Data Gathering Tools to Make the Right Crop Management Decisions
We’ve been hearing a lot about data in agriculture lately. It’s a popular topic at every conference and many ideas are flowing: How do you get your data out of your field? What type of sensors do you use? Do you use drones? How’s your app working?
This is an exciting time to be in agriculture. Not only are there many new ideas leveraging actual data for better decision making, but those ideas are coming at us quickly. Almost too quickly! Unfortunately we don’t have all the right data for a number of reasons, so we still rely on what we know best—historical information, past best practices, and intuition. What’s coming that might help?
Companies, from startups to large agriculture service providers, are working on the data gathering problem. Out in fields today we are seeing networked sensor systems collecting soil conditions like temperature and moisture levels. Other systems collect soil chemistry alongside the temperature data. All this is fed back to the cloud through a wired or wireless network, run though algorithms, and returned to the grower via an app or dashboard. From this, data driven decisions can be made.
Other growers are starting to leverage more complete systems that include plant information. Drones with cameras and sensors fly over fields multiple times to help monitor growth, predict yield, and detect disease. The ability to collect data from an entire field, versus a portion collected by a human (which is then extrapolated), has greatly improved growers' knowledge about the condition of the plants.
So We’ve Got Drones and Sensors. Does that Cover it?
Not exactly. The sensor systems can’t always get the data pushed to the cloud. Cellular signals might not reach a location, or LoRa® network connections are disrupted by the topography of the land. Wired systems may prove cost prohibitive.
Drones add another layer of complexity. They need to be flown multiple times through fields by certified pilots. A cost of business, yes, and if it translates to better crop performance, then the ROI may pencil out. But does a drone always see everything? What about the undersides of crops that grow on trees like apples, pears or grapes. Strawberries grow layered on top of each other, often covered in plant leaves. What then?
Data Gathering Made Smarter by AI
Artificial intelligence has pushed crop sensing to a new level, and we've developed systems that collect data from ground level crop imagery and soil sensing, and then upload it to the cloud, eliminating the need to be always connected. A sensing platform can be added to a farm vehicle or driven autonomously, collecting data without the need for additional in-field work. Adding a bit of intelligence will only make the system better and more capable over time. With AI incorporated into the sensing platform and a significant amount of data being gathered, computations can start to be made on yield, and diseases can be identified earlier.
Our Principal IoT Systems Architect, Paul Ganichot, breaks it down in this new blog series that goes through the what, why, who, how, where, when of the “Information of Things.” In this first post, we’ll define the Internet of Things, decompose its main components, summarize the IoT ecosystem, and demystify buzzwords such as Digital Twin, Digital Engineering, and Virtual Sensor.
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Data in agriculture is flowing freely, which normally means a sensor network for soil information, and drone or visual inspection of plants from the air. Smart data gathering platforms will deliver the most complete data set that starts to unlock the mysteries of crop yield and disease detection.
Insights into the newest technologies in agriculture from industry experts at World Agri Tech Innovation Summit
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