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Precision Agriculture Series: Data Challenge #3- Financial Obstacles

This is the fourth post in our “Precision Agriculture series.” In this part, we’ll address the third challenge of data labeling: financial obstacles. Be sure to stay tuned for our fifth post addressing the fourth challenge in precision agriculture: data privacy.

According to a survey of over 225 enterprise Data Scientists, most organizations are still in the early days of AI technology. As many enterprises rank AI high on their list of priorities, data science teams scramble to keep up with the escalating pressure of scaling projects. It was revealed in this survey that almost 40% of failed projects were apparently delayed in the training data-intensive phase. Upon further investigation, it was revealed that 26% of enterprises blame the failure of their AI projects on a lack of budget, and we’ve seen it several times ourselves.    

AI projects typically run a high bill. Therefore, it is of utmost importance to find the right balance for your business, ensuring that you’re focusing your attention on high-priority, cost-saving areas in order to ensure the budget is allocated efficiently.  

Assessing the financial cost of quality labeled crop data

We’ve often found a serious lack of transparency into exactly what enterprises are paying for in their data labeling projects, whether it’s in-house or contracted out, which makes it unsurprising that companies can’t manage their data labeling budget. Without any standard pricing or established metrics to use for comparison, we’ve encountered many businesses that struggle to manage their data labeling budget as a result. 

Organizations that outsource data labeling generally need to choose whether they’ll pay for data labeling per hour or per task. Paying per task is more cost effective, but it incentivizes rushed work as labelers try to get more tasks done in a given timeframe. In our experience, most enterprises prefer to pay per hour.

Farming businesses need to calculate the costs of collecting all the unstructured data that’s generated by drones, monitoring devices, field computers, tracking machines, and more. They also need to add the cost of building in-house platforms to connect and match produce and items across different sites. It is equally important to keep in mind that all this unstructured data will need somewhere to hold it, and more specifically, a platform to manage it.

Keep in mind when assessing the price of data gathering, processing, and analysis that you need to have enough data to manage your crops at their maximum efficiency. This is where advanced ML models that are trained successfully on accurate data are able to guide farmers in the choice and amount of crops to plant; optimizing pollination, irrigation, fertilization, and application of herbicides and pesticides. This can be accomplished by accurately tracking crop yield with the help of high quality models/data, in order to help you improve production and support swift and timely harvesting. In addition, this will also help to improve resource allocation, cut expenditure, reduce fuel and water spend, and improve the bottom line. 

Poor crop monitoring causes trillions of dollars of losses for farmers each year, but when done well it can maximize profits and increase revenue. 

Poor crop monitoring causes trillions of dollars of losses for farmers each year, but when done well it can maximize profits and increase revenue. Click To Tweet

Forecasting weather patterns and insect movements allows farmers to minimize the damage to produce and boost revenue. Which in turn ensures that farmers can accurately predict demand, and won’t be left with a glut of produce that won’t sell, or risk undersupplying frustrated suppliers.. 

Small, in-house manual data labeling teams run expensive, due to the time and training needed to reach true quality. As data quantity grows exponentially, prices rise too. 

Bottom Line

We know that it’s impossible to predict the final volume of data for processing, which only adds to the complexity of balancing the cost of labeled data against the cost of not having it. Our solution is to manage all your data in a single location, connecting various solutions, bodies, and teams across locations to a central platform. 

Are you concerned about your budgetary needs, or want to find out how we can help manage your data labeling budget? No worries, we’re here to help, reach out to one of our experts to learn more about Dataloop.

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