Agronomy4future

When analyzing grain size, we’ve used high-throughput image scanning machines. However, if we can detect grains using R code, estimating grain size becomes possible. In my previous R function, colorcapture(), we were able to detect fruit size and estimate its surface area in 2D. Recently, I developed a new R function called phenokio() specifically designed to estimate the grain area of cereals. 1. Install the function Before installing, please download Rtools (https://cran.r-project.org/bin/windows/Rtools) If the function is not able to be…

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In my previous post, I explained how to calculate Cook’s Distance step by step, and noted that in R you can simply use the function cooks.distance(). However, this simple function only provides the Cook’s Distance values. In my previous post, I explained the formula for Cook’s Distance step by step, including how to compute residuals, leverage, and internal studentized residuals. To make it easier to calculate these values, I recently developed an R package called datacooks(). □ datacooks() Before installing…

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When analyzing yield, we usually use bar or box plots based on the mean. However, sometimes we need to visualize the spatial variation of yield. In those cases, GIS maps are commonly used, often created with powerful software such as ArcGIS, which I have used and found to be excellent. The drawback is that ArcGIS is not free. If you want to create a simple yield map using R, it is possible, although the map quality may be slightly lower….

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This colorcapture() R function provides easy image analysis to estimate fruit surface area. 1. Install the pacakge Before installing colorcapture(), please download Rtools (https://cran.r-project.org/bin/windows/Rtools), and install the following package. □ colorcapture() 2. Basic code If you want to change other criteria, type ?colorcapture to see detailed information about the colorcapture() function. The default code is set to capture the yellow color. If you run the code, it will detect the yellow surface area. The frame size was 24 cm × 24 cm. and the…

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When measuring leaf area or fruit surface area, these have usually been measured manually, which is time-consuming and often inaccurate, especially when the shape is not perpendicular. For more reliable measurement, image analysis provides a good alternative. To facilitate this process, I developed an R function, greencapture() that captures the green area of leaves or fruits in the lab using a fixed frame. 1. Install the greencapture() package 2. Set up folders for input and output images I will save…

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When analyzing data, we sometimes need to analyze cumulative data. When calculating cumulative data, grouping is important, and it takes time to perform the grouping and calculations. To simplify this process, I developed an R package called datacume(). Let’s upload the dataset. This dataset contains biomass measurements across different treatments over time, recorded for various plants and their branches. I want to calculate the cumulative biomass over time. To do this accurately, I’ll first calculate the average biomass of the…

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https://github.com/agronomy4future/r_code/blob/main/geom_mark_ellipse.ipynb We aim to develop open-source code for agronomy ([email protected]) © 2022 – 2025 https://agronomy4future.com – All Rights Reserved. Last Updated: 19/07/2025

This post is part of a series introducing R packages that present methods for calculating phenotypic plasticity. □ [R package] Finlay-Wilkinson Regression model (feat. fwrmodel)□ [R package] Calculate the responsiveness of each treatment relative to a control (Feat. deltactrl)□ [R package] Quantifying Reaction Norm Plasticity from Slopes to Individual Responses (Feat. nrmodel) In my previous post, I explained how to quantify phenotypic plasticity in crops and described three different approaches: Responsiveness, Reaction Norm, and the Finlay-Wilkinson Regression Model. □ Quantifying…

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■ [R package] Embedding Key Descriptive Statistics within Original Data (Feat. descriptivestat) ■ [R package] Calculate the responsiveness of each treatment relative to a control (Feat. deltactrl) In my previous posts, I introduced two R packages. The first package, descriptivestat(), displays raw data along with mean values and additional descriptive statistics. The second package, deltactrl(), calculates the responsiveness of dependent variables in response to the control. Today, in this article, I will demonstrate how combining these two R packages allows…

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■ Quantifying Phenotypic Plasticity of Crops In my previous posts, I explained how to quantify phenotypic plasticity of crops in response to environmental factors, and introduced the concept of responsiveness, calculated using the formula: (Treatment – Control) / Control, as shown in the table below. Genotype Control Treatment Responsiveness A 100 90 -10.0% = (90-100)/100 B 120 70 -41.7% C 115 90 -21.7% D 95 85 -10.5% E 110 105 -4.5% While calculating responsiveness for a single variable is relatively…

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When analyzing data, we often need to examine descriptive statistics such as standard deviation, standard error, or the coefficient of variation (CV). These are typically summarized in a short table, but in some cases, it may be necessary to include such statistics directly in the original dataset for further analysis. Let’s upload a dataset to begin. I would like to calculate the mean, variance, standard deviation, standard error, 95% confidence interval, coefficient of variation, and the 25th percentile of yield…

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When uploading data to R, we sometimes worry about losing track of the data over time. This is because we save data in different folders according to various projects, and we might forget where we stored it. Additionally, if the file path changes, it can be difficult to upload the data directly and locate its current location. Therefore, a better approach is to save the data as code, allowing us to access it directly when opening the R file where…

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In my previous post, I explained how to calculate interpolation to predict in-between data points. ■ [Data article] Predicting Intermediate Data Points with Linear Interpolation in Excel and R To make interpolation calculations easier, particularly for groups, I recently developed a new R package, interpolate(). First, let’s upload a dataset. This dataset contains chlorophyll content measurements for sorghum and soybean. I measured chlorophyll content every 10 days between 65 and 125 days after sowing, with four replicates at each time…

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■ [Data article] Data Normalization Techniques: Excel and R as the Initial Steps in Machine Learning In my previous post, I explained how to normalize data using various methods and demonstrated how to perform the calculations for each method. To simplify these calculations, I recently developed an R package that easily generates normalized data. 1. Install the normtools() package 2. Basic code format 3. Practice with actual dataset (data upload) 4. Normalize data 4.1. Z-test normalization 4.2. Robust Scaling 4.3….

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■ Python GIS: Interpolating and Plotting Corn Grain Yield Data In my previous post, I explained how to create a GIS map using Python. Today, I’ll introduce how to create the same GIS map using R. First, let’s install all the required packages. and I’ll upload a dataset for practice. Next, I’ll extract columns for latitude, longitude, and y (output) and I’ll interpolate data Finally, I’ll create a GIS map using ggplot(). Full code If you copy and paste the…

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I want to create a normal distribution graph with a specific variance. First, it’s necessary to create the data. I’ll generate data with a mean of 100 and a variance of 100 (which means the standard deviation is 10). However, it’s important to establish a range. To do this, I’ll set up a range of 6σ, and the dataset will contain 1,000 rows. and I’ll create a normal distribution graph. These are graphs with different variances, ranging from 1σ to…

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