An article by Dimitrios Damianos, Technology and Market Analyst, Photonics & Sensing Division and Pierre Cambou, Principal Analyst, Photonics & Sensing Division, both from Yole Développement (Yole), for EETIMES –
Farmers are harvesting sensor data to shift from preventive to predictive agriculture.
Since its inception, the Industrial Revolution has centered on automating production processes. Now that we have entered the era of Industry 4.0, most industrial processes have become data-centric, generally involving five steps of data manipulation: collection, transmission, storage, analysis, and, finally, display. This last step is to keep humans in the loop, but data can also be fed back to some actuating device, bringing the process into the realm of robotics.
Agriculture has not been immune to industrialization over the past two centuries, and in recent years, Agriculture 4.0 has gained momentum. Just as industrial production made the transition toward data management, agriculture is now following that path. Companies that traditionally have served industrial segments now offer similar data-centric approaches to the agriculture sector, and we are even seeing agricultural-equipment manufacturers expand into industrial-equipment manufacture. Although agriculture is often characterized by an unstructured environment with respect to traditional industrial manufacturing industries, the versatility of new data-centric technologies is helping agriculture to become an industry that is piloted in the same manner as automotive or aerospace. The farmer has become an engineer like any other engineer.
It all started in the 1990s with the first automation equipment for the high-value dairy industry – primarily milking machines from the likes of Swedish manufacturer DeLaval and Netherlands-based Lely. At the same time, optical sorters for grains, particularly rice, were developed by companies such as Satake, headquartered in Japan, and Bühler, based in Switzerland. Some of these sorting techniques ended up in the field again for high-end agricultural products, such as vineyard grapes. Pellenc, in southern France, developed such robotic gear, which transformed farmers into data scientists.
Indeed, once automation was in place for this new generation of farmers, they had the opportunity to go the extra step, not just looking passively at their yield but acting proactively to improve the quality and quantity of their agricultural produce. Whereas the small-scale farming operations of the past could rely on the farmer’s eyes and intuition to monitor everyday activities, today’s gigantic farming operations can no longer rely on human senses. Data technology has become central to steering the farm in the right direction. Whether it is for herding, crop production, or high-end production such as wine, data is the focus of Agriculture 4.0.
Camera utilization in agriculture
One of the best examples of agricultural data management is the monitoring of fields using drones. Paris-based Parrot is a key player in that domain, largely thanks to its U.S. subsidiary, MicaSense. However, the French company announced in January that it had agreed to sell MicaSense to AgEagle Aerial, a U.S.-based data collection, analytics, aerial imaging services, and drone company, for US$23 million. MicaSense developed a camera that uses different wavelengths to compute normalized difference vegetation index (NDVI) maps, which have become the accepted way to monitor crop growth and spot problem areas. The state-of-the-art methodology is now to download the NDVI maps to tractors and thereby adjust the fertilizers delivered to the field.
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