🌾 100 Applications of R in Agriculture (with Examples)


🧪 Experimental Design & Analysis

  1. Randomized Block Design Analysis – Testing fertilizer effects on maize yield.

  2. Split-Plot Design Analysis – Irrigation method × crop variety.

  3. Completely Randomized Design (CRD) – Effect of light exposure on germination.

  4. Latin Square Design – Managing soil variability in rice trials.

  5. Factorial Experiments – Interaction between pesticide and watering level.

  6. ANOVA (Analysis of Variance) – Comparing wheat yield across treatments.

  7. Post Hoc Tests (Tukey HSD) – Finding which fertilizer treatments differ.

  8. Regression Analysis – Predicting yield from rainfall.

  9. Linear Mixed Models – Analyzing repeated measures in plant height.

  10. Power Analysis – Determining sample size for field experiments.

🌿 Crop Science and Plant Breeding

  1. Yield Analysis – Comparing rice varieties for grain yield.

  2. Trait Correlation Analysis – Relationship between height and yield.

  3. PCA on Crop Traits – Dimensionality reduction in maize traits.

  4. QTL Mapping – Locating yield-related loci in wheat.

  5. Genome-Wide Association Studies (GWAS) – SNPs linked to drought resistance.

  6. GGE Biplot Analysis – Genotype × Environment interaction in field trials.

  7. Stability Analysis – Evaluating variety stability across environments.

  8. Growth Curve Modeling – Logistic model for crop biomass.

  9. Crop Stage Phenotyping – Tracking flowering, fruiting, and maturity stages.

  10. Heatmap of Trait Expression – Expression levels under drought stress.

🧬 Genetics and Genomics

  1. RNA-seq Data Analysis – Differential gene expression in stressed plants.

  2. DNA Sequence Visualization – Plotting gene structure.

  3. SNP Calling and Filtering – VCF data processing for population genetics.

  4. Gene Ontology (GO) Analysis – Classifying gene functions in legumes.

  5. KEGG Pathway Mapping – Nitrogen fixation pathways.

  6. Marker-Trait Association – Association mapping in sugarcane.

  7. Co-expression Network Analysis – WGCNA on expression data.

  8. Genomic Selection Models – Predicting yield using genotypes.

  9. Heritability Estimation – Broad-sense heritability of grain weight.

  10. Genetic Diversity Analysis – Phylogenetic tree of local cultivars.

🌍 Soil Science

  1. Soil Nutrient Mapping – Interpolating NPK across farm fields.

  2. Soil pH Analysis – Comparing treatments across soil types.

  3. Soil Texture Classification – Analyzing clay, silt, sand proportions.

  4. Soil Moisture Modeling – Predicting soil moisture with weather data.

  5. Organic Matter Estimation – Tracking composting effects.

  6. Geostatistical Analysis – Kriging soil nutrients in orchards.

  7. Time-Series Soil Data Analysis – Monitoring salinity changes over years.

  8. Soil Carbon Sequestration Modeling – Estimating carbon in cover crops.

  9. Soil Respiration Studies – CO₂ emissions from fertilized plots.

  10. Soil Microbial Diversity Analysis – Using amplicon sequencing data.

🛰️ Remote Sensing and Precision Agriculture

  1. NDVI Calculation from Drones – Monitoring crop health in real time.

  2. Satellite Imagery Classification – Land use categorization.

  3. Time-Series Analysis of MODIS Data – Crop phenology tracking.

  4. Yield Prediction from Imagery – Using vegetation indices.

  5. Canopy Cover Estimation – From aerial RGB images.

  6. Raster Data Analysis – Soil moisture from remote sensors.

  7. GPS Mapping of Farm Plots – Spatial field layout.

  8. Image Segmentation for Crop Rows – Detecting plant rows from above.

  9. UAV-based Disease Detection – Leaf spot identification in wheat.

  10. VRT (Variable Rate Technology) Mapping – Creating fertilizer application maps.

📈 Agricultural Economics

  1. Cost-Benefit Analysis – Comparing organic vs. conventional farming.

  2. Market Price Trend Analysis – Time-series of maize prices.

  3. Farm Budgeting Models – Planning for cash crops.

  4. Profitability Index Calculation – Return on investment for wheat.

  5. Input-Output Modeling – Economic modeling of farm inputs.

  6. Risk Analysis with Monte Carlo – Weather risk for peanut yield.

  7. Price Forecasting with ARIMA – Rice price trends in markets.

  8. Consumer Preference Modeling – Survey analysis for organic products.

  9. Livelihood Analysis – Income sources of smallholder farmers.

  10. Subsidy Impact Evaluation – Fertilizer subsidy on yield.

🧠 Machine Learning in Agriculture

  1. Random Forest for Yield Prediction – Based on soil and weather data.

  2. SVM for Disease Classification – Leaf blight detection.

  3. Decision Trees for Crop Recommendations – Based on climate and soil.

  4. K-Means Clustering of Farms – Grouping by soil properties.

  5. ANN for Weather-Based Forecasting – Rainfall prediction for planting.

  6. XGBoost for Yield Estimation – With multiple traits.

  7. Time-Series Forecasting with LSTM – Temperature trends.

  8. Outlier Detection in Field Data – Cleaning experimental data.

  9. Deep Learning on Image Data – Identifying nutrient deficiencies.

  10. Crop Recommendation System – Based on previous yield and input data.

☁️ Climate and Weather Data Analysis

  1. Rainfall Trend Analysis – Long-term weather records.

  2. Drought Index Calculation – SPI or PDSI for agricultural planning.

  3. Growing Degree Days (GDD) – Crop development modeling.

  4. Evapotranspiration Estimation – Penman-Monteith method.

  5. Climate Data Visualization – Monthly mean temperatures.

  6. Agroclimatic Zoning – Mapping zones based on rainfall.

  7. Extreme Weather Event Detection – Heatwaves during flowering.

  8. Weather Forecasting Models – Rainfall prediction for farmers.

  9. Seasonal Climate Variability Study – Rain-fed agriculture risks.

  10. Climate Change Impact Modeling – On crop productivity.

🧾 Data Handling, Reporting & Visualization

  1. ggplot2 for Plotting Field Data – Yield bar plots.

  2. Plotly for Interactive Dashboards – Fertilizer response curves.

  3. Shiny Apps for Farmer Portals – Interactive input recommendations.

  4. RMarkdown for Automated Reports – Summarizing agronomic trials.

  5. Data Wrangling with dplyr/tidyr – Reshaping trait datasets.

  6. Data Cleaning Scripts – Removing errors in weather logs.

  7. Generating Summary Tables – Means, SD, CV for agronomic traits.

  8. Combining Datasets from Multiple Farms – Merging by GPS or IDs.

  9. High-Resolution Map Generation – Crop health maps.

  10. Creating Extension Reports – PDF reports for outreach.

🧫 Post-Harvest and Food Technology

  1. Shelf-Life Modeling – Storage effect on tomato firmness.

  2. Quality Trait Analysis – Sugar content in fruits.

  3. Moisture Content Regression Models – Drying behavior of grains.

  4. Post-Harvest Loss Estimation – Weighing before and after storage.

  5. Storage Temperature Effect Analysis – On vegetable spoilage rate.

  6. Sensory Evaluation Statistics – Consumer preferences on taste.

  7. Nutritional Value Visualization – Nutrient content in legume varieties.

  8. Spoilage Prediction Models – Mold growth under humidity.

  9. Sorting Efficiency Analysis – Machine performance on fruit size sorting.

  10. Grading System Automation – Image-based size classification.


Krishicode

Posted by Krishicode

Post a Comment

0 Comments

Close Menu