On the public citation graph
Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US
0 signed · 148 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.
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known work and none has been retracted — and get a dated CiteStamped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.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-0-585-33173-7_10openalex-snapshot
Optical Properties of Leaves: Modelling and Experimental Studies
inferredcites →https://doi.org/10.1007/978-3-031-28057-3openalex-snapshot
Soil Physical Environment and Plant Growth
inferredcites →https://doi.org/10.1007/978-90-481-9133-8_8openalex-snapshot
Spatial Variation and Site-Specific Management Zones
inferredcites →https://doi.org/10.1007/978-981-10-4274-4openalex-snapshot
Soil Pollution - An Emerging Threat to Agriculture
inferredcites →https://doi.org/10.1007/s11119-011-9221-xopenalex-snapshot
The impact of topography on soil properties and yield and the effects of weather conditions
inferredcites →https://doi.org/10.1007/s11119-018-09628-4openalex-snapshot
An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning
inferredcites →https://doi.org/10.1007/s11119-019-09689-zopenalex-snapshot
Mapping sub-field maize yields in Nebraska, USA by combining remote sensing imagery, crop simulation models, and machine learning
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/s11119-023-09995-7openalex-snapshot
Within-field yield stability and gross margin variations across corn fields and implications for precision conservation
inferredcites →https://doi.org/10.1007/s11119-023-10057-1openalex-snapshot
Sweet corn yield prediction using machine learning models and field-level data
inferredcites →https://doi.org/10.1016/0034-4257(79)90013-0openalex-snapshot
Red and photographic infrared linear combinations for monitoring vegetation
inferredcites →https://doi.org/10.1016/0034-4257(88)90106-xopenalex-snapshot
A soil-adjusted vegetation index (SAVI)
inferredcites →https://doi.org/10.1016/0034-4257(94)90134-1openalex-snapshot
A modified soil adjusted vegetation index
inferredcites →https://doi.org/10.1016/0034-4257(94)90136-8openalex-snapshot
Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves
inferredcites →https://doi.org/10.1016/bs.agron.2021.08.005openalex-snapshot
Principles and applications of topography in precision agriculture
inferredcites →https://doi.org/10.1016/j.agrformet.2008.12.007openalex-snapshot
Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices
inferredcites →https://doi.org/10.1016/j.agrformet.2012.12.013openalex-snapshot
Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV)
inferredcites →https://doi.org/10.1016/j.agrformet.2017.08.001openalex-snapshot
Improving the accuracy of satellite-based high-resolution yield estimation: A test of multiple scalable approaches
inferredcites →https://doi.org/10.1016/j.compag.2018.07.016openalex-snapshot
Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield
inferredcites →https://doi.org/10.1016/j.compag.2021.106092openalex-snapshot
Estimation of corn yield based on hyperspectral imagery and convolutional neural network
inferredcites →https://doi.org/10.1016/j.compag.2022.106852openalex-snapshot
Improving wheat yield prediction integrating proximal sensing and weather data with machine learning
inferredcites →https://doi.org/10.1016/j.eja.2006.10.007openalex-snapshot
A simple model of regional wheat yield based on NDVI data
inferredcites →https://doi.org/10.1016/j.eswa.2023.122778openalex-snapshot
A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation
inferredcites →https://doi.org/10.1016/j.fcr.2020.107788openalex-snapshot
The role of topography, soil, and remotely sensed vegetation condition towards predicting crop yield
