Filtering – Cleaning Yield Maps

In the previous post, a fairly general picture of yield sensors and associated data was drawn. Here we return to a recurring question related to yield data: How can we ensure that yield data are reliable enough to be used properly? We will therefore review the main sources of uncertainty in these data sets; and discuss some of the methods that have been proposed to address this issue. We will not go into detail on all of these methods, as that would be far too tedious, but the bibliography is still available for those interested!

Why filter yield maps ?

Yield  maps  have  been  extensively  recognized  as  a  valuable  source  of  information  for  field  decision making  (Diker et  al.  2004;  Florin  et  al.  2009;  Pringle  et  al.  2003). They  effectively  provide  a  global overview of the field spatial variability which makes it interesting to target areas or zones for variable rate management. As a combine harvester passes through a field, yield monitors acquire almost in real -time multiple yield measurements all over the field. At the same time, those data are associated with the GNSS positioning of the machinery which enables precise location of each one of these observations at the within-field level. As such, thousands of yield spatial observations are generated and are ready to be used  in  the  decision-making  process. While this  considerable  volume  of  data  is  critical  for  field management  and  decision-making,  these  datasets  must  be  used  with  great  caution.  They effectively contain lots of defective observations or technical errors that need to be removed to ensure data quality (Arslan  and  Colvin,  2002;  Blackmore  and  Moore,  1999). It is important to understand that considering these faulty observations as “errors” is actually a bit wrong. Rather, they are observations that do not correspond to the true yield observed in the fields and that could have been expected from the cropping itinerary. If we wanted to be precise, we would have to say that it is the data acquisition process (with an embedded sensor on the combine harvester) that leads to the generation of data that are sometimes not consistent with reality. It is not the sensor itself that makes a mistake (although this can happen) but the fact of having an on-board system that conditions the way the data is acquired. In the rest of this post, I’ll talk about “yield errors” because it’s simpler but you’ll have understood (I hope) that it’s a simplification of reality.

As  a  consequence,  yield  datasets  are  often severely fil tered to make sure further analyses are not flawed (Robinson and Metternicht, 2005; Sudduth and Dummond, 2007; Sun et al. 2013). Several authors have described to what extent a yield map could evolve after removing abnormal values (Simbahan et al. 2004; Su dduth and Dummond, 2007). Griffin et al. (2008) have even shown that these latter observations were able to influence field management decisions. However, be careful because the article by Griffin et al. (2008) remains quite qualitative. There aren’t really any studies that have really looked very thoroughly at the impact that faulty observations can have on yield mapping. If it were up to me, I would tend to say that it all depends on what you want to do with the yield data. If you want to have average yield information at the plot level, or if you want to have large yield trends in the plots, a fairly simple filtering method should be sufficient and give fairly conclusive results. However, if you want to go into more detail, for example, to validate experimental results or to modulate inputs precisely, you should work better with slightly more advanced and robust methods. On the other hand, one should never assume that data cleaning will be perfect! The expertise of the field, be it that of the farmer, his advisor or an experimentation leader, who knows the plot, is essential.

Typology of yield errors

These technical errors or defective observations have been largely documented in the literature. Lyle et al. (2013) have  proposed  a categorization of those latter errors into four major groups: (i) harvesting dynamics of the combine harvester, (ii) continuous measurements of yield and moisture, (iii) accuracy of  the  positioning  system  and,  (iv)  harvester  operator .  These  technical  errors  are  briefly  described hereafter,  in  the  previously  defined  order,  along  with  methodologies  that  have  been  proposed  by  the scientific community to identify these defective observations.

