Method of estimating crop yields

1. A method of estimating the instantaneous crop yield ŷ 2 (t) at time t in a field, during harvesting with a harvesting machine that operates by cutting and/or collecting the crop and has a crop mass flow sensor that measures the mass flow rate {dot over (m)} out (t) of crop processed by the machine at a location remote from that at which the harvesting machine cuts the crop and generates a signal {dot over ({circumflex over (m)} out (t) indicative thereof, the method comprising the steps of:

(i) generating a said mass flow rare signal {dot over ({circumflex over (m)} out (t) using said mass flow sensor; and

(ii) generating an area rate signal ŝ(t) indicative of time domain variations in the area of the field harvested per time unit;

characterized in that it comprises the further steps of:

(iii) filtering said area rate signal ŝ(t) using a function P(s) representative of the dynamics of the harvesting machine to generate a filtered area rate signal ŝ 2 (t); and

(iv) using said filtered area rate signal ŝ 2 (t) for deriving from said mass flow rate signal in {dot over ({circumflex over (m)} out (t) a yield per area unit ŷ 2 (t).

2. A method according to claim 1 , characterized in that the instantaneous yield ŷ 2 (t) is estimated according to the expression:

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3. A method according to claim 1 , characterized in that it includes the further step of (v) shifting the resulting estimated yield value ŷ 2 (t) by a time interval Δt representative of a delay between cutting and/or collecting of the crop by the harvesting machine and measurement of the mass flow rate by the crop mass flow sensor.

4. A method according to claim 3 , characterized in that it includes the further step of (vi) mapping the time shifted crop yield values for a range of values of t whereby to obtain a yield map of a field or part thereof.

5. A method according to claim 3 , characterized in that the steps (ii)–(iv) are carried out in real time by a computer carried by or operatively connected to the harvesting machine.

6. A method according to claim 1 , characterized in that it includes the step of (vii) recording a series of crop yield values in a memory.

7. A method according to claim 4 , characterized in that it includes the step of (vii) recording a series of crop yield values in a memory, wherein the mapping step (vi) takes place offline from said computer using the crop yield values stored in said memory.

8. A method according to claim 1 , characterized in that the function P(s) includes functions F 1 (s), F 2 (s) and F 3 (s) that respectively model the dynamics of different subsystems in the forward path of crop via the harvesting machine.

9. A method according to claim 1 , characterized in that it includes the further steps of:

(a) monitoring a machine condition;

(b) automatically adapting the function P(s) to a changing machine condition.

10. A method according to claim 9 , characterized in that said machine condition is a machine setting.

11. A method according to claim 10 , characterized in that said machine condition is chosen from threshing drum or cleaning fan speeds, concave setting or sieve setting.

12. A method according to claim 9 , characterized in that said machine condition is a distribution of crop flow over the machine.

13. A method according to claim 12 , characterized in that said monitoring step includes monitoring a crop flow at a particular location of the machine.

14. A method according to claim 12 , characterized in that said machine condition is the ratio (ρ) of a return crop flow to a full crop flow.

15. A method according to claim 9 , characterized in that the sub-step of automatically adapting the function P(s) includes:

(c) filtering the area rate signal ŝ(t) using a function that simulates the influence of the machine dynamics at zero return flow; and, as necessary.

(d) simulating the influence of a return flow ratio ρ.

16. A method according to claim 1 , characterized in that the function P(s) includes a function R(s) that models the flow of incompletely processed crop, via a return path defined in the harvesting machine, for further processing.

17. A method according to claim 16 , characterized in that the function R(s) is substantially non-dynamic.

18. A method according to claim 17 , characterized in that it is carried out using a particular algebraic solution of R(s) that is based on a discrete approximation of the proportion of the crop in the harvesting machine that flows via said return path.

19. A method according to claim 1 , characterized in that it includes the step of (viii) repeating the method a plurality of times at a sampling frequency of 1 Hz.

