All About Precision: AGERpoint achieves 99% accuracy in tree counting and 95% accuracy in canopy metrics
While we pride ourselves in collecting agricultural data in a timely manner (and with less manpower) than other data collection methods, our main focus is on the accuracy of the data we collect and provide to our clients. Precision is crucial to us here at AGERpoint.
We work with clients, across multiple crop types, who use the data we collect and process for a variety of business, technical, and scientific needs. This requires our data to be precise so it can provide actionable information for our clients.
How accurate can we get?
Pretty accurate – we are able to achieve a 99% accuracy in tree counting, and can achieve a 95% accuracy in canopy metrics, such as canopy diameter, volume, density, and tree height. And if we have clear visibility of trunks, we can also achieve a 95% accuracy of truck diameter.
How do we get such precise and accurate data?
Through a combination of algorithms, QAQC using custom built tools, machine learning and classification methodologies, we are able to get the accuracy as described above. And we continue to validate and calibrate our methodologies on a regular basis for the most precise results.
QAQC Edit Process
The end-to-end AGERpoint data process includes an edit function, the QAQC Edit Process, executed by our trained Geospatial engineers and utilizing proprietary and custom AGERpoint tools. These tools allow our team to manage “outlier plants” identified by the algorithms that process the raw data we collect in the field.
Our QAQC Analyst makes visual edits using our custom tools on these rare outliers, further ensuring total data accuracy. At least ~5% of all scanned trees are sampled and visually analyzed to ensure data precision.
Field Ground Truthing
As part of AGERpoint’s standard field collection processes, we have developed reference data sets based on crop types from statistically relevant samples of trees and their associated parameters (height, trunk diameter, canopy diameter, etc.). These samples are physically measured and recorded in the field. This data is makes up the ground-truth data set used to benchmark the macro results of AGERpoint’s end-to-end data process.
We compare our collected measurements against the ground-truth data sets. The collected measurement is considered correct if it falls within a threshold of distance of the ground-truth measure (typically around 10%). Comparing our measured data to these data sets it where we are able to derive our accuracy metrics.
With over 1.5 trillion data points collected across 24 different crop types, we have a large store of historical data. This allows us to compare data sets of like crop types to ensure that what is collected is consistent across the software development process and past data collections.
With our plant matching algorithms, we are able to scan the same plants again and again to further refine the accuracy for an individual client down to the individual plant level. Over time trends or issues may be easier to spot with our data collection than with previously used methods, like manual sampling.
Our methods together are what lead to incredibly accurate data for our clients. Are you interested in learning how you could get 99% data accuracy, with less manpower? Contact us today: Sales@AGERpoint.com
GAIN THE ABILITY TO REDUCE LABOR ASSOCIATED WITH THE IDENTIFICATION, TRACKING, AND MANAGEMENT OF AREAS WITH WEEDS.
Our client needed a way to easily identify areas of poor weed control, while at the same time decreasing the labor associated in this management process. Historically this process has been a manual inspection on each row by farm employees – increasing time and cost.
In addition, they wanted to have the output (artifact) to be easily usable by farm workers on-site via a mobile device or tablet.
COLLECTED AND COMBINED GROVETRACKER™ AND AERIAL MULTISPECTRAL IMAGERY DATA TO CRATE ONE FUSED GPS ENABLED IMAGE FOR EASY NAVIGATION OF IDENTIFIED WEED AREAS.
Taking a one-time data collect with GroveTracker™ and three rounds of aerial multispectral imagery data collected over a one-year span of time, the final product was a map showing exact locations of weeds indicted by defined markers.
The defined weed markers were then categorized by how frequently they returned allowing the identification of weeds that were tolerant to the herbicide used. This categorization allowed our client to focus efforts on those areas with a more advanced weed management protocol. In the past the same weed management practice would have been applied to all weeds, resulting in poor control on herbicide tolerant weeds.
REDUCED LABOR COST BY MORE THAN 50% DUE TO THE COLLECTION, PROCESSING, AND ANALYSIS OF BOTH GROUND AND AERIAL IMAGERY DATA.
