This is the presentation that I gave on 4th September at AASC 2024 Rottnest Island.

Case Studies in Advanced Analysis of Large Strip On-farm Experiments

Abstract: On-farm experiments (OFE) are gaining attention among farmers and agronomists for testing various research questions on real farms. The Analytics for the Australian Grains Industry (AAGI) has developed several techniques for analysing OFE data. Geographically Weighted Regression (GWR) and the multi-environment trial (MET) technique, which partitions paddocks into pseudo-environments (PEs), have proven effective. Additionally, we have explored the potential of the Generalised Additive Model (GAM) for handling temporal and spatial variability, given its flexibility in accommodating non-linear variables. In this presentation, we will demonstrate case studies using these techniques to analyse OFE data and compare the outcomes of different approaches.

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This is the 5-minute presentation that I gave to GRDC western panel and growers at Pawsey Centre..

Analytics Innovations in On-farm Experiments

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In the realm of precision farming, the application of Machine Learning (ML) and Deep Learning (DL) algorithms has proven to be invaluable for analyzing and modeling agricultural data that varies across both space and time. While most research has heavily leaned toward utilizing remote sensing data, particularly in image recognition, the advancements in deep learning methodologies for regression problems have been comparatively slow.

Regression techniques are crucial for large-scale agricultural trials, especially when the goal is to identify optimal management practices for specific locations within a field. To enhance predictive accuracy, it’s essential to incorporate various factors—topographical, environmental, and ground conditions—into the modeling process.

To address this gap, our recent work introduces an innovative framework that leverages spatio-temporal estimation and prediction of agricultural data through deep learning and Gaussian processes. By integrating a deep Gaussian Process component, we explicitly model the spatio-temporal structure of the data, while utilizing standard deep learning techniques to account for other influential factors.

We applied our framework to two real-world datasets, demonstrating its effectiveness and accuracy in yield prediction. This approach not only enhances our understanding of agricultural dynamics but also paves the way for more informed decision-making in precision farming.

Stay tuned for further insights as we delve deeper into the applications and implications of this research!

It’s a workshop provided to introduce participants to RShiny, a powerful web application framework for R. During this workshop, attendees will learn how to create interactive web applications that can visualize data and facilitate user input.

Workshop Overview

The workshop will cover the following key topics:

• Introduction to R and Shiny: Understanding the basics of R and how it integrates with Shiny.
• Creating a Simple Shiny App: Step-by-step guidance on building your first Shiny application.
• UI and Server Components: Exploring the user interface (UI) and server functions that make up a Shiny app.
• Reactive Programming: Learning how to make applications responsive to user input.
• Data Visualization: Using libraries like ggplot2 to create dynamic plots within Shiny apps.
• Deployment: Overview of how to deploy Shiny apps to shinyapps.io or a local server.

Target Audience

This workshop is aimed at R users who want to enhance their data analysis skills by creating interactive web applications. No prior experience with Shiny is required, but a basic understanding of R programming is recommended.

Objectives

By the end of the workshop, participants will be able to:

• Develop a basic Shiny application.
• Understand the key components of Shiny apps.
• Implement interactive features to enhance user experience.
• Deploy their applications for public access.

Resources

Participants will be provided with:

• Workshop materials, including example code and data.
• Access to recorded sessions for future reference.
• Support for any follow-up questions after the workshop.

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Check out the embedded Quarto HTML file below:

There is no doubt on the importance of randomisation in agricultural experiments by agronomists and biometricians. Even when agronomists extend the experimentation from small trials to large on-farm trials, randomised designs predominate over systematic designs at most times.

This is the joint presentation by me and Suman at SAGI Symposium 2020.