The Ongoing Challenges
In the dynamic world of fruit growing and packing, precision and efficiency are paramount. Every harvest, fruit growers and packers face the challenge of accurately assessing the size and color grade of their incoming fruit, often relying on inaccurate manual methods or costly pre-grading processes.
These inefficiencies can lead to difficulties in promotional, sales, and storage planning, poor labor allocations for packing lines, short periods of downtime during lot changes due to a mismatch in drops for the incoming lot, and other time-consuming challenges that ultimately affect the bottom line.
Introducing Hectre Color Grade
An upcoming innovation that unlocks color grade information earlier than ever before. With Color Grade, you can get a deeper insight into the color profile of your fruit.
For example, you can uncover what percentage of your apples or cherries have enough dark red that they can be exported, or whether your Galas have a green background or a yellow background, which could help you decide whether to store them in long-term storage.
This information can be gained from just a single photo and can be captured anywhere, doesn’t matter if you are in the packhouse or the orchard. Just imagine what more can be achieved by having this crucial information much earlier in the production cycle.
The Value Of Color Grade For Growers
Alongside packhouses, fruit growers are also finding immense value for accurate color grading in a variety of ways.
For one, Hectre Color Grade allows growers to distinguish between different combinations of foreground and background color on their bi-color fruit to match their packhouse’s color specification.
Additionally, Color Grade enables growers to monitor picker productivity and fruit quality, providing feedback to improve picking practices. Growers can easily identify if citrus fruit is being picked too green or cherries are too light, allowing them to refocus on harvesting the most valuable fruit.
Furthermore, Color Grade helps growers predict the percentage of their fruit that will meet their packhouse’s top grade standards. By inputting the packhouse’s grade standard into the Spectre system, growers can anticipate and plan for the quality of their harvest, optimizing their operations and maximizing profitability.
So How Does It Work?
Hectre Color Grade allows you to easily set up different grade classifications for each of your varieties, with flexibility to configure it to match the grade standard in your packhouse. You can set up color intensity and percentages to define their different grades.
For instance, if 35% or more of an apple is dark red and 35% or more is medium red, or if at least 50% is dark red and no more than 20% is green, that apple would be classified as Premium grade based on what you defined.
This process may sound familiar, as it is much like when you need to adjust color intensity thresholds on a sorting machine. Then, depending on when you want to receive this information—be it at a packhouse receiving site or right in the field just after picking—you take photos of the top layer of your bins using the Hectre Hand-Held app or your Top Down setup. Within seconds, you receive your color grade information along with a size distribution for that lot.
What’s more – thanks to this flexibility, Hectre Color Grade will accommodate more fruit types and will work perfectly well for green and yellow fruit too!
Have you ever had an issue where you made a commitment to a buyer for Premium grade fruit based on the average size of the lot, then when you see your packed Premiums are skewing much bigger than the rest of the lot, you scramble to open another room to fill the order?
Hectre Color Grade can now avoid this situation by zooming in on each grade and seeing the size breakdown.
How do you know the color grade, if only a photo of one side of the fruit is taken?
Hectre Color Grade achieves accurate, aggregate color grade information from just a photo of one half of each apple through statistical inference over the whole lot.
Assuming fruit from the same lot is generally similar, we use statistical inference using all of the other top halves of the apples in the lot to narrow down what’s likely on the underside of the fruit.
For example, if no green areas are visible on the top layers of apples in the lot, it is highly unlikely that the bottom half of any other apples will be green. This approach allows for reliable color grading without the need to capture the entire fruit.
A common concern that we hear is around pickers placing fruit with the best color on the top layer. While this can occur, it’s quite difficult to do at such a scale consistently to skew the results meaningfully. When it comes to size, our results over the past have proved that we’re consistently ±5% of grader accuracy.
How do different lighting conditions affect the color grade results?
Hectre Color Grade handles different lighting conditions by allowing you to set up seperate color settings for three lighting scenarios: bright, medium, and dull.
When a photo is taken, the algorithm automatically detects the lighting environment and uses the colors you have set up for that specific lighting condition. This ensures accurate grading regardless of where or when the photo was taken.
The app also accounts for shadows cast over bins; if half of a bin is shaded, Hectre Color Grade treats the shaded half differently from the brighter half, using a different color setup for each half of the bin.
This automatic adjustment simplifies the process, and your QC staff don’t have to make any choices when they are taking photos. They simply upload the photos with the selected variety, and Hectre takes care of the rest, providing reliable color grade results.
Interested In Learning More?
Hectre Color Grade not only enhances the efficiency of fruit growing and packing operations, but also provides peace of mind by giving you confidence in knowing the color grade of your fruit, early.
Register your interest in Color Grade, and be among the first in the world to unlock this benefit for your operation.
