Imagine a competition to produce the highest yields of winter wheat between Sheldon Cooper and a winner of the Apprentice. Who would win? It’s tempting to choose the Big Bang brain-box but what if Lord Sugar’s apprentice had spent 10 years working on arable farms in the UK, Australia and the USA before joining the house of hopefuls?
Precision agriculture poses similar questions. Is it better to have the deepest understanding of plant biology, soil chemistry and metrology or the widest? Is it better to have the most detailed mathematical model of plant growth or the most robust?
These questions got an interesting airing in a recent paper by scientists at CSIRO (Commonwealth Scientific and Industrial Research Organisation) published in Field Crops Research journal. Andre Colaco and colleagues considered the question of how to optimise the harvest of winter wheat by supplying nitrogen. They compared the performance of detailed advisory models that used single field sensors with other less detailed models, that used multiple sensor inputs from the field. What does this mean? One advice system for example, might be based on measuring nitrate concentrations in crop leaves and include a whole set of equations describing how nitrate ions move from fertiliser pellets on the soil surface into the roots, up the stems and into the leaves. Another might also use sensor measurements of temperature, humidity, rainfall, wind speed and hours of sunshine; but use only basic assumptions about transport of macronutrients.
Which approach is more successful – the deep one or the wide one?
Colaco and colleagues proposed on-farm experimentation and machine learning with multiple sensor inputs as a better way to apply artificial intelligence to crop management. They took 20 years of publicly available winter wheat data from Oklahoma State University (OSU) and used it to test different deep and wide approaches to advising how much nitrogen should be applied in mid-season to realise the potential yield of a crop of wheat. Four different approaches were tested, using half the historical test data as learning-sets and half as test-sets.
Their first approach was based on predicting a Yield Potential and then assessing the difference between the nitrogen content of that yield (i.e. kg of wheat per hectare) and the available nitrogen in the soil. The difference is the recommended nitrogen that must be applied to the field (in kg N per hectare). How were these two numbers calculated? Yield potential for wheat was measured by OSU using the so-called GreenSeeker sensor model. GreenSeeker is a handheld multispectral device that measures the NDVI (normalised differential vegetation index) of field crops. By comparing the NDVI response of field samples against a look-up table, an in-season estimated yield was obtained. Basically the greener the field test-strip, the bigger the expected yield. Farmers have been using simpler metrics like crop height to predict yield in a similar fashion for some years.
Nitrogen demand for such a yield was calculated using standard assumptions: nitrogen content of wheat is typically 2.4% by weight and the efficiency of nitrogen uptake is again typically 44% of that applied to a field. The nitrogen recommendation for Approach 1 was therefore equal to:
[(expected nitrogen in predicted yield) – (available nitrogen in the field) ] x uptake efficiency
The second approach was probably more appealing to chemists, using assumptions about the nitrogen response rate (i.e. the concentration of nitrogen multiplied by some rate constant driving the complex growth reaction) rather than a nitrogen mass-balance. With Approach 2, the NDVI values of wheat in different test strips were used directly as parameters for plant growth rate. The in-field experiments required mid-season measurements of wheat test strips with different levels of applied nitrogen at the start of the season and aimed to find the plateau NDVI value that corresponded to the maximum level of nitrogen that the plants could take up given the soil and climate conditions. NDVI measurements in this case were made with a Crop Circle sensor and converted to a recommended nitrogen application rate using a look-up table directly.
The third approach introduced Machine Learning (ML) to Approach 1. First a whole load of seasonal variables were introduced in to the yield prediction calculation as possible solutions to the variation that was seen in natural year-to-year variation in crop yield. The table below shows the type of variables taken into account.
A simple regression analysis was made to identify the most influential seasonal variables. Next a Machine Learning method known as Random Forest (RF) was used to investigate various decision-tree models (combinations of the most influential seasonal variables) that could possibly lead to an applied nitrogen recommendation at mid-season. There are some useful video links at the bottom of this Insight article that explain decision-trees and RF. It turned out that the most influential variables for Approach 3 were: NDVI, RI (response index), soil moisture and rainfall. The Random Forest trees were derived using half of the historic OSU winter wheat data and refined so as to create a Machine Learning model that could be used to predict the recommended nitrogen application for the remaining 50% of the historic OSU data.
The forth and final approach was to apply Machine Learning to Approach 2 and produce a model Colaco called Data Driven. For their Data Driven approach, all the available sensor data was added to Approach 2 so that a very wide range of information was used to find the most influential seasonal variables. This time all 12 of the variables in the above table were used for Approach 4. Again the Machine Learning Random Forest method was used to find a set of decision-trees that best represented the 50% learning set. This set of decision-trees was then used to predict the recommended nitrogen application for the remaining 50% of the historic OSU winter wheat data.
So after all this modelling and number crunching what was the result?
The performance of the four Approaches was evaluated by plotting the recommended nitrogen rate against the actual optimal nitrogen rate. An R2 value of 1.0 would give perfect goodness of fit and the RMSE root mean square error values were used as an indication of the accuracy of the Approach. Based on these criteria, Approach 4 is the clear winner.
- Approach 1 R2 = 0.42 and RMSE = 31.3 kg N ha-1
- Approach 2 R2 = 0.63 and RMSE = 21.9 kg N ha-1
- Approach 3 R2 = 0.51 and RMSE = 26.0 kg N ha-1
- Approach 4 R2 = 0.79 and RMSE = 16.5 kg N ha-1
Oklahoma State University winter wheat yields varied from 1 tonne per hectare to 7 tonnes per hectare and over the 20 years over all the fields in the database, the optimal mid-season nitrogen application based on actual yields varied between zero kg per hectare and 110 kg N per hectare. Reducing the error in nitrogen application from 31.3 to 16.5 kg N per hectare by using Approach 4 rather than Approach 1 is therefore a significant optimisation of nitrogen supplementation. This would be expected to result in lower costs (when Nrecommended is too high) and higher yields (when Nrecommended is too low).
Applying machine learning can improve the use of either direct or indirect parameters in precision agriculture. Using multiple variables that farmers encounter from year to year and from field to field can produce more robust advice, even when the variables are used directly, without knowing exactly how they affect the yield of a crop.
Smarts or knowledge? Precision agriculture gains from the use of both better understanding and knowledge. When machine learning is added to a method, on farm experiments and local variables like microclimate can produce the very best results.
Corbeau for one, can’t wait to see the results of applying this approach in UK vineyards as well as winter wheat in the USA.
- Find the whole article from Colaco in Field Crops Research
- Read a short related article on a similar study in Australia
- Watch a related video featuring one of the CSIRO team
- Watch a FUN explanation of Random Forest machine learning, yes really!
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