Understanding The Variation In Harvester Yield Map Data For Estimating Crop Traits

Our recent exploratory research involves extracting tangible crop traits from images such as yield-maps. Research by Diepeveen et al (2012) demonstrated that genetic information can be extracted from near-infrared (NIR) images using both the implicit knowledge of data, environmental data and using a multivariate approach. Yield map data is geo-referenced data of grain-yield that is generated by a harvester cutting the crop, threshing the straw and extracting the grain. Our results show that the yield-map data has significant issues associated with it. One issue is the delay from time of cutting the crop to entering into the storage-bin for measurement. This is dependent on speed of the harvester and is compounded with maintaining a critical volume going through the harvester to operate efficiency. There are also issues with variation from the density and plant size of the crop within the paddock being harvested. Our preliminary results just highlight the significant challenges in extracting precise crop traits from yield map data.