• The harvesting dynamics of the machine includ e three different offsets, referred to as the lag time,  filling time  and emptying time  (Blackmore  and Moore,  1999). The lag time  induces an offset between the actual and the true location in space of a yield observation because the yield is not measured si multaneously with the cutting of the crop. Some attempts have been made to determine  this  offset  through  (i)  geostatistical  methods  (Chung  et  al.,  2002),  (ii)  image processing techniques (Lee et al. 2012) and (iii) signal deconvolution (Arslan, 2008; Reink e et al. 2011). The filling time at the start of a harvest pass leads to an under-estimation of the yield because  the  grain  flow  is  increasing  and  still  has  not  reached  a  plateau,  i.e.  the  permanent regime. Therefore, yield measurements do not match the ex pected true yield values. At the end of a harvest pass, some grain might still continue to flow after the last crop was harvested and the lag time has been reached. As a consequence, the latest observations of a harvest pass are generally under-estimated. The methods that have been proposed so far are exclusively visual, i.e.  the  grain  flow  is  plotted  against  the  travel  time  or  distance  of  the  machine  and  the  data located before or after the plateau are removed (Lyle et al. 2013; Simbahan et al. 2004).

• Continuous  measurements  relate  to  yield  and  moisture  observations.  So  far,  studies  have focused on thresholds, mostly determined empirically, to identify measurement errors (Sudduth and  Drummond,  2007;  Taylor  et  al.  2007).  Arslan  and  Colvin  (2002)  have  repor ted  sensor accuracies varying between 1 and  4% while  other authors  have  found differences up to 10% depending  on  environmental  conditions  during  data  acquisition,  e.g.  steep  slopes  (Reitz  and Kutzback, 1996). To overcome that issue, a couple of studies hav e focused on the impact of the combine harvester vibrations on the yield measurement accuracy (Hu et al. 2012; Jingtao and Shuhui, 2010).

• The accuracy of the positioning systems can lead to (i) observations outside field boundaries, (ii) measurements at the same spatial location, i.e. co-located points, or (iii) deviations in space according to a  predefined harvest pass (Blackmore  and  Moore,  1999). The two first types of errors are easily handled by removing the points outside the boundaries of the field or points with  similar  co-ordinates  (Robinson  and  Metternicht,  2005;  Simbahan  et  al.  2004).  Some algorithms have been implemented to reconstruct precisely the harvest passes by studying the angles formed by consecutive points (Lyle et al., 2013). Suspicious points – those the combine harvester is not likely to have gone through – are removed from the dataset.

• Last type of errors has to do with the harvester operator. First, large variations in speed are likely to  have  a  major  impact  on  the  yield  dataset  qu ality  (Arslan  and  Colvin,  2002;  Sudduth  and Drumond, 2007). Speed issues are generally processed the same way as yield and moisture, i.e. by setting thresholds to the whole dataset or only to neighbouring data (Lyle et al. 2013). The harvester operator is also likely to overlap consecutive or adjacent harvest passes which may result in yield measurement errors. Some authors have focused on this ‘not fully used cutting bar’ effect and have come up with vector-based  pre-processing  methods  to take into account  these overlaps, mainly by reconstructing harvesting polygons (Drummond et al., 1999). These vector-based methods are heavily dependent on the positioning accuracy of the GNSS device and  require  a  large  processing  time.  Other  authors  have  proposed  specific on-board  systems, such as those based on ultrasonic sensors (Zhao et al. 2010). Finally, harvest turns and headlands are also responsible for bad yield estimates (Lyle et al. 2013). Studies dedicated to these last sources of errors – though limited in the literature – have focused on finding the points inside harvest  turns  or  headlands  by  using  distance  or  angle  measures  between  consecutive  points. Suspicious points are removed.  On-board  sensors  such  as  yield  monitors  generate  an  extremely  large  amount  of observations.

You will find some of the typologies of yield errors on the following figures:

Figure 1. Yield Map

Figure 2. Yield map with errors annotated.

