For Plant Bioinformatics coding which platform R Studio or Python is much suitable?

For plant bioinformatics, neither R nor Python completely replaces the other 

They are strongest in different parts of the workflow.

Area	R / RStudio	Python
Statistical analysis	⭐⭐⭐⭐⭐	⭐⭐⭐⭐
Plant breeding statistics	⭐⭐⭐⭐⭐	⭐⭐⭐
GWAS analysis	⭐⭐⭐⭐⭐	⭐⭐⭐⭐
Genomic data analysis	⭐⭐⭐⭐	⭐⭐⭐⭐⭐
RNA-seq / transcriptomics	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Bioinformatics pipelines	⭐⭐⭐	⭐⭐⭐⭐⭐
Machine learning	⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Deep learning	⭐⭐⭐	⭐⭐⭐⭐⭐
Image-based phenotyping	⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Computer vision	⭐⭐⭐	⭐⭐⭐⭐⭐
Data visualization	⭐⭐⭐⭐⭐	⭐⭐⭐⭐
PCA, clustering, correlations	⭐⭐⭐⭐⭐	⭐⭐⭐⭐
Genomic prediction	⭐⭐⭐⭐⭐	⭐⭐⭐⭐
Automation	⭐⭐⭐	⭐⭐⭐⭐⭐
Reproducible research	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Beginner-friendly for breeders	⭐⭐⭐⭐⭐	⭐⭐⭐⭐

For your plant-breeding background

Because your work involves morphological traits, digital image phenotyping, molecular markers, PCA, clustering, correlation/path analysis, heritability, GCA/SCA, hybrid evaluation and MGIDI, I would recommend:

R/RStudio = your primary platform
Python = your complementary platform

A practical combination would be:

RStudio
→ experimental design
→ ANOVA
→ GCA/SCA
→ heterosis
→ heritability/genetic advance
→ correlation/path analysis
→ PCA
→ clustering
→ AMMI/GGE stability
→ MGIDI
→ GWAS/statistical genetics
→ publication-quality figures

Python
→ image processing
→ computer vision
→ automated phenotyping
→ machine learning/deep learning
→ large genomic datasets
→ sequence processing
→ automated bioinformatics pipelines
→ AI-assisted breeding

If your goal is modern plant bioinformatics

I'd learn them in this order:

1. R/RStudio — advanced level
2. Python — intermediate → advanced
3. Linux/Bash — essential for serious bioinformatics
4. Git/GitHub — reproducibility and collaboration

The important point is that RStudio is not really a competitor to Python. R is a programming language, while RStudio is an IDE; similarly, Python can be used through VS Code, Jupyter, PyCharm, etc. Phenotyping in plants

My recommendation for you

Given your existing plant-breeding experience, don't abandon R. Instead, build a R + Python + Linux skill set.

A particularly valuable specialization for you would be:

Plant Breeding → Digital Phenotyping → Computer Vision → Genomics → Machine Learning → Genomic Selection

That combination would connect your existing breeding/statistical experience with the newer bioinformatics and AI side of crop improvement. Bioinformatics in plants