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Precision Agriculture

What is Precision Agriculture?

Precision agriculture refers to the management of farms with satellite and other data and technologies. The resulting maps provide highly detailed information about the topology and condition of farms, down to the individual plants.

Other common terms for precision agriculture include satellite farming and site-specific crop management.

Why Is It Important to Have a Good Precision Agriculture System?

Precision farming enables farmers to water crops, apply fertilizer, and monitor growth with highly specific accuracy. When farmers manage their farms more effectively, they can save on maintenance costs, fertilizer use, and increase yields at the same time.

Further, when farmers use IoT-enabled devices, they can collect and analyze data in real-time while irrigating crops, spraying fertilizer, and otherwise empowering technology to manage crop health.

What Internal Data Should I Have for a Good Precision Agriculture System?

To create their highly detailed maps, precision agriculture systems rely on the farmer’s crop and property boundaries. The system should also have crop data so it can recognize desired plants from undesirable.

Additionally, for security reasons, the system should include employee identification data.

What External Data Is Essential for a Good Identification System?

Of course, a good precision agriculture system uses GPS and satellite data. Sensors should also use multi-spectral or hyper-spectral imaging methods to identify topology and crops.

While hyper-spectral imagery is considered more accurate, it is also more expensive. The agricultural industry therefore has a longer history of using multi-spectral imaging technologies and many agriculture systems are designed for their use.

What External Data May Prove Useful for a Good Identification System?

Additional external data depends on the farmer’s resources and interests. While almost all farmers collect data on soil quality, not all of them require the same data. For example, some crops require more careful monitoring of humidity in the air than others; those farmers would consider this data essential.

Other useful data can come from any IoT-connected devices in the field. However, these precision agriculture systems can also integrate with device programming to help them perform their duties better. For example, smart irrigation systems that contain or communicate with sensors that monitor water levels in the soil or in individual plants use GPS data to make sure that each individual plant in an industrial farm receives exactly the amount of water it needs to thrive.

What Are the Main Challenges of this Use Case?

The main challenges of a good precision agriculture system include price and management of the system (as a whole and of the individual connected devices which can be damaged or stolen). In particular, new technological advances offer similar results through different means, and it may not be clear which system to use. And while these technologies have trended to the cheaper over the past few years, price remains a major concern.

Finally, management of this system, which includes cybersecurity, requires data analysis and tech skills that some farmers may find difficult to acquire.

Interesting Case Studies and Blogs to Look Into

Digital Commons: Four Case Studies in Precision Agriculture
Nature: Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production

Tangible Examples of Impact

Tractors optimized with GPS can help farmers deliver more exact inputs using the boom nozzles. The technology can even automatically adjust the amount needed when making turns. With GPS and cloud processing, the nozzles can stop spraying once the area has received its allotment.

Fast Company: Smart farming: The growing role of precision agriculture and biotech

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