Agronomy4future
  • CURRICULUM VITAE
  • BASIC STAT
  • STATISTICAL MODEL
    • ANOVA
    • REGRESSION
    • SPLIT-PLOT
    • GLM
    • Mixed Model
    • REML
    • NONLINEAR
  • CODING
    • R coding
    • SAS
    • PYTHON
    • Machine Learning
  • DATABASE
    • Structured query language (SQL)
    • EXCEL/VBA
    • Power Query
    • Data Science

Agronomy4future

Stories about cereals and statistics (plus coding). We aim to develop open-source code for agronomy.

PYTHON

PYTHON

■ Setup

  • How to use Google Colab for Python (power tool to analyze data)?
  • How to Specify a Folder Path in Google Colab: A Tutorial

■ Data upload

  • How to Upload Data from GitHub Using R and Python?
  • How to import Kaggle datasets directly into Google Colab?

■ Data frame

  • How to create a data table in Python?
  • How to convert to a .json file using Python?

■ Data preprocessing

  • Python Data Preprocessing: Practice

■ Data summarization

  • How to summarize data using Python?

■ Graph

Line/Bar graph

Histogram graph

Normal distribution graph

  • How to draw a normal distribution graph using Python?

GIS

  • Visualizing Geospatial Data with Folium in Python

■ Statistical Analysis

  • A Practical Guide to Data Normalization using Z-Tests in Python

■ Modeling

  • [Data article] Simulating Crop Growth Over Time Using a Sigmoid Growth Model

JK Kim 
([email protected])
"We aim to develop open-source code for agronomy"

• GitHub: github.com/agronomy4future
• YouTube:
@agronomy4future

annotate() aov () arrange() c(rep() case_when() colnames() data.frame() ddply () dplyr facet_grid() facet_wrap() filter() fwrmodel() gdds() geom_abline () geom_bar () geom_vline() github HSD.test() if() ifelse() intercept0() kimindex() mutate() nls() nlsLM() normtools() pivot_longer() poly() predict() probdistz() rbind() reshape2::melt() residuals() sapply() scale_fill_manual () spread() str_replace_all () subset() table() tdist() theme_grey() windows () write_xlsx() xtabs()

The Gamma distribution is ideal for modeling strictly positive, asymmetric data unlike the Normal distribution. In this post, I demonstrate how to calculate the Gamma PDF manually and in Excel, and introduce gammacurve(), an R package.

■ Full article: https://t.co/ApzxrAvZ4t pic.twitter.com/RPbS6bfLGz

— J.K Kim (@agronomy4future) November 5, 2025

[R package] colorcapture(): Segment and measure colored objects in images.

By simply switching the HSV segmentation, different colors of fruits (or grains) can be detected by R, and its surface area automatically calculated.

□ Code explained: https://t.co/HR8ga1xa1Z pic.twitter.com/QNdhDu5MTq

— J.K Kim (@agronomy4future) October 26, 2025

Local SQL servers limit data access. Cloud-based SQL offers better efficiency. I’ve simplified importing data to Cloud-based SQL with Python via Command Prompt. For big data, Cloud-based SQL servers are key.

* Full article: https://t.co/GSOscCcqY7 pic.twitter.com/5XMdHLgm8J

— J.K Kim (@agronomy4future) October 5, 2025

[R package] greencapture(): Segment and Measure Green Objects in Images. This function provides tools for capturing, and quantifying green plant tissue area from digital images. Designed for high-throughput in vitro or greenhouse image analysis (https://t.co/nVpyRZTAgh) pic.twitter.com/asilupDIut

— J.K Kim (@agronomy4future) September 29, 2025

[R package] datacume()
• Compute Cumulative Summaries of Grouped Data

Just released a new R function. This function helps you easily calculate cumulative values over time (or trials), grouped by category (also supports averaging).

□ code explained: https://t.co/7Az1kmXsEl pic.twitter.com/Vdjt41xwAD

— J.K Kim (@agronomy4future) September 7, 2025

Just released a new R function: #datacooks(). It adds diagnostic stats (residuals, leverage, studentized residuals, Cook’s distance, etc.) directly to your model dataset — then automatically spots and flags potential outliers.

□ code explained: https://t.co/nbwk2dwZtY pic.twitter.com/ZInoHebVAi

— J.K Kim (@agronomy4future) August 17, 2025

[R package] #rnmodel()
The concept behind this code is that plasticity can be quantified by estimating slopes for individual genotypes across environments. It offers an easy way to obtain these slopes for each specific environment.

□ Code explained: https://t.co/2KsHyADjWO pic.twitter.com/RDrPnsJXEF

— J.K Kim (@agronomy4future) June 23, 2025

Previously, I developed two R packages, descriptivestat() and deltactrl(). By combining these packages, it becomes easy to create a responsiveness graph.

