Posts

Background Colorimetry is a fascinating topic to discuss. In conjunction with the patterns of a natural world (See this awesome video about fibonacci numbers and plants), colors could have mesmerizing feels. In this post and the follow-up article, we will discuss in details about colorimetric features of a universe made of plants, in particular, which are cultivated/adopted and have edible human values – the agricultural crops.

Introduction There are a great deal of influencers along the chain of generating finished seed material from a produce offered by farmers. As we discussed earlier in this post, the process takes on a fairly complex pathway while certification and processing requirements are being met.

Context SMR of Cereal crops SMR of vegetable crops Table 1: Seed multiplication ratio of common vegetable crops SN Crop Seed rate (per ha) Seed yield Seed Multiplication Ratio 1 Broad leaf mustard 600 600 1000.

The problem Seed business is a multifaceted undertake. Although most businesses suffer strong feedback effects, seed business are more markedly left with those than most others. Let us refer to a simple case description to recapitulate just how pronounced it can be, and we are talking about the success in a long term venture.

Introduction Agromorphology constitutes what’s observable and that which is economic. Because agriculture has important connection to economy, this connection is at best rung everywhere though what agriculture reveres and, and when talked modestly, relies on: Crops. Unsurprisingly, finer details that agriculture touches upon to make ends met (processes and resources involved along the Production-Consumption chain) are convoluted.

Background This post is all about examples and use cases. So…Let’s break a leg. Extract all words except last one using anchors and look arounds nasty_char <- c("I love playing wildly") # remove the last word 'wildly' stringr::str_extract(nasty_char, ".

Example case Suppose you have a bunch of really filthy names, which makes you puke… You can go about fixing those with the help of stringi and stringr Lets say following character vector hosts those filthy names. filthy <- c("Grains %", "Moisture (gm/kg)", "Plant height (cm)", "White spaces", "White space (filth%)") filthy ## [1] "Grains %" "Moisture (gm/kg)" "Plant height (cm)" ## [4] "White spaces" "White space (filth%)" Now to get rid of the filth use string manipulation.

Introduction $\LaTeX$ is a Turing complete langauge besides being one of the popular typesetting systems in action since a very long ago. $\LaTeX$ workflows are fully reproducible and extremely customizable, hence its popularity over WYSIWYG softwares. This my first in a series of lessons about easing the workflow with $\LaTeX$.

Background When one has a large text file to sift through, windows command line executables might come to rescue. To illustrate usefulness of some of these tools. I have build herein an example case where you require finding a line with search certain search pattern.

Introduction Variance component models are also suited for analysis of incomplete block designs, besides complete block designs. This post aims to demonstrate exactly that. Using a dataset generated from alpha lattice design, I show how the design can be properly modeled and fit using OLS regression having various fixed model components.