Now that drones have become more accessible in price, they are finding themselves become useful in a host of industrial applications where costs would have formally been a limiting factor. Agriculture is no exception. Farmers have long relied on aerial imagery, among other tools, to take stock of the quality of their crop. Aerial shots can do so in a way that is simply not possible when looking a field from the ground. A farmer, for instance, can learn about irrigation problems, pest infestations and analyze soil quality simply by looking at their fields from an overhead perspective. With drones, however, the cost of doing so drops considerably.
What used to cost a farmer $1000 per hour is now costs farmers a fraction of that price to survey their fields aerially. Farming drones can be purchased for as little as $1500, but, of course, can be reused. Not to mention, newer drones are being made specifically with agriculture in mind. They are programmed to scan entire fields using high-resolution imagery entirely without any human intervention. Furthermore, they have built-in analytics to actually parse through the data and provide actionable insights. It is much more cost effective than having a person go through the raw data, as what is commonly done now.
It is speculated in the future that drones could garner an even bigger role in agriculture. Drones with a bigger payload capacity, for example, would be able to treat crops with pesticides efficiently and without human intervention. One could imagine a future wherein farm drones both survey, analyze and react to crop quality and treat the problems all on their own.
Like many industries which were not immediately affected by automation the digital revolution, agriculturalists are beginning to adopt a data-oriented mindset when it comes to increasing crop yields. With the proliferation of cheap sensors and smart systems capable of parsing through big data sets, it is becoming increasingly cost effective for farmers to approach their business in the same way an e-commerce store would optimize their website. It has led to the adoption of a farming practice known as precision farming.
Precision agriculture uses data generated by sensors placed on drones and other farming equipment to analyze and suggest ways of improving efficiencies across a farm. This not only includes optimizing the quality of a yield, but, also, to reduce the amount of inputs such a water and fertilizer required to produce it. Following these practices can increase the amount of time that a piece of land is arable and can help maintain the quality of the ecosystem in which the farm is situated. Unsurprisingly then, drones are not the only type of autonomous robots that are of growing interest to those in agriculture.
Take, for instance, the driverless tractor, which, unlike the driverless cars we are used to now, are completely autonomous. Using GPS and other precise sensors, autonomous tractors are able to seed and harvest crop, spray and even communicate with other autonomous tractors. Not to mention, driverless tractors are loaded with cameras made to further analyze the quality of the crops that surround it. Such technology can have a huge impact on the profitability of a farm over time.
But these are just a few of domains where farmers can start to automate their tasks. There are other autonomous farming tools that milk cows, pick strawberries, sort produce, weed fields and more. It is evident, then, that there are plenty of agricultural robots on the market. But most, are still too expensive for most to use. Yet, if they lower in costs as has happened with drones, it is likely that more farmers will adopt these practices automation.
So what might be the end goal be? If each autonomous component of a farm can analyze the data it produces and communicate with each autonomous devices, then it is likely that we’ll see farms that can run mostly autonomously in the future. They’ll be able to regularly scan for issues, diagnose them and then dispatch the appropriate resources to fix them without ever needing a human to tell them to. Not only should this lower costs of labor, but it should, in turn, hopefully lower costs of food at the supermarket while simultaneously use fewer resources.
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