[Data article] Visualizing Responsiveness: Integrating Raw Data for a Holistic Dataset View (https://t.co/Yz3I6bbMsF) pic.twitter.com/eBesZBJUY9

— J.K Kim (@agronomy4future) June 9, 2025

□ New R package: #deltactrl(); delta control

This package is designed to easily calculate the responsiveness of each treatment relative to a control.

□ Github: https://t.co/BYCSf7KU9v
□ Code explained: https://t.co/GlXvnhh4mV pic.twitter.com/iIhWHLDAAH

— J.K Kim (@agronomy4future) June 9, 2025

We’re working on measuring crop canopy with image analysis and comparing it to actual leaf area. If the model fits well, this could be a fast way to estimate canopy size. Currently setting up the frame and code. pic.twitter.com/GE33yujdKi

— J.K Kim (@agronomy4future) May 21, 2025

New R package: #descriptivestat()
This package automatically embeds descriptive statistics into the dataset, allowing for clear visualization alongside the raw data.

□ Github: https://t.co/tYJPmzvCzJ
□ Code explained: https://t.co/sPOqQwIDgu pic.twitter.com/EL330dIH21

— J.K Kim (@agronomy4future) May 18, 2025

When the intercept is forced to 0 in a simple linear regression, most software programs report an incorrect R². Therefore, I developed a new R package, #intercept0(), which provides the correct R².

□ Github: https://t.co/In3GS6szFI
□ Code explained: https://t.co/EAL3Rbeo6U pic.twitter.com/LJ2eoUlLeT

— J.K Kim (@agronomy4future) May 11, 2025

This is a simple crop growth simulation code based on the Sigmoid Growth Model. If you assume a certain crop growth curve (e.g., biomass or canopy size) over time, you can set up this simulation curve and track it over time. I am sharing the Python code (https://t.co/RiBpXzpXkP) pic.twitter.com/YatW9RcikP

— J.K Kim (@agronomy4future) March 21, 2025
https://twitter.com/agronomy4future/status/1898040162229403743

I developed an R package, #interpolate() to facilitate data interpolation, particularly by grouping. With this R package, you can easily predict intermediate data points based on actual data points.
□ Github: https://t.co/ClJuZPAMfH
□ Code explained: https://t.co/RRoaphRSi8 pic.twitter.com/u6SVdY1tBO

— J.K Kim (@agronomy4future) March 3, 2025
https://twitter.com/agronomy4future/status/1878839760493658397
https://twitter.com/agronomy4future/status/1858762408925557136

The Log-Likelihood is a crucial component in statistical modeling, as it helps evaluate how well a model fits the data. In this article, I will explain how to calculate the Log-Likelihood by hand. There is an exercise you can try with an actual dataset (https://t.co/Q1b7693UUB) pic.twitter.com/YrV2SxnzzE

— J.K Kim (@agronomy4future) November 8, 2024

[STAT Article] Step-by-Step Guide to Calculating and Analyzing Principal Component Analysis (PCA) by Hand (https://t.co/IB1kXGYGiJ)

In this article, I introduce how to calculate and analyze Principal Component Analysis (PCA) by hand step by step. pic.twitter.com/HmjHOvOtaB

— J.K Kim (@agronomy4future) November 1, 2024

□ R package: normtools() for Normalization Methods for Data Scaling.

Recently, I developed an R package to normalize data using various methods (Z-test, Robust Scaling, Min-Max Scaling, and Log Transformation) for data scaling.

□ Code explained: https://t.co/EE8fatSqks pic.twitter.com/0u99SrvPLn

— J.K Kim (@agronomy4future) October 8, 2024

I've developed an R package, #gdds(), to easily calculate Growing Degree Days (#GDDs) with a base temperature (BT).

□ Cumulative temp when BT is 0
GDDs = gdds(df, "date", "temp", "group", date= c("0000-00-00", "0000-00-00"), BT= 0)

□ Github: https://t.co/p4oUI3XEnM pic.twitter.com/psKtHWZNiR

— J.K Kim (@agronomy4future) September 15, 2024

#kimindex() is a simple R package I developed to predict grain weight from area based on ŷ=x^1.32

# basis code
□ predicted_gw=kimindex(df, "grain_area", remove_na= TRUE)
□ predicted_area=kimindex1(df, "grain_weight", remove_na= TRUE)

□ Code explained: https://t.co/ISaQIGuQTr pic.twitter.com/ERguU3fee7

— J.K Kim (@agronomy4future) September 10, 2024
https://twitter.com/agronomy4future/status/1830491078920970649

Stepwise regression is a method that iteratively constructs a regression model by selecting independent variables to include in the final model, and I summarized the basic concept.
#
Stepwise Regression: A Practical Approach for Model Selection using R (https://t.co/YeLnex72ng) pic.twitter.com/HFOIukVzhk

— J.K Kim (@agronomy4future) May 8, 2024

Data normalization improves scale uniformity and enhances visualization interpretability. I introduce several methods for data normalization.

