When we were initially approached about having the capability to scan bins and receive a size profile, what intrigued me was how quickly we were able to snap a photo and get results within a matter of minutes. I wanted to have that ability, but I was a bit skeptical at first, thinking, "How is it going to do all that by just scanning the top of the bin?" It seemed somewhat far-fetched, or at least very interesting.

Being curious, and always looking for the next big thing, I didn't want to miss out on the opportunity, so we signed up for a Spectre trial. I wanted to give Spectre a shot because the fruit industry, particularly the apple industry, is more progressive now. Things are moving along, with a more data-driven approach and technology being heavily involved. We also received a lot of positive feedback from Hectre customers using Spectre, which contributed to our decision to give Spectre a try.

Before we had Spectre’s early-fruit-sizing technology, we used a manual system to assess fruit size. This method involved using information from our field manager, weighing fruit, and relying on our experience to judge the size of the fruit. Typically, as the apples arrived during harvest, we would lay bins out on the pavement and do an overview of the fruit. We would get an idea of whether the fruit was consistently small, medium, or large by looking at them. We then took notes and used spreadsheets to create size profiles for each CA room.

We utilize Spectre Top Down in our receiving area to gather information about the incoming fruit bins, and ultimately to help decide which room to store the fruit in. The process begins with an apple truck passing under the Top Down camera, which provides us with a size profile for each particular variety or lot. We then create a Tableau file that records the size profile against specific rooms and lots.

We aim to create a blend of different sizes within each room, as it ensures a more balanced inventory and prevents potential sales difficulties that could arise from having rooms filled exclusively with large or small fruit. Typically, we open only one room at a time, as opening multiple rooms simultaneously could lead to the fruit being exposed to regular atmospheric oxygen, which could negatively affect the storage conditions.

Spectre's data helps ensure a balanced distribution of different sizes, while also providing valuable data to help identify the CA room we should open when we are looking for a particular fruit size. It helps eliminate the need for guesswork in determining what's in a room, making it a valuable tool to have when making these storage decisions. Additionally, we can break down Spectre’s data by receiving dates to identify any inconsistencies or differences in sizing related to the date the fruit was received.

Now that we have Spectre, I can easily open the Tableau file containing Spectre’s dataset, look for a specific room, and get a general overview of things. The dataset generated by Spectre is already in a CSV file, which means I can easily import it and then create a worksheet for myself. This is more efficient than scrolling through a spreadsheet and looking for a range of sizes. Instead, I now have a breakdown of the percentage of each individual size in a particular bin, making the process more efficient.

Before Spectre, it would take me up to half an hour to go through the rooms and with my spreadsheet get an idea of the sizing in each room. With Spectre’s data and the Tableau worksheet, I can now go through that information in about five minutes.

When there is market demand for a particular size, the sales team may ask for information on the size profile of a particular variety. We use the data we collect with Spectre for making informed sales decisions, particularly when it comes to promotions.

If a marketing campaign requires specific sizes, Spectre’s data helps identify the room that is storing the required fruit. By analyzing the data for a specific CA room and considering the number of bins in that room, the team can gain insights into how many boxes of a particular size will be available. This information is then shared with the sales staff, who can compare it to the requirements of the promotion.

We generally have one packing line dedicated to larger fruit and another for smaller fruit due to their different capabilities, such as bagging and bulk filling or tray packing. We use Spectre’s data in tandem with manually evaluating the consistency of the fruit before we run it. By combining Spectre data with our visual assessment of the bins, we are able to make better informed decisions about which packing line to use for the fruit.

With the success of Spectre for Apples, and with the expectation it will be just as beneficial, we will soon be implementing Spectre for Cherries in our packhouses. If Spectre for Cherries proves to be as effective and beneficial as it has been for apples, it will be another fantastic addition to our process.