Challenges in Using Open Source Datasets for Your AI Agriculture Project

Category: AI insights

Published date: 10.08.2020

Read time: 5 min

Obtaining the training data you need for machine learning can be difficult if you don’t know where to look. Although there are many open-source datasets available, finding exactly the right data may be challenging. For example, there are many different datasets available through aggregators like Kaggle and Google Dataset Search, but you may need to do some more digging to find the exact data you require, especially in an area like agriculture where there are many different types of data annotations. Let’s take a closer look at some of the issues you might encounter when using open-source datasets for agricultural AI models, and how custom data annotation services will help you create a higher quality product.


Lack of Data Integrity

When we consider the robotic process automation (RPA) being developed for the agricultural industry, the training data must be of the highest quality to ensure accuracy. For example, last year the first raspberry picking robot, able to pick 25,000 raspberries per day, was unveiled. Although this robot can collect a lot more raspberries than a human worker, you can imagine the accuracy level needed in the data annotation process to ensure the robot understands the difference between a ripe and unripe raspberry. This requires highly detailed semantic segmentation in order to differentiate between all of the color shades that raspberries produce during their growth. Likewise, before using a dataset in your project, you must be certain that the right type of data annotation has been executed, and that it has been executed correctly.

We must also consider who performed the data annotation. There are some tasks that can only be performed by specialists with many years of experience in the agriculture industry. When you decide to use an open-source dataset, there is no way of knowing who actually performed the annotation. This is why hiring a dedicated team to take care of your data annotation needs is the smart idea, as you will have greater control over the quality and integrity of the data.

Is the Dataset Really Open Source?

When seeking out an open-source data set, you will undoubtedly encounter many cases where free is not exactly free. Although a dataset is found in an open-source collection, like Kaggle or Google Dataset Search, licensing may prevent you from using the dataset for commercial purposes. There are lots of open-source datasets in the database that are just there for helping others create open source projects, so you really need to fully understand the licensing requirements to make sure you do not violate any rules. Likewise, you need to know that just because a particular dataset is included in the open-source database, this does not necessarily mean that it was intended to be free to use. There is always the possibility that someone submitted a particular dataset against the original licensing requirements, and using the data could land you in a lot of trouble.

Open Source Datasets Will Only Get You So Far

While it may be useful to use an open-source data set to create the first version of your product, you will need customized annotation in order to perfect it. Large companies like GAFAM can make their datasets available because they are dominant players in their respective fields. In the agriculture industry, however, there are so many companies competing for the same market share that it is highly unlikely that any of them will freely share their annotated datasets. With this in mind, you will need to hire a dedicated team to make sure you have the requisite amount of data to train your model, and that it is of high quality. Since you will eventually need to have custom data annotation, it is better to start out the right way so you can begin working with your service provider and determine whether or not they can be of value over the long term.

Mindy Support Provides Comprehensive Agricultural Data Annotation Services

Mindy Support is one of the largest BPO providers in Eastern Europe with more than 2,000 employees at six locations across Ukraine. Our size and location allow us to quickly source the necessary candidates and we can scale your project without sacrificing quality. Our rigorous QA process ensures that all annotation work is done correctly the first time, saving you money and allowing you to meet deadlines. This is why SMEs, several Fortune 500 companies and GAFAM trust Mindy Support with their data annotation needs.


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