43] Detección de estrés nutricional con cámaras multiespectrales
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[44] Developing remote sensing as an industry wide yield forecasting, nitrogen mapping and research aid; final report 2013/025
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[45] Multi-temporal landsat algorithms for the yield prediction of sugarcane crops in Australia
7th Asian-Australasian Conference on Precision Agriculture, 2017
[46] Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia
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