This considerable volume of observations requires the filtering approaches to be at the same time automated, very general and non-parametric (Simbahan et al. 2004; Spekken et al. 2013). The automation condition is fundamental with regard to the increasing size and number of yield datasets to process. For instance, it would not be conceivable for an operator or advisor to spend time on the correction of hundreds of possible within-field yield maps. General and non-parametric detection methods a re also to be preferred because of the diversity of datasets that have to be processed. These datasets are effectively acquired through a variety of acquisition systems – machines, sensors – and on multiple crops, with different operators and under  varying conditions of acquisition,  e.g.  topography or  climate. It is therefore important to make sure that the approaches are able to deliver conclusive results whatever the dataset to be  analysed.  Even though new operating systems exist to improve the quality  of yield  datasets,  e.g. ultrasonic sensors (Zhao et al. 2010), it can be argued that all the actual combine harvesters are far from being equipped with it.  General methods are  therefore  also  required  to  process  datasets  arising  from multiple types of machines, whatever the level of additional equipment installed. It must be kept in mind that  agronomic  datasets  are  often  included  in  complex  processes  of  field  management  and  decision -making, and are sometimes used as inputs in agronomic models. Data filtering methods have therefore to be robust enough so that the decision-making process is accurate and not flawed. A limitation of the actual literature is that most of the existing approaches are semi-automatic and rely on expert thresholds and filters. These last aspects might be problematical for the processing of yield maps at a larger scale as filtering settings can be influenced by each map producer and as skilled operators might be required for a considerable amount of time (Spekken et al. 2013). Once again, the end user will have control over the processing resulting from an automated method, and will be able to consider whether or not the processing appears relevant to him or her. For interested readers, a filtering method attempting to meet these constraints as much as possible has been proposed in the framework of my thesis (Leroux et al., 2018).

Some additional elements

So far, we have focused on various yield errors, but with the assumption that the yield information collected is still mostly of good quality (otherwise, if everything was bad, how could we consider some data to be removed and others to be kept). That is, we assumed that the yield monitor was initially well calibrated…. The calibration of the yield (and humidity…) sensor is very important to be sure that the yield values obtained can be used as they are, i.e. in absolute values. If the sensor is badly calibrated, nothing tells us that the values are those expected; on the other hand one can still make the assumption that the sensor will not reverse the observed yield trends (in other words that it will not consider a low yield as strong and vice versa). All this to say that even badly calibrated, a yield sensor should still be able to reproduce fairly faithfully the main yield trends in the plot, even if these values may be false in absolute terms. Optimally, it should be possible to calibrate the sensor every day (in view of changing acquisition conditions, such as humidity for example) and when the type of plants harvested changes. This is unfortunately difficult to do from an operational point of view; but calibration of the yield sensor should be done correctly at least once at the beginning of the harvesting season. One could also imagine correcting the yield map in absolute terms from a reference yield value at the plot level, for example the one obtained at the exit weighing of the plot (if the weighing is done for each plot). This could be a way to compare the average “true” yield at the plot level and the average yield obtained with the yield data. Be aware, however, that the calibration error may not be linear over the entire range of yield values (i.e., the yield error may be greater for high yield values than for low yield values). Also be aware that correcting with a mean value does not take into account the variance of the yield that could be expected in the plot.

The majority of the yield maps are presented in the form of point data. However, keep in mind that yield information is really an area, the area given by the speed of the harvester and its cutting bar. Add to this, if we want to be fussy, that when the plants are cut, it is the plants in front of the centre of the cutting bar that are brought into the harvester first, relative to the plants at the tails of the cutting bar. All of this can affect the actual weight to be given to yield observations. Getting into these considerations becomes extremely complex and one could question the relevance of going into so much detail. However, some research has gone as far as modeling the functioning of a combine harvester to take these aspects into account (Reinke et al., 2011). Since all combines are different, this approach unfortunately seems a bit too complex to be applied in the field. Finally, in relation to the modelling of combine harvester operation, I would like to come back to a point that we have left a little aside so far, the rethreshing of tailings (presented in the figure of the combine harvester in the previous post). To understand this phenomenon, we can imagine that at time ‘t’, a stock of grain enters the combine. In a perfect system, all the grain stock entering the combine at the same time would arrive in the grain tank at the same time. Unfortunately, some of this grain (not necessarily well threshed or sieved) remains in the harvester and is mixed with the grain stock(s) that continues to arrive at time ‘t+1’ for example. This phenomenon therefore raises questions about the yield weighting on each of the measuring points carried out since, in reality, the yield measured corresponds only to a portion of the grain actually harvested at a specific point in space. Can we make the hypothesis that this rethreshing of tailings remains more or less stable throughout the harvest and therefore that all observations would be affected in the same way? It could be worth checking… That’s the hypothesis we’re making, anyway.

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