inferredcites →https://doi.org/10.1016/j.geoderma.2016.03.025openalex-snapshot
POLARIS: A 30-meter probabilistic soil series map of the contiguous United States
inferredcites →https://doi.org/10.1016/j.petrol.2021.109244openalex-snapshot
Application of gradient boosting regression model for the evaluation of feature selection techniques in improving reservoir characterisation predictions
inferredcites →https://doi.org/10.1016/j.rse.2003.12.013openalex-snapshot
Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture
inferredcites →https://doi.org/10.1016/j.rse.2005.09.002openalex-snapshot
Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy
inferredcites →https://doi.org/10.1016/j.rse.2005.09.007openalex-snapshot
Spatio–temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers
inferredcites →https://doi.org/10.1016/j.rse.2014.07.010openalex-snapshot
A physically based vegetation index for improved monitoring of plant phenology
inferredcites →https://doi.org/10.1016/j.rse.2015.04.021openalex-snapshot
A scalable satellite-based crop yield mapper
inferredcites →https://doi.org/10.1016/j.rse.2019.111599openalex-snapshot
Soybean yield prediction from UAV using multimodal data fusion and deep learning
inferredcites →https://doi.org/10.1016/j.still.2020.104880openalex-snapshot
Assessing the impact of agricultural field traffic on corn grain yield using remote sensing and machine learning
inferredcites →https://doi.org/10.1016/j.wace.2015.08.001openalex-snapshot
Temperature extremes: Effect on plant growth and development
inferredcites →https://doi.org/10.1016/s0034-4257(00)00113-9openalex-snapshot
Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance
inferredcites →https://doi.org/10.1016/s0034-4257(00)00197-8openalex-snapshot
Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density
inferredcites →https://doi.org/10.1016/s0034-4257(01)00332-7openalex-snapshot
Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data
inferredcites →https://doi.org/10.1016/s0034-4257(02)00010-xopenalex-snapshot
Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages
inferredcites →https://doi.org/10.1016/s0034-4257(02)00096-2openalex-snapshot
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
inferredcites →https://doi.org/10.1016/s0034-4257(96)00072-7openalex-snapshot
Use of a green channel in remote sensing of global vegetation from EOS-MODIS
inferredcites →https://doi.org/10.1016/s0166-2481(97)80029-3openalex-snapshot
Chapter 2 Physical attributes of soil quality
inferredcites →https://doi.org/10.1016/s0176-1617(99)80314-9openalex-snapshot
A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Tests using Eucalyptus Leaves
inferredcites →https://doi.org/10.1016/s1161-0301(02)00107-7openalex-snapshot
The DSSAT cropping system model
inferredcites →https://doi.org/10.1016/s1672-6308(07)60027-4openalex-snapshot
New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice
inferredcites →https://doi.org/10.1023/a:1004233303066openalex-snapshot
Water, nutrients and slope position in on-farm pearl millet cultivation in the Sahel
inferredcites →https://doi.org/10.1029/2002gl016450openalex-snapshot
Remote estimation of leaf area index and green leaf biomass in maize canopies
inferredcites →https://doi.org/10.1029/2018wr022797openalex-snapshot
POLARIS Soil Properties: 30‐m Probabilistic Maps of Soil Properties Over the Contiguous United States
inferredcites →https://doi.org/10.1034/j.1399-3054.1999.106119.xopenalex-snapshot
Non‐destructive optical detection of pigment changes during leaf senescence and fruit ripening
inferredcites →https://doi.org/10.1037/a0016973openalex-snapshot
An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests.
