Actionable insights from agricultural drone imagery
Variety trials require massive data collection and precise analysis—often the biggest bottleneck in scaling research. MAS Seeds, a leading provider of hybrid crops, turned to drone technology to accelerate and improve their field assessments.
Plant Focus masks out soil, weeds and grass using a vegetation index or PlantAI detections, so plant health maps and plot statistics reflect only the crop.
Precision ag technology, like drone imagery, holds great potential to improve production efficiency, but most growers want to see the proof in the pudding before investing time and money. So what’s the best way to make on-farm technology accessible?
Traditionally, growers based harvest timing on visual observations or sample field measurements. But most growers would like a faster, more objective means of evaluating harvest readiness and head size. Plant Counts with size estimation from Solvi gives growers a simple protocol to automate the process.
When DJI’s new Mavic 3 Multispectral drone landed in the market, a lot of heads turned. We decided to take a deep dive to see what’s making it so popular and see how it performs in precision agriculture research like plant counting and field analysis.
There are two basic approaches to plant counts: manual (row sampling) and image-based (computer-powered). They may return the same type of data, but the methodology and uses are worlds apart. Which one should you choose?
North Carolina State University researchers conducted a two-year study to better understand cotton replanting recommendations using UAVs (drones) to count plant populations and, more importantly, skips.
We are excited to announce the release of PlantAI 2.0 featuring more detailed plant-level analytics, support for multiple-crop detections, row and gap analysis, and more
How to avoid and correct the five most common plant counting mistakes for fast, highly accurate crop stand counts.
Vegetation indices are valuable precision agriculture tools to assess crop health, yield potential, and other variables that drive informed farm management decisions. Choosing the right vegetation index is vital for accurate results but depends upon camera hardware, environmental conditions, crop type and growth stage.
To make precision agriculture more accessible, the Swedish University of Agricultural Sciences (SLU) is developing decision support tools to aid growers in winter wheat fertility management. These ongoing projects are powered by drone sensor-based models.
Plant counts are an essential part of precision agriculture’s expanding dataset. But the traditional error-prone row plant estimates are an unreliable relic. Transform small-scale sampling into an accurate full-field inventory with drone plant counts.
How high to fly a drone for agricultural field mapping is a common question among pilots. Since drone altitude impacts the resulting stitched image quality (and metrics available), getting it right can improve your crop datasets and reduce data losses or the need for reflights.
Breeding new vegetable varieties can take between 6-16 years of rigorous evaluation before a package of traits is expressed stably enough for commercial release. To expedite their breeding pipeline, Rijk Zwaan adopted drone imagery to assist in characterizing phenotypes of promising breeding lines and to document their volumes of vegetable trials.
5 Critical data quality factors to consider when choosing a drone imagery processing platform for multispectral data from MicaSense sensors.
The Danish Technological Institute uses Solvi’s multispectral drone image-stitching and data processing in their agricultural research analytics to evaluate volumes of field data fast.
Learn how to streamline the process of creating research plot boundaries with a simple user interface to visually create plot boundaries, quickly render statistics, and export results.
Case study: how Nordic Beet Research uses drone imagery to inspect 10,000 sugar beet trial plots more efficiently and make trial data more objective.
A mango grower decreased hydric tree stress by 18% during critical fruiting stages while reducing overall water consumption 50% by establishing unique management zones from Solvi's drone analytics.
Drone plant counts give vegetable growers a whole-field view of stand loss and plant size, to pinpoint problem areas, predict yield and plan harvest labor.
Crop stand establishment directly affects yield. But often we don’t know what this season’s or any field’s limiting factor is.
Solvi Plant Counts now powered by Plant AI™ converts plant-level drone data into actionable crop insights in minutes.
Drones are an effective crop scouting tool for vegetable and grain farmers to assess plant health and guide management decisions to improve crop performance.
Weighing green biomass and scanning the field with a drone is a simple, fast, and cheap way to optimize nitrogen application for winter oilseed crops.
Learn how using drone imagery to map variations in crop establishment can help create variable rate seeding files for the following seasons.
Nitrogen application is one of the key actions that growers take to ensure stable crop development. Learn how drone imagery can help create prescription files to ensure the right amount of fertilizer is applied in the right place.
Support for the all-new MicaSense Altum multispectral sensor, including new thermal data visualization, has landed in Solvi!
We are excited to announce Zonal Statistics and Trial Plot extraction tools for more effective plant phenotyping, breeding, and research.
