On the public citation graph

Day: Data-Driven Machine Learning Framework for Avocado Yield Forecasting

https://doi.org/10.2139/ssrn.5084963
0 signed · 33 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s11119-010-9187-0openalex-snapshot
Yield prediction in apple orchards based on image processing
inferredcites →https://doi.org/10.1007/s11119-011-9245-2openalex-snapshot
Multilevel systematic sampling to estimate total fruit number for yield forecasts
inferredcites →https://doi.org/10.1007/s11119-015-9407-8openalex-snapshot
Use of multi-spectral airborne imagery to improve yield sampling in viticulture
inferredcites →https://doi.org/10.1007/s11119-018-9614-1openalex-snapshot
Estimation of fruit load in mango orchards: tree sampling considerations and use of machine vision and satellite imagery
inferredcites →https://doi.org/10.1007/s11119-020-09717-3openalex-snapshot
Vineyard yield estimation by combining remote sensing, computer vision and artificial neural network techniques
inferredcites →https://doi.org/10.1007/s11119-022-09897-0openalex-snapshot
Statistical and machine learning methods for crop yield prediction in the context of precision agriculture
inferredcites →https://doi.org/10.1007/s42979-023-02463-zopenalex-snapshot
Neural Network Model for Predicting Apple Yield Based on Arrival of Phenological Stage in Conjunction with Leaf disease, Soil and Weather Parameters
inferredcites →https://doi.org/10.1016/j.compag.2016.09.014openalex-snapshot
Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors
inferredcites →https://doi.org/10.1016/j.compag.2022.106812openalex-snapshot
Fruit yield prediction and estimation in orchards: A state-of-the-art comprehensive review for both direct and indirect methods
inferredcites →https://doi.org/10.1016/j.compag.2022.107124openalex-snapshot
An expert system for insect pest population dynamics prediction
inferredcites →https://doi.org/10.1016/j.compag.2024.109111openalex-snapshot
A novel transformer-based neural network under model interpretability for improving wheat yield estimation using remotely sensed multi-variables
inferredcites →https://doi.org/10.1016/j.rse.2020.112276openalex-snapshot
Regional winter wheat yield estimation based on the WOFOST model and a novel VW-4DEnSRF assimilation algorithm
inferredcites →https://doi.org/10.1016/j.scienta.2018.04.021openalex-snapshot
Potential geography and productivity of “Hass” avocado crops in Colombia estimated by ecological niche modeling
inferredcites →https://doi.org/10.1038/s41598-021-97221-7openalex-snapshot
An interaction regression model for crop yield prediction
inferredcites →https://doi.org/10.1038/s41598-024-54354-9openalex-snapshot
Multi-source information fusion-driven corn yield prediction using the Random Forest from the perspective of Agricultural and Forestry Economic Management
inferredcites →https://doi.org/10.1080/0952813x.2022.2062458openalex-snapshot
A novel machine learning approach for rice yield estimation
inferredcites →https://doi.org/10.1088/1748-9326/aa518aopenalex-snapshot
Comparing estimates of climate change impacts from process-based and statistical crop models
inferredcites →https://doi.org/10.1088/2515-7620/ac81a4openalex-snapshot
Fruit weight and yield estimation models for five avocado cultivars in Ethiopia
inferredcites →https://doi.org/10.1109/inocon57975.2023.10101144openalex-snapshot
Wheat Yield Prediction using Temporal Fusion Transformers
inferredcites →https://doi.org/10.1111/gcbb.12384openalex-snapshot
Evaluation of Agricultural Production Systems Simulator as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments
inferredcites →https://doi.org/10.1155/2017/1353691openalex-snapshot
Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications
inferredcites →https://doi.org/10.2166/wcc.2019.179openalex-snapshot
Evaluating the climate change impact on water use efficiency of cotton-wheat in semi-arid conditions using DSSAT model
inferredcites →https://doi.org/10.3390/agriculture10090385openalex-snapshot
Assessment of Olive Tree Canopy Characteristics and Yield Forecast Model Using High Resolution UAV Imagery
inferredcites →https://doi.org/10.3390/agriculture8070094openalex-snapshot
Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes
inferredcites →https://doi.org/10.3390/agriculture9030054openalex-snapshot
Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques
inferredcites →https://doi.org/10.3390/agronomy11071409openalex-snapshot
Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time
inferredcites →https://doi.org/10.3390/atmos10070378openalex-snapshot
Weather Based Strawberry Yield Forecasts at Field Scale Using Statistical and Machine Learning Models
inferredcites →https://doi.org/10.3390/rs10121866openalex-snapshot
Exploring the Potential of High Resolution WorldView-3 Imagery for Estimating Yield of Mango
inferredcites →https://doi.org/10.3390/rs13163073openalex-snapshot
Comparison of Machine-Learning and CASA Models for Predicting Apple Fruit Yields from Time-Series Planet Imageries
inferredcites →https://doi.org/10.3390/rs14235942openalex-snapshot
Potential of Time-Series Sentinel 2 Data for Monitoring Avocado Crop Phenology
inferredcites →https://doi.org/10.3390/rs70809886openalex-snapshot
Temporal Dependency of Yield and Quality Estimation through Spectral Vegetation Indices in Pear Orchards
inferredcites →https://doi.org/10.3390/rs9040317openalex-snapshot
Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.