inferredcites →https://doi.org/10.1038/s41586-020-2649-2openalex-snapshot
Array programming with NumPy
inferredcites →https://doi.org/10.1038/s41598-019-50480-xopenalex-snapshot
Early Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery
inferredcites →https://doi.org/10.1038/s41598-021-00936-wopenalex-snapshot
Aerial high-throughput phenotyping of peanut leaf area index and lateral growth
inferredcites →https://doi.org/10.1046/j.1469-8137.1999.00424.xopenalex-snapshot
Assessing leaf pigment content and activity with a reflectometer
inferredcites →https://doi.org/10.1061/jhyeff.heeng-6227openalex-snapshot
Machine Learning Models for Prediction of Shade-Affected Stream Temperatures
inferredcites →https://doi.org/10.1073/pnas.1921562117openalex-snapshot
Contrast trees and distribution boosting
inferredcites →https://doi.org/10.1078/0176-1617-01176openalex-snapshot
Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation
inferredcites →https://doi.org/10.1080/01431160210163074openalex-snapshot
Vegetation indices derived from high-resolution airborne videography for precision crop management
inferredcites →https://doi.org/10.1080/01431169008955110openalex-snapshot
Hue image RGB colour composition. A simple technique to suppress shadow and enhance spectral signature
inferredcites →https://doi.org/10.1080/01431169308953986openalex-snapshot
Red edge spectral measurements from sugar maple leaves
inferredcites →https://doi.org/10.1080/10106040108542184openalex-snapshot
Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat
inferredcites →https://doi.org/10.1088/1748-9326/ab7df9openalex-snapshot
Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest
inferredcites →https://doi.org/10.1093/bioinformatics/btab074openalex-snapshot
Random forest of perfect trees: concept, performance, applications and perspectives
inferredcites →https://doi.org/10.1097/00010694-200104000-00006openalex-snapshot
USING SOIL PROPERTIES TO PREDICT WHEAT YIELDS ON ILLINOIS SOILS
inferredcites →https://doi.org/10.1109/icdmw58026.2022.00137openalex-snapshot
Corn Grain Yield Prediction Using UAV-based High Spatiotemporal Resolution Multispectral Imagery
inferredcites →https://doi.org/10.1109/igarss.1989.576128openalex-snapshot
TSAVI: A Vegetation Index Which Minimizes Soil Brightness Effects On LAI And APAR Estimation
inferredcites →https://doi.org/10.1109/ijcnn.2009.5178693openalex-snapshot
On the selection of decision trees in Random Forests
inferredcites →https://doi.org/10.1117/12.864796openalex-snapshot
Statistical derivation of fPAR and LAI for irrigated cotton and rice in arid Uzbekistan by combining multi-temporal RapidEye data and ground measurements
inferredcites →https://doi.org/10.1126/science.1251423openalex-snapshot
Greater Sensitivity to Drought Accompanies Maize Yield Increase in the U.S. Midwest
inferredcites →https://doi.org/10.1201/9781315277189-6openalex-snapshot
Soil Properties (Physical, Chemical, Biological, Mechanical)
inferredcites →https://doi.org/10.1201/b18759-2openalex-snapshot
Historical Evolution and Recent Advances in Precision Farming
inferredcites →https://doi.org/10.1214/aos/1013203451openalex-snapshot
Greedy function approximation: A gradient boosting machine.
inferredcites →https://doi.org/10.12694/scpe.v21i4.1714openalex-snapshot
Principles and Practices of Making Agriculture Sustainable: Crop Yield prediction using Random Forest
inferredcites →https://doi.org/10.13031/aim.202200545openalex-snapshot
Corn Emergence Uniformity at Different Planting Depths and Yield Estimation Using UAV Imagery
inferredcites →https://doi.org/10.1371/journal.pone.0156571openalex-snapshot
Random Forests for Global and Regional Crop Yield Predictions
inferredcites →https://doi.org/10.17221/257-pseopenalex-snapshot
Topography impact on nutrition content in soil and yield
inferredcites →https://doi.org/10.21276/ijircst.2021.9.2.7openalex-snapshot
