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Predicting Crop Shape-Related Features Using Agricultural Inputs

https://doi.org/10.2139/ssrn.4732363
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inferredcites →https://doi.org/10.1002/9780470316672openalex-extracted
Multiple Comparison Procedures
inferredcites →https://doi.org/10.1007/s42452-021-04148-9openalex-extracted
Comparison of feature importance measures as explanations for classification models
inferredcites →https://doi.org/10.1016/j.agrformet.2019.107886openalex-extracted
Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil
inferredcites →https://doi.org/10.1016/j.compag.2016.08.015openalex-extracted
The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modelling
inferredcites →https://doi.org/10.1016/j.compag.2020.105709openalex-extracted
Crop yield prediction using machine learning: A systematic literature review
inferredcites →https://doi.org/10.1016/j.compag.2021.106011openalex-extracted
Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery
inferredcites →https://doi.org/10.1016/j.molp.2021.03.010openalex-extracted
An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops
inferredcites →https://doi.org/10.1016/j.resconrec.2018.05.017openalex-extracted
Estimating on-farm food loss at the field level: A methodology and applied case study on a North Carolina farm
inferredcites →https://doi.org/10.1016/s0169-7161(04)24011-1openalex-extracted
Classification and Regression Trees, Bagging, and Boosting
inferredcites →https://doi.org/10.1016/s0925-5214(98)00088-xopenalex-extracted
Preharvest factors affecting appearance
inferredcites →https://doi.org/10.1016/s1161-0301(02)00107-7openalex-extracted
The DSSAT cropping system model
inferredcites →https://doi.org/10.1016/s1161-0301(02)00108-9openalex-extracted
An overview of APSIM, a model designed for farming systems simulation
inferredcites →https://doi.org/10.1038/s41598-021-97221-7openalex-extracted
An interaction regression model for crop yield prediction
inferredcites →https://doi.org/10.1038/s42003-023-04833-yopenalex-extracted
A data-driven crop model for maize yield prediction
inferredcites →https://doi.org/10.1080/00401706.1970.10488634openalex-extracted
Ridge Regression: Biased Estimation for Nonorthogonal Problems
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THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED
inferredcites →https://doi.org/10.1093/oso/9780198538493.001.0001openalex-extracted
Neural Networks for Pattern Recognition
inferredcites →https://doi.org/10.1109/5254.708428openalex-extracted
Support vector machines
inferredcites →https://doi.org/10.1111/j.2517-6161.1996.tb02080.xopenalex-extracted
Regression Shrinkage and Selection Via the Lasso
inferredcites →https://doi.org/10.1155/2014/509429openalex-extracted
Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction
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An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach
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Crop yield prediction integrating genotype and weather variables using deep learning
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Direct Measurement of Sweetpotato Surface Area and Volume Using a Low-cost 3D Scanner for Identification of Shape Features Related to Processing Product Recovery
inferredcites →https://doi.org/10.21273/horttech.19.1.133openalex-extracted
Using Growing Degree Days, Agrometeorological Variables, Linear Regression, and Data Mining Methods to Help Improve Prediction of Sweetpotato Harvest Date in Louisiana
inferredcites →https://doi.org/10.2166/hydro.2008.015openalex-extracted
Data-driven modelling: some past experiences and new approaches
inferredcites →https://doi.org/10.2307/1271436openalex-extracted
Ridge Regression: Biased Estimation for Nonorthogonal Problems
inferredcites →https://doi.org/10.3389/fpls.2019.00621openalex-extracted
Crop Yield Prediction Using Deep Neural Networks
inferredcites →https://doi.org/10.5424/sjar/2014122-4439openalex-extracted
Predictive ability of machine learning methods for massive crop yield prediction
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