Explore some of our most frequently asked questions to learn more about how Automated Fruit Scouting Inc. can solve your fruit counting problems.

What business is AFS in and what problem is being solved?

Automated Fruit Scouting (AFS) is focused on accurately solving one problem, the oldest data problem in agriculture: counting the fruit in the field.  We help growers instrument their fields in a simple and practical way so they have accurate counts of buds, flowers, fruitlets, maturing fruit and ripe fruit.

AFS is an Industrial Automation, Ag Tech, Computer Vision company. Our solution creates new conditions for precision farming operations, closing the gap between what is possible in the agriculture and manufacturing sectors.

Why is that important?

Current industry standards are to count fruit manually, a.k.a., eyeballs and clipboards in orchards and fields.  Manual counting is time consuming and yields highly flawed reports that growers rely on to make million-dollar decisions such as when to conduct fruit thinning, how to dose chemicals, and resource requirements needed to optimize harvest windows.

How does AFS solve this problem?

AFS's Fruit Recognition System™ uses a proprietary agricultural computer vision solutions to make accurate measurements throughout the year.  Counts of buds, flowers, fruitlets to harvestable fruit can be obtained by taking pictures with a smartphone.  Scouting that used to require hours can now be completed in minutes.

How does it affect my operations as a grower?

It is a business intelligence tool that adds value to your decisions and time to your day.

It can greatly speed up you scouting processes because an entire row can now be accurately surveyed in about 10 minutes.  Easily obtainable data about entire rows and blocks provides you with more insight into orchard productivity.  

Photos can be taken with a smartphone and uploaded to AFS for processing.  AFS then promptly turns around a FruitScout Report™ based on the tree, row of trees, or block that you have scouted.

FruitScout Reports™ that fit precisely into existing workflows and contain data that is both verifiable and reviewable.  Applications include crop load management, yield estimation, running experiments, and managing ongoing field operations for optimal fruit quality.  Greater visibility into realtime developments enables faster responses to business developments.

How big is the investment that I, as a customer, would need to make in order to get started?

There are no significant expenses or capital costs to implement AFS's Fruit Recognition System™. For AFS SNAP and FruitScout™ BlockView, all you need is a smartphone.

How does a grower usually get started with AFS and its FruitRecognition System™?

We work directly with you, in-person or remotely. Our process typically begins with a customer pilot. Its purpose is to ensure all of the questions related to full-scale implementation are addressed for you the customer and AFS’s technical support teams.  During this period, you will receive FruitScout™ Reports™ about the piloted acreage.  Pilots can be conducted successfully with hand-held smartphones using the AFS SNAP app or FruitScout™BlockView product options.

If AFS is taking pictures of my crops and delivering me a report, who owns the data?

AFS’s policy is that each customer owns the data pertinent to their farm or orchard.  AFS will use metadata to develop enhanced customer solutions and to troubleshoot where necessary, but the actual plant images will be owned by the customer.

How do I get in touch with AFS?  Who are its distributors?

AFS works directly with farmers. You can reach us at info@fruitscout.ai. We'd love to hear from you!

Does AFS use drones?

Drones have their applications, but they do not count fruit. Drones could carry AFS’s optical sensors except that in the US, a commercial pilot’s license is required to use them in commercial orchard operations. Collecting data with hand-held smartphones is the approach favored by our customers.

What does AFS count?

AFS identifies visible fruit on trees at all stages of growth: buds, flowers, fruitlets, maturing, and ripe fruit.  It does this by analyzing a smartphone picture that shows 1 - 4 trees abreast.  Detecting the buds, flowers, or fruit in the image is the first step to analyzing them.

What else is AFS counting?

Nothing. While other fruit may be visible in the picture, AFS excludes fruit showing on background trees or laying on the ground. The system identifies a specific cultivar and will not make the mistake of counting similar fruit, such as a crab apple, even if a crab apple tree is among the 1 - 4 trees in the image.

What about the fruit or flowers not in the images?

How much of the orchard is imaged depends on the grower’s needs, and is determined individually by each customer. AFS starts with a recommendation of about 5% coverage per photo scouting pass, then works with the grower to develop a block by block customized photo scouting plan for the season. For a typical 10-15 acre block, an entire row can be photo scouted in about the same time it would take to manually count one tree.

What about counting both sides of the tree, or interior fruit hidden from view?

In data science terms, interior apples are just another part of the block, and since it is impractical to image the whole block, the objective is to collect a statistically representative sample. It is useful to convert metrics to ‘per tree’ units, but the fundamental measurement is of buds, flowers or fruit per row. Some may prefer collecting images from multiple sides of a tree, but AFS recommends spending that same amount of time gathering more data (images) from an additional row in the block.

How does FruitScout™ help with my crop yield estimates?

Unlike systems that promise the shiny “Holy Grail” of estimating counts of fruit that cannot be seen, AFS is in the business of taking direct measurements in your orchards.  Many growers develop estimates by comparing what they see today to recollections of prior years. Instead of replacing that expertise, FruitScout™ upgrades the yardstick used to make year-over-year comparisons.  It takes the labor and subjectivity out of image collection, turning estimating into a quick, accurate, and objective data-driven process.

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