Crop Yield Prediction Using Random Forest Algorithm for Major Cities in Maharashtra State
inferredcites →https://doi.org/10.2134/1997.stateofsitespecific.c5openalex-snapshot
Field Heterogeneity in Soil—Plant Nitrogen Relationships:Implicatinos for Site‐Specific Management
inferredcites →https://doi.org/10.2134/age2018.04.0008openalex-snapshot
Topographic Controls on Soil Nutrient Variations in a Silvopasture System
inferredcites →https://doi.org/10.2134/agronj2001.933583xopenalex-snapshot
Use of Remote‐Sensing Imagery to Estimate Corn Grain Yield
inferredcites →https://doi.org/10.2134/agronj2006.0326openalex-snapshot
Yield Editor: Software for Removing Errors from Crop Yield Maps
inferredcites →https://doi.org/10.2134/agronj2010.0395openalex-snapshot
Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index
inferredcites →https://doi.org/10.2134/agronj2012.0393openalex-snapshot
Corn Grain and Stover Yield Prediction at R1 Growth Stage
inferredcites →https://doi.org/10.2489/jswc.75.2.27aopenalex-snapshot
Soil organic matter content and crop yield
inferredcites →https://doi.org/10.25080/majora-92bf1922-00aopenalex-snapshot
Data Structures for Statistical Computing in Python
inferredcites →https://doi.org/10.3389/fpls.2017.01111openalex-snapshot
Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives
inferredcites →https://doi.org/10.3389/fpls.2020.01120openalex-snapshot
Forecasting Corn Yield With Machine Learning Ensembles
inferredcites →https://doi.org/10.3389/fpls.2021.658621openalex-snapshot
Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models
inferredcites →https://doi.org/10.3389/fpls.2022.912332openalex-snapshot
Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding
inferredcites →https://doi.org/10.3389/frai.2021.647999openalex-snapshot
Machine Learning-Based Modeling of Spatio-Temporally Varying Responses of Rainfed Corn Yield to Climate, Soil, and Management in the U.S. Corn Belt
inferredcites →https://doi.org/10.3390/agriculture14040565openalex-snapshot
Phenotyping Peanut Drought Stress with Aerial Remote-Sensing and Crop Index Data
inferredcites →https://doi.org/10.3390/agriculture14071110openalex-snapshot
Unmanned Aerial Vehicle-Measured Multispectral Vegetation Indices for Predicting LAI, SPAD Chlorophyll, and Yield of Maize
inferredcites →https://doi.org/10.3390/agronomy10071046openalex-snapshot
Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms
inferredcites →https://doi.org/10.3390/agronomy12081945openalex-snapshot
Evaluation of the U.S. Peanut Germplasm Mini-Core Collection in the Virginia-Carolina Region Using Traditional and New High-Throughput Methods
inferredcites →https://doi.org/10.3390/agronomy13092390openalex-snapshot
Defining the Ideal Phenological Stage for Estimating Corn Yield Using Multispectral Images
inferredcites →https://doi.org/10.3390/drones3010006openalex-snapshot
Greenness Indices from a Low-Cost UAV Imagery as Tools for Monitoring Post-Fire Forest Recovery
inferredcites →https://doi.org/10.3390/genes9090424openalex-snapshot
Human Age Prediction Based on DNA Methylation Using a Gradient Boosting Regressor
inferredcites →https://doi.org/10.3390/plants12030446openalex-snapshot
Analysis of Corn Yield Prediction Potential at Various Growth Phases Using a Process-Based Model and Deep Learning
inferredcites →https://doi.org/10.3390/rs13040821openalex-snapshot
Improving the Selection of Vegetation Index Characteristic Wavelengths by Using the PROSPECT Model for Leaf Water Content Estimation
inferredcites →https://doi.org/10.3390/rs13091763openalex-snapshot
Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning
inferredcites →https://doi.org/10.3390/rs13193976openalex-snapshot
Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection
inferredcites →https://doi.org/10.3390/rs14194953openalex-snapshot
Using UAV to Identify the Optimal Vegetation Index for Yield Prediction of Oil Seed Rape (Brassica napus L.) at the Flowering Stage
