Agricultural organizations face a unique challenge. While the industry itself—as well as the farmlands upon which businesses within those industries make their money—are indisputably large, the actual profit margins that these businesses are seeing are remarkably slim.
Much of this, as we will discuss, is due to previously unseen inefficiencies, which put the burden on farmers to expend unnecessary resources in order to ensure the value and quality of their crop yield.
When left to manual efforts, it’s extremely difficult to ascertain accurate crop yield predictions and to guarantee the margins that most agricultural enterprises want. Yielding lower production than anticipated—especially when your business is pouring resources into monitoring the health of crops and the shipping of product—can be frustrating, if not crippling. Even more importantly, inaccurate forecasts for agricultural yield can disrupt trust between businesses and their buyers, should they be unable to actually provide what they’ve promised.
Luckily, with Computer Vision there is a better way to do business in agriculture: one which can minimize spend and maximize profit margins.
Old Methods of Monitoring Crop Health Are Outdated: Importance of Computer Vision in Agriculture
As agricultural operations expand to cover increasingly vast areas of farmland, manual methods for crop management are becoming impractical. Modern tractors now incorporate precision farming technologies, such as automated picking systems and precision spraying. However, even with advanced vision systems, there is still significant room for error. For instance, tractors cannot operate effectively in fully grown cornfields (due to lack of space for tracks) or strawberry fields (where plants and fruits are too delicate). Likewise, using crop dusters isn’t always the most efficient solution due to high costs associated with planes, pilots, and spray wastage.
For this reason (among many others), major agricultural organizations have opted to forego using tractors for crop inspections in favor of drone technology, which allows for a wider range of coverage in a shorter amount of time.
After all: who wants to have to personally drive up and down the entirety of a massive agricultural plot when you can simply have a drone with a highly accurate camera fly over it—delivering equally, if not more, accurate information on your yield with far less drain on your manpower. You can hone in on sick crops and figure out whether they can be treated or simply must be uprooted; you can identify specific problems or blights on different types of crops—be they fruit or grain—and more effectively work to fix them.
All of this, without having to spend hours driving a tractor in the sun.
How Computer Vision is Transforming Farming and Increasing Agricultural Yields
Using drone technology to collect all of this information is well and good for agriculture businesses; but how do you synthesize all of this information into accurate yield predictions? How can you tell if the ton of strawberries you promised a buyer will actually be ready? How do you know which of your apples are best for supermarket shelves, and which are better used for juice or cider?
Computer Vision is designed to help agriculture enterprises pull the essential insights from their crop inspections to ascertain genuinely accurate predictions about their potential yield. Leveraging historical data and incorporating variables such as seasonal weather and the rate at which different crops are growing, this technology allows farming organizations to know exactly what they can promise to their buyers, as well as the quality of the promised yield.
But this analytical data doesn’t begin and end with crop health during the growth period. Computer Vision can also help businesses sort their produce according to size and quality. Does an apple have a dent in it that will make it unappealing when it’s seen by a grocery store customer? That apple can be sold to a buyer looking for fruit for juicing.
Through highly intelligent automated software capabilities, Computer Vision helps enterprises accurately predict their yield and make the most of it.
Maximize Your Crop Yield: Leveraging Computer Vision in Agriculture
We would be preaching to the choir should we say that agriculture is an industry marked by unpredictability. With massive farmlands and a wide variety of crops, businesses are working overtime to read the tea leaves in hopes of predicting their yield, as well as the quality of said yield. As manual strategies prove to be increasingly costly and untenable—especially in the face of growing buyer demand—it’s more pressing than ever that your enterprise is able to gather accurate visual data on your crops, and analyze it accordingly.
Akridata is here to help. With Computer Vision in agriculture, your business can more thoroughly understand the health of your crops, the issues that individual plants may have, the size of your yield, and how you can make the most out of that yield. In doing so, you’ll be in a better position to strengthen your position with buyers and increase your profit margins, by expending fewer resources and gathering more precise information.
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