20. A method according to claim 1 , characterized in that it includes the further steps of:

(ix) assessing whether variations in the flow of crop through the harvesting machine derive predominantly from time domain variations in the area of the field harvested at time t; and

(x) carrying out said method if the result of said assessment is affirmative.

21. A method of estimating the instantaneous crop yield in a field during harvesting using a harvesting machine that operates by cutting and/or collecting the crop and has a crop mass flow sensor that measures the mass flow rate {dot over (m)} out (t) of crop passing through the harvesting machine and generates a signal {dot over ({circumflex over (m)} out (t) indicative thereof, the method including the steps of:

(xi) assessing whether variations in the flow of crop through the harvesting machine derive predominantly from time domain variations in the field conditions; and

(xii) if the result of said assessment is affirmative, generating a said mass flow rate signal {dot over ({circumflex over (m)} out (s) using said sensor, filtering said signal using a function P −1 (s) that models the inverse dynamics of the flow of crop through the harvesting machine and additionally filtering said signal using a function F(s) that is a low pass filter to generate an estimate ŷ 1 (t) of the instantaneous crop yield at time t.

22. A method according to claim 21 , characterized in that ŷ 1 (t) is determined by the expression:

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wherein ŝ(t) is the measured field surface harvested at instant t.

23. A method according to claim 21 , characterized in that the cut-off frequency of the low pass filter F(s) is 0.2 Hz.

24. A method according to claim 21 , wherein if the result of said assessment is negative then the method includes the further step of shifting the resulting estimated yield value ŷ 2 (t) by a time interval Δt representative of a delay between cutting and/or collecting of the crop by the harvesting machine and measurement of the mass flow rate by the crop mass flow sensor.

25. A harvesting machine that operates by cutting and/or collecting crop and has a crop mass flow sensor that measures the mass flow rate of crop processed by the machine at a location remote from that at which the harvesting machine cuts and/or collects the crop, wherein a computer is programmed to carry out a method comprising the steps of:

(i) generating a said mass flow rate signal {dot over ({circumflex over (m)} out (t) using said mass flow sensor; and

(ii) generating an area rate signal ŝ(t) indicative of time domain variations in the area of the field harvested per time unit;

characterized in that it comprises the further steps of:

(iii) filtering said area rate signal ŝ(t) using a function P(s) representative of the dynamics of the harvesting machine to generate a filtered area rate signal ŝ 2 (t); and

(iv) using said filtered area rate signal ŝ 2 (t) for deriving from said mass flow rate signal {dot over ({circumflex over (m)} out (t) a yield per area unit ŷ 2 (t).

26. A harvesting machine that operates by cutting and/or collecting the crop and has a crop mass flow sensor that measures the mass flow rate of crop processed by the machine at a location remote from that at which the harvesting machine cuts the crop, wherein a computer is programmed to carry out a method including the steps of:

(i) assessing whether variations in the flow of crop through the harvesting machine derive predominantly from time domain variations in the field conditions; and

(ii) if the result of said assessment is affirmative, generating a said mass flow rate signal {dot over ({circumflex over (m)} out (s) using said sensor, filtering said signal using a function P −1 (s) that models the inverse dynamics of the flow of crop through the harvesting machine and additionally filtering said signal using a function F(s) that is a low pass filter to generate an estimate ŷ 1 (t) of the instantaneous crop yield at time.

27. A method according to claim 21 , characterized in that if the result of said assessment is negative, that the method further comprises:

(i) generating a said mass flow rate signal {dot over ({circumflex over (m)} out (t) using said mass flow sensor; and

(ii) generating an area rate signal ŝ(t) indicative of time domain variations in the area of the field harvested per time unit;

characterized in that it comprises the further steps of:

(iii) filtering said area rate signal ŝ(t) using a function P(s) representative of the dynamics of the harvesting machine to generate a filtered area rate signal ŝ 2 (t); and

(iv) using said filtered area rate signal ŝ 2 (t) for deriving from said mass flow rate signal {dot over ({circumflex over (m)} out (t) a yield per area unit ŷ 2 (t).