inferredcites →https://doi.org/10.3390/rs14236171openalex-snapshot
Training Machine Learning Algorithms Using Remote Sensing and Topographic Indices for Corn Yield Prediction
inferredcites →https://doi.org/10.3390/rs15010079openalex-snapshot
Maize Yield Prediction with Machine Learning, Spectral Variables and Irrigation Management
inferredcites →https://doi.org/10.3390/rs16040683openalex-snapshot
Corn Grain Yield Prediction Using UAV-Based High Spatiotemporal Resolution Imagery, Machine Learning, and Spatial Cross-Validation
inferredcites →https://doi.org/10.3390/rs16061073openalex-snapshot
Evaluation of Leaf Chlorophyll Content from Acousto-Optic Hyperspectral Data: A Multi-Crop Study
inferredcites →https://doi.org/10.3390/rs2010290openalex-snapshot
Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.5194/egusphere-egu2020-138openalex-snapshot
Comparing a random forest based prediction of winter wheat yield to historical production potential
inferredcites →https://doi.org/10.5194/hess-25-2951-2021openalex-snapshot
Machine-learning methods for stream water temperature prediction
inferredcites →https://doi.org/10.5194/isprs-annals-v-3-2022-193-2022openalex-snapshot
AI-DRIVEN MAIZE YIELD FORECASTING USING UNMANNED AERIAL VEHICLE-BASED HYPERSPECTRAL AND LIDAR DATA FUSION
inferredcites →https://doi.org/10.7275/5fex-b874openalex-snapshot
Interpreting Multiple Linear Regression: A Guidebook of Variable Importance
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.5237252openalex-extracted
Agricultural Productivity: An AI-Based Crop Yield Prediction System Using Climate and Soil Data
inferred← citeshttps://doi.org/10.1007/978-981-96-5735-3_12openalex-snapshot
Adapting Soil and Nutrient Management to a Changing Climate: Emerging Innovations and Policy Priorities
inferred← citeshttps://doi.org/10.1007/978-981-96-5735-3_19openalex-snapshot
Sustainable Agriculture and Climate Policy: Integrating Modern Technologies for Crop Stress Mitigation
inferred← citeshttps://doi.org/10.1007/s00542-025-05947-5openalex-snapshot
Embedded microsystem for soil nutrient estimation using machine learning models
inferred← citeshttps://doi.org/10.1007/s10489-026-07306-xopenalex-snapshot
From black box to transparent farming: eXplainable AI in agriculture recommendation systems
inferred← citeshttps://doi.org/10.1007/s11119-026-10394-xopenalex-snapshot
Spatiotemporal cotton yield variations in relation to time-series satellite imagery: Implications for within-season crop management
inferred← citeshttps://doi.org/10.1007/s42729-026-03466-4openalex-snapshot
An Assessment of the Relationship Between 1000‑Kernel Weight Size and Ten Years of Historical 1000‑Kernel Weight Data from 26 Doubled Haploid Maize (Zea mays L.) Lines
inferred← citeshttps://doi.org/10.1016/j.agwat.2026.110626openalex-snapshot
Classification of paddy field water conditions with unmanned aerial vehicle: A novel method incorporating the short-wave infrared band
inferred← citeshttps://doi.org/10.1016/j.atech.2025.101630openalex-snapshot
Optimizing on-farm corn yield prediction by a multi-source data fusion approach using remote sensing and machine learning
inferred← citeshttps://doi.org/10.1016/j.atech.2026.102428openalex-snapshot
A hybrid process-based–machine learning–reinforcement learning framework for exploring adaptive corn planting policies under climate uncertainty
inferred← citeshttps://doi.org/10.1016/j.eja.2026.128086openalex-snapshot
From drone to satellite: Alfalfa fractional cover estimation with machine learning and sentinel-2
inferred← citeshttps://doi.org/10.1038/s41598-025-27174-8openalex-snapshot
Growth analysis of cotton using UAS derived multi temporal canopy features
inferred← citeshttps://doi.org/10.1109/iceca66444.2025.11383500openalex-snapshot
Advances in AI for Smart Farming: A Review of Hybrid and Explainable Models for Crop Recommendation and Yield Prediction
