At the moment there are only a few yield data at field level that can be analyzed using machine learning and satellite data. Who works with yield data for precision farming ? Would it make sense to share our data to have a common database for the development of new methods ?
It would be advantageous to have a common data set which covers several climate zones or crop types. I look forward to suggestions and answers.
@Michael, yes common data set covering entire crop types and climatic zones as well as soil types are very advantageous. For the development of new methods the common data set makes sense. However , precision agriculture combined with modern information and communications technologies, is becoming more common in agricultural activities now-a-days. Crop management data analysis, based on machine learning and data mining, focuses mainly on how to efficiently forecast and improve crop yield. Raw and semi-processed agricultural data are usually collected using sensors, robots, satellites, weather stations, farm equipment, farmers and agribusinesses while the Internet of Things (IoT) should deliver the promise of wirelessly connecting objects and devices in the agricultural ecosystem. Agricultural data generally captures information about farming entities and operations. Every farming entity encapsulates an individual farming concept, such as field, crop, seed, soil, temperature, humidity, pest, and weed. Agricultural datasets are spatial, temporal, complex, heterogeneous, non-standardized, and very large. In particular, agricultural data is considered as Big Data in terms of volume, variety, velocity and veracity. Designing and developing a data warehouse for precision agriculture is a key foundation for establishing a crop intelligence platform, which will enable resource efficient agronomy decision making and recommendations. Some of the requirements for such an agricultural data warehouse are privacy, security, and real-time access among its stakeholders (e.g., farmers, farm equipment manufacturers, agribusinesses, co-operative societies, customers and possibly Government agencies). Unfortunately, currently there are very few reports in the literature that focus on the design of efficient data warehouses with the view of enabling agricultural big data analysis and data mining.
Good discussion initiated . The data sharing for different climaic always improve the precision in the development of any methodology and contribute to the bringing out of new robust technology...
What if we defined a common standard and put our yield data online. I only mean the yield data for a field.The next step would be to add appropriate measurements. Yield data can be per field or directly from the combine harvester. It is important that the field is georeferenced so that weather and satellite data can be downloaded.
We know what each of the data points represents. We can presume that the number of data samples per unit of field are reasonably similar. The bigger difference to my mind is the human factor. Data standardization is the long-term solution but, is a long and slow process to get there. While data standardization is in process, as a stop-gap temporary solution a simple way to promote a method that by its nature promotes uniformity. By looking through the telescope of data standardization from its opposite end, I see.....data templates designed for the purpose in wide distribution and data warehouses that only accept data from a given template for addition to a given database.
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