Not only was the desired result of decreased labor costs realized, the eradication of identified weed areas aided in yield management, and reduced water requirements for affected areas – furthering cost savings and revenue opportunities.
COLLECT AN EXTENSIVE AMOUNT OF DATA TO SUPPORT PRODUCT DEVELOPMENT.
Our client works daily with crop protection products and services, fertility products and services, and plant growth regulators. They run regular trials on these services and require a large data set to make decisions. They needed to be able to not only collect an incredibly large amount of data, but also reduce trial management expenses.
DATA COLLECTED USING A COMBINATION OF GROVETRACKER™ AND YIELDTRACKER™ PRODUCTS.
AGERpoint® collected data points on tree height, canopy diameter, truck diameter, canopy volume, canopy density down to the individual tree level using GroveTracker™. In addition, yield estimation on a per tree basis was also provided.
These data points were provided as a GIS formatted data file for further analysis by our client.
~30-50% DECREASE IN TIME-FRAME FOR PRODUCTS AND SERVICES DUE TO HIGHER DATA ACCURACY AND LESS REQUIRED TRIALS.
As AGERpoint® collects data on thousands of trees versus the typical process of only hundreds at best, the higher data accuracy of trials was able to give our client more confidence in their findings. This larger data set also allowed for identification of specific product characteristics as it related to tree growth.
In addition, as less trials were required for our client their management expenses for end-to-end trials were significantly reduced.
Decreased Labor Costs While Producing 95% Accuracy Level On Yield Prediction Using A Multi-year Sample Set
IMPROVE THE EFFICIENCY AND DECREASE LABOR COSTS ASSOCIATED WITH YIELD PREDICTION ON CITRUS CROPS.
The yield prediction process has historically had high labor costs due to the complexity of the data points needed to collect and analyze. Our client wanted to improve the accuracy of their yield prediction while lowering their labor costs.
DEVELOPED AN OFFLINE MOBILE MAPPING SOLUTION TO NAVIGATE TO TREES AND ACCURATELY COLLECT DATA FROM THE FIELD.
Leveraging existing GroveTracker™ data sets, an online mobile mapping solution was developed to allow low skill workers to efficiently, and accurately, navigate to specific trees and capture the data from the field directly into the mobile application.
This entered data could then be exported and the calculation of predicted yield automated.
YIELD PREDICTION 95% ACCURATE OVER A MULTI-YEAR SAMPLE SET.
Using GroveTracker™ and Volumetric methodology, the calculated yield prediction was verified to be 95% accurate over a multi-year sample set of data collected.
This allowed our client to leverage lower skilled (and lower wage) workers to collect data in the field and still receive an extremely accurate prediction. The data analysis complexity and associated effort was almost completely eliminated and allowed the data to become actionable.
In addition, the availability of multi-year data allows for the analytics to potentially quantify, measure, and improve field conditions and yield across blocks (block variability).
IMPROVE EFFICIENCY OF SCOUTING AND IDENTIFICATION OF UNDERPERFORMING TREES.
Our client was looking to make their scouting practices for insects, disease, and water issues more efficient. In addition, the goal was to be able to identify trees with issues faster than they would be manually identified.
FUSED GROVETRACKER™ AND VITALITYTRACKER™ DATA TO EASILY VIEW DATA POINTS FOR ALL TREES AT A GLANCE.
The benefit to fusion of ground and aerial data is the ability for advanced analysis of disease down to the individual tree level. In addition, the availability of year-over-year data allowed for sophisticated decision prediction and management.
EASIER, AND DOWN TO THE INDIVIDUAL TREE, IDENTIFICATION OF UNDERPERFORMANCE.
The combination of GroveTracker™ and VitalityTracker™ data via the AGERmetrix interface allowed for easy identification of problem trees with cankers and marginal leaf necrosis. This data was delivered through a mobile app to show specifically which tree had issues as shown to the right – yellow trees are the ones that need to be scouted
EVALUATE CURRENT TREE PRUNING PRACTICES TO VERIFY AND VALIDATE IMPACT ON YIELD AND REDUCTION OF MANAGEMENT COSTS.