inferred← citeshttps://doi.org/10.1109/icesic67389.2026.11496433openalex-snapshot
Hybrid Machine Learning approach and Statistical Ensemble Model for Spatiotemporal Crop Yield Prediction
inferred← citeshttps://doi.org/10.1109/icosec67334.2025.11459618openalex-snapshot
Crop Yield Prediction using Hybrid Deep Learning and Geo-Spatial Data Integration
inferred← citeshttps://doi.org/10.1109/icscsa66339.2025.11171172openalex-snapshot
Intelligent Crop Monitoring: Machine Learning-based Classification of Plant Growth Stages for Precision Agriculture
inferred← citeshttps://doi.org/10.12677/sd.2025.159276openalex-snapshot
Building Resilience in Agricultural Supply Chains under the Rural Revitalization Strategy—Based on Digital and Intelligent Transformation Analysis
inferred← citeshttps://doi.org/10.1360/csb-2026-0224openalex-snapshot
Research advances in high-throughput maize phenotyping technologies and platforms
inferred← citeshttps://doi.org/10.15547/ast.2025.04.045openalex-snapshot
Precision management of soil fertility through zonal fertilization and nutrient balancing in Southeastern Bulgaria
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9348900/v1openalex-snapshot
CropCast: Context-aware AI framework for in-season crop growth forecasting
inferred← citeshttps://doi.org/10.2139/ssrn.6876396openalex-snapshot
In-season cotton yield forecasting using high resolution satellite imagery
inferred← citeshttps://doi.org/10.2478/ijssis-2026-0031openalex-snapshot
Development of a UAV-based crop disease detection system using deep learning algorithms to enhance precision agriculture
inferred← citeshttps://doi.org/10.3389/fsufs.2026.1775604openalex-snapshot
Prediction of maize yield on saline soils using Sentinel-2 remote sensing data in the Shaulder irrigated area, Kazakhstan
inferred← citeshttps://doi.org/10.3390/agriculture15070746openalex-snapshot
Estimation of Silage Maize Plant Moisture Content Based on UAV Multispectral Data and Ensemble Learning Methods
inferred← citeshttps://doi.org/10.3390/agriculture15222378openalex-snapshot
Integration of Machine Learning and Remote Sensing to Evaluate the Effects of Soil Salinity, Nitrate, and Moisture on Crop Yields and Economic Returns in the Semi-Arid Region of Ethiopia
inferred← citeshttps://doi.org/10.3390/agriengineering8020065openalex-snapshot
Varietal Identification and Yield Estimation in Potatoes Using UAV RGB Imagery in the Southern Highlands of Peru
inferred← citeshttps://doi.org/10.3390/agronomy15040793openalex-snapshot
Predicting Sugarcane Yield Through Temporal Analysis of Satellite Imagery During the Growth Phase
inferred← citeshttps://doi.org/10.3390/agronomy15092199openalex-snapshot
Maize Yield Prediction via Multi-Branch Feature Extraction and Cross-Attention Enhanced Multimodal Data Fusion
inferred← citeshttps://doi.org/10.3390/ai6030058openalex-snapshot
Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices?
inferred← citeshttps://doi.org/10.3390/app15137216openalex-snapshot
Phenology-Based Maize and Soybean Yield Potential Prediction Using Machine Learning and Sentinel-2 Imagery Time-Series
inferred← citeshttps://doi.org/10.3390/ijgi15060275openalex-snapshot
Multisource Satellite Data-Driven Machine Learning Approach for Rice Yield Prediction
inferred← citeshttps://doi.org/10.3390/rs18091419openalex-snapshot
Assessing GAN Super-Resolution in Grasslands: The Role of Spatial Heterogeneity and Textural Complexity
inferred← citeshttps://doi.org/10.52151/jae2025624.1979openalex-snapshot
High-Resolution Spectral Reflectance-based Crop Classification and Chlorophyll Content Estimation Using Machine Learning
inferred← citeshttps://doi.org/10.5937/ffr0-61215openalex-snapshot
Nutritional characterization and regional variability of corn grains for monogastric animal feeding in western Romania
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.