Data Normalization Techniques: Excel and R as the Initial Steps in Machine Learning (https://t.co/5yUE2fjHfn) pic.twitter.com/7x6mU2ccgf

— J.K Kim (@agronomy4future) April 29, 2024

#Agrivoltaic conference in June 2024. In shaded environments, various studies on crop physiology are available without the need for additional shading installations. Agrivoltaics also makes it an energy-efficient approach in crop science. I love this concept. pic.twitter.com/OiDtyKUxAC

— J.K Kim (@agronomy4future) April 17, 2024

Solar Farm 2.0 #SCAPES Crops Research @ University of Illinois at Urbana-Champaign.
Last season, I altered the sources-sink strength of sorghum and soybean and collected valuable data. This upcoming season, I will analyze the genotypic variance of soybeans in response to shading. pic.twitter.com/76EqBF8kuB

— J.K Kim (@agronomy4future) April 9, 2024

Solar Farm 2.0 Crops Research at UIUC. This is one of my researches I've been involved, and my main interest lies in understanding how shading affects the main yield components and determining the best strategy for managing crop growth under solar panels (https://t.co/ztp25lHCky)

— J.K Kim (@agronomy4future) October 28, 2023

Solar Farm 2.0 SCAPES Crops Research @ University of Illinois Urbana-Champaign. https://t.co/fIqsmoMJYu

— J.K Kim (@agronomy4future) September 12, 2023

Source-sink manipulation was performed on sorghum by removing alternate leaves throughout the entire canopy and half of the head 10 days after flowering, as referenced in Gambín and Borrás (2007). How does sorghum grain weight respond to the change in assimilates? pic.twitter.com/omR7oDQTO8

— J.K Kim (@agronomy4future) August 9, 2023
  • Agricultural Institute
  • AI
  • Article
  • Canada
  • Cereals
  • Conference
  • Crop breeding
  • Crop physiology
  • Data Science
  • España
  • Excel
  • Fertilizer
  • GHGs
  • Google Colab
  • hemp (Cannabis sativa)
  • JK column
  • JMP
  • Machine learning
  • Maize
  • Nederland
  • Principle-Centered Leadership
  • Python programming
  • R programming
  • SAS
  • Soil
  • Sorghum
  • source-sink dynamics
  • Soybean
  • SQL
  • Statistics
  • Tool
  • Uncategorized
  • United States
  • VBA
  • Wheat
  • What is the Gamma Distribution? Shape and Scale Parameters, and the Probability Density Function (PDF)
  • guides(fill=”none”)
  • How to Set Up RStudio Server on a Linux-Based Virtual Private Server (VPS)
  • [Data article] How to Import Data from MySQL Server to R?
  • [R package] Segment and Measure Colored Objects in Images (Feat. colorcapture)
  • [Data article] How to Import Data to a Cloud MySQL Server (DigitalOcean) Using Python from Command Prompt
  • [R package] Segment and Measure Green Objects in Images (Feat. greencapture)
  • [R package] Compute Cumulative Summaries of Grouped Data (Feat. datacume)
  • [R package] Cook’s Distance Diagnostics and Outlier Detection (Feat. datacooks)
  • [R package] Streamlined Mixed-Effects Analysis for Agrivoltaics Experiments (Feat. agrivoltaics)
  • geom_mark_ellipse
  • [STAT Article] Statistical Models in Agrivoltaics: Linear Mixed Models Across Different Field Layouts
  • [R package] Quantifying Reaction Norm Plasticity from Slopes to Individual Responses (Feat. nrmodel)
  • [STAT Article] How to calculate reaction norm in crop physiology?
  • [Data article] Visualizing Responsiveness: Integrating Raw Data for a Holistic Dataset View
  • [R package] Calculate the responsiveness of each treatment relative to a control (Feat. deltactrl)
  • Amblyseius swirskii – Predatory Mite
  • The New York Night scene from the Airplane
  • pepper flowering initiated
  • [R package] Embedding Key Descriptive Statistics within Original Data (Feat. descriptivestat)
  • How to upload data from Google Drive to Google Colab in an R environment?
  • [R package] R-Squared Calculation in Simple Linear Regression with Zero Intercept (Feat. Intercept0)
  • How to download data from R environment in Google Colab?
  • Urea (46-0-0) application at the agrivoltaics field in 2024 and 2025
  • How to Upload Data from GitHub Using R and Python?
  • [Data article] Simulating Crop Growth Over Time Using a Sigmoid Growth Model
  • [STAT Article] RMSE Calculation with Excel and R: A Comprehensive Guide
  • What is split-plot design in agronomy research?
  • [R Package] Convert Data into Code Instantly – Save as a Script with One Line
  • [R package] An easy way to use interpolation code to predict in-between data points



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