El control de calidad de los productos frescos puede actualizarse en el siglo XXI. ¿No cree?

Fresh produce quality control - ready for change? | Clarifruit

Until fairly recently, you’d be forgiven for thinking that fresh produce quality control was one of those things that never fundamentally changed. While the processes might have been updated from time to time, the underlying methodology stayed the same – look at produce with the human eye and note down the quality. So you may be surprised to discover that for the first time, real change is underway and stakeholders across the supply chain are internalizing the need for a different approach to fresh produce QC.

To understand this change, and to get a visual understanding of the way that fresh produce quality control has evolved over the years, let’s use stages, from 1-5. Remember, while each new stage brings with it a new QC method, it doesn’t necessarily eliminate the ones that came before. However, each new method adds to the efficiency and productivity of fresh produce QC, and can be used to complement or improve the others.

Stage One: Tracking Data about Fresh Produce Attributes (Quality and Other)

During this stage, a shift was made between immediate, on-the-spot decision making about the quality of fresh fruit and vegetables and the need to track and collect this information over the long-term. Historically, stakeholders might simply have marked a fruit or vegetable as a ‘yes’ or ‘no’ based on eyesight alone. But from this point, they realized the importance of recording this data – although they might not have called it that! This was done manually and sometimes written down with pen and paper, or stored in files. A more advanced version of this stage would be when this information was categorized by produce, type or year.

Stage Two: Storing the Data on a Computer

As computers became ubiquitous for any and all industries, manual data was slowly moved to e-formats such as online spreadsheets or in-house systems. Third-party technology often filled a gap, offering the ability to log additional attributes for the produce such as the color of the fruit or the size of a specific vegetable. This stage was still completely manual in terms of data collection, which means it suffered from the same lack of accuracy and consistency as simply writing the information down in a notebook. However, for the first time, this data could be easily shared, for example by email.

Stage Three: Data on the Move

Think about how websites and technology are now often built ‘mobile-first’ and you won’t be surprised to hear that the third stage of fruit and vegetable quality control centers around mobile devices. By allowing staff to manually input information using these devices, data could be a lot more immediate and accessible. However, while this was one step in the right direction in terms of quality control data storage, the industry was still unable to scale the hurdle of inaccurate or inconsistent data because of the reliance on manual and subjective data collection and input.

The time had come for the quality control of fresh fruits and vegetables to take a leap into automation!

Why is this So Important?

You might be saying, “Hey, the fresh produce industry has been getting along fine with manually collected information up until now. Why the need to digitize further?”

Well, to answer this question, let’s break down the three main challenges that arise as a result of manually collected data.

  1. Inaccurate information: Subjectivity leads to a lack of consistency, and simply ‘eyeballing’ a product means that each and every person has a different point of view about the quality of the produce at hand. Surely, with the advancements made in today’s fast-paced technologically-driven world, we could achieve more and see more than the human eye could see alone?
  2. Prohibitively high costs: Did you know that the annual cost of fresh produce QC is as much as $7.5 billion? A lot of this is a wasted cost, sunk into labor and resources that complete quality control in a lab environment, which is both slow and expensive. If an application could be built to measure the QC of fruits and vegetables on the spot, imagine the cost savings for every stage of the supply chain.
  3. Excessive waste: As the world becomes more socially conscious, it’s vital to address the up to 45% of fresh produce that is wasted annually, all around the globe. Quality mismatches between buyers and sellers lead to wasted produce, and manual quality control methods often lead to perfectly good food being thrown away. A consistent method for fresh produce quality control would eliminate this problem.

There is no wonder that the industry chatter is all about making a change to something objective, consistent, and cost-effective. Enter, Stage Four.

Stage Four: Automatic Data Collection. Yes Please!

This is exactly where Clarifruit comes in, offering an integrated, end-to-end platform for fresh produce quality control. Clarifruit’s platform uses computer vision technology to automatically collect information about external attributes such as color, size, stem color or any defects, and easily integrates with other third-party solutions that give insight about internal attributes such as degrees Brix or firmness. By taking away the subjective element to fresh produce QC, you work to reduce all the problems we looked at above.
But that’s not all! Leveraging this technology, Clarifruit has also found a way to support businesses with their digital transformation by welcoming in a whole new stage of quality control.

Stage Five: Analyzing Trends and Patterns Using Big Data and Artificial Intelligence

Clarifruit’s solution automatically transmits collected data from the mobile app through to a centralized dashboard, giving the same level of insight to the entire supply chain. This starts from growers and includes marketing companies, wholesalers, retailers and even the end-consumers.

This data can be collected by these stakeholders and stored on the cloud, so that they can achieve deeper analysis into the ‘whys’ of the information that is collected. Think trends, patterns, anomalies and root cause detection. As your data is the most accurate it can be, your data is also top quality – great news for data analytics projects.

With so many benefits to this technology, it looks like the future is going to be an automated one! If you want to hear more about how it works, let’s set up a call and we can walk you through it.

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