Our client ran trials to review their pruning methods effect on yield and management costs. Yield is measured on a per variety basis and is attributed down to the individual tree. In addition, the management costs includes not only the labor costs for pruning, but also debris management.
COMBINING DATA FROM GROVETRACKER™ AND YIELDTRACKER™, AGERPOINT® WAS ABLE TO CREATE A YEAR-OVER-YEAR YIELD VISUALIZATION ON A VARIETY BASIS.
Over a three-year period, AGERpoint® collected GroveTracker™ across the identified blocks and YieldTracker™ data throughout the almond harvest season.
Yield data was collected by attaching the YieldTracker™ collection system to client supplied vehicles and scanning during harvest. This data was then transformed into representative volume indices and matched to the specific plant and its associated row within a block. With this syncing of location, the YieldTracker™ data and GroveTracker™ data could be married to provide insights relating to pruning management.
AGERPOINT® WAS ABLE TO DERIVE THAT SOME PRUNING TECHNIQUES DECREASED YIELD VERSUS NORMAL PRUNING PRACTICES BUT SOME ACTUALLY INCREASED YIELD.
Values from AGERpoint®’s YieldTracker™ showed that the different canopy management techniques had significant impacts on yield three years after the pruning. Differences ranged from a 3% increase to a 11% decrease in value per acre versus normal pruning practices. Further, when GroveTracker data is applied with YieldTracker data, it shows that the different pruning techniques had little impact on tree height. Based on these insights, the client is now able to compare the cost of the different pruning practices with the impact on yield to determine the most cost effective pruning method.
We know growers don’t make decisions on how to handle their farm with only one or two pieces of information. And we certainly know that growers take all data points into consideration when making their decisions.
The image below show the yield of a block based on a coloration index – the darker the color, the more yield and the lighter the color, the less yield. With this yield data overlaid with other data a grower can easily identify trends or spots that need attention. For example, there may be a pattern in yield that is related to a specific variety of crop, life cycle of crop, drainage.
Because this data all lives within AGERmetrix it is easier than ever to identify these trends from a laptop without having to walk blocks and keep manual notes.
Interested in learning more? Send us a note!
Like we mentioned earlier, we’ve been pretty busy making upgrades to our AGERmetrix platform. We’ve listened to feedback and worked to incorporate that feedback into our improvements.
Recently we’ve added the feature for growers to see multiple blocks under their farm in the data panel. Growers can still change blocks by selecting the specific block from the map (see the image below), but can now also select the block they’d like to look at in more detail from the data panel as well.In addition, growers can open multiple blocks in the data panel to do a visual comparison of what is in their blocks – like number of trees. This is extremely helpful especially when running trials on crops as a quick comparison can be seen in one view.
Growers will also be able to easily access data from each of AGERpoint’s products; GroveTracker, YieldTracker, and VitalityTracker. This is shown by the blue, purple, and green badges in the data panel.
Interested in learning more? Send us a note!
Use GroveTracker, YieldTracker, and VitalityTracker, or some combination of these AGERpoint products? Now layer products and views together to analyze an entire data set.
Everything from matching up the specific locations and phenotypical data from GroveTracker to the insights on health, vegetation, photosynthesis and more from VitalityTracker, to post-harvest assessments collected with YieldTracker. Now a grower can toggle back and forth on the blue, purple, and green badges which represent the different product types available for a block to see the data points collected. This allows the grower to use a combination of AGERpoint’s products efficiently to see their morphologies, yield, and vitality to help make crop management decisions.
A grower no longer has to flip back and forth between the products. All of this data is easily accessible in AGERmetrix, our comprehensive Crop Data & Analytics Platform, in one place.
Interested in learning more? Send us a note!
Having data on how crops are doing each year provides incredible insights each growing season. However, the ability to compare historical data year over year, or more frequently, provides a new level of information that will help growers make find patterns to make informed decisions with less manpower than historically able.
This is the newest upgrade to AGERmetrix, where from our single platform a grower can easily view and compare annual or harvest trends to help make growing decisions.
For example, the two images below show the same block over two collects. The first showing from July 2017 and the second in May 2018. At a quick glance the grower can analyze problem areas and how they are improving year over year.
Interested in learning more?