High-throughput phenotyping offers a plethora of advantages over a manual approach when it comes to seed production techniques. And, when it comes to accuracy and efficiency at scale, using the right precision agriculture tools makes all the difference.
Here are a few of the ways that incorporating the right technology, such as software that allows you precisely track and analyze performance, into your seed breeding can elevate your plant breeding program.
Relying on technology solutions to support high-throughput phenotyping uses advanced machine learning and automated analyses to remove the limitations you’d face when breeding for more traits if you relied solely on manual methods to collect and analyze seed breeding data.
From tracing spatial uniformity statistics for existing traits, like stand count, to enabling new traits, such as multispectral crop health, the agriscience technology available today makes it possible to track it all, keep tabs on performance at every stage of the growth cycle, and, ultimately, determine whether you’ll be successful in expressing the traits you’re expecting.
Your team’s time is highly valuable, and there’s no sense having them spend hours manually collecting data that could be done more quickly – and with greater accuracy. By relying on sensors and data processing software designed for seed breeding, you can easily take measurements as frequently as you’d like, which can provide you with greater insight into performance at each stage.
Faster Time to Analysis
You can forget about spending hours recording, entering, and analyzing your data. With tech like aerial imagery, image-based workflows can be processed in near real-time – and that means that you can begin analyzing performance right away.
Every moment matters, after all, so there’s no need to spend time in the field (or the lab) gathering and evaluating data when it can be done with such speed and accuracy using drones, satellites, and data processing software.
Now, when it comes to data and technology in general, it’s important to keep in mind that more is not necessarily better, and we know there’s all kinds of software out there that can be used for various types of information and reporting.
That’s why we believe it’s important to have scalable tools that reproduce pipelines that can scale readily across trial locations – or with an increasing number of plots. As you know, it’s not just straight up performance you’re looking at, here. You’re using that data to look for trends and variability. You need to know how your seed is performing in various weather conditions – and, if it improved, you need to know by how much.
With manual methods, there’s plenty of information you can gather – but there’s also plenty you might miss. With the proper technology, you can track not just whether it improved slightly, but whether performance was affected by the weather by, say, .01 percent, and whether that was the case in every instance or if that varied by location in the field, spacial uniformity (or lack of it), or other factors.
Now, we’re a little biased, but with Sentera, it’s easy to collect all the data you want using our drones, sensors, and FieldCapture, as often as you want – then use whatever software you prefer to analyze it.
Of course, we tend to think that transforming that data into analytics with FieldInsights, then using the FieldAgent platform to generate reports is the most effective way to get all the information you want in exactly the way you want it, but we realize everyone has different needs and preferences, so if you simply want a spreadsheet you can sort, that’s no problem.
But, regardless of what specific technology you opt to use for your high-throughput phenotyping, it’s vital that you make sure it can accurately sense exactly what you need and pair with your preferred software for data analysis to ensure that you make the most of those seed breeding measurements.
Download our free eBook to see how our full suite of FieldInsights data supports high-throughput phenotyping and characterization for seed breeding.