METHOD OF CONTROLLING AND MANAGING A PRODUCTION CYCLE OF A LIVESTOCK FARM

Abstract

A computer-implemented method of systematically controlling and managing a production cycle of a livestock farm housing a population of animals, the method comprising (a) obtaining real-time data by a plurality of, sensors and/or manual or machine-based measurement and evaluation devices, including a set of farm condition parameters, (b) establishing statistical correlations of animal status parameters and process parameters with the animal performance parameters; (c) calculating and automatically adjusting, depending on the farm condition parameters obtained in (a) and on the statistical correlations identified in (b), a set of data set points for farm operating parameters such that at least one of a selected one of the animal performance parameters is optimized; and (d) repeatedly conducting (a) to (c) until finishing the production cycle. A system for systematically controlling and managing a production cycle of a livestock farm housing a population of animals.

Claims

1. A computer-implemented method of systematically controlling and managing a production cycle of a livestock farm housing a population of animals, the method comprising: (a) obtaining real-time data by one or more sensors and/or manual or machine-based measurement and evaluation devices, the real-time data including at least the following farm condition parameters: (a1) animal status parameters indicative of an animal status; (a2) process parameters descriptive of a production process; and (a3) animal performance parameters indicative of a performance of the animals; (b) establishing statistical correlations of the animal status parameters and the process parameters with the animal performance parameters; (c) calculating and automatically adjusting, depending on the farm condition parameters obtained in (a) and on the statistical correlations identified in (b), a set of data set points for farm operating parameters such that at least one of a selected one of the animal performance parameters is optimized; wherein: the adjusting is performed with at least one controller and/or at least one actuator; and (d) repeatedly conducting (a) to (c) until finishing the production cycle.

2. The method according to claim 1, wherein the animal status parameters (a1) include at least one parameter selected from the group consisting of distribution and movement of animals within the farmhouse, motoric activity, current weight of animals, feed consumption, composition of the animal house’s atmosphere, and any combination thereof.

3. The method according to claim 1, wherein the process parameters (a2) include at least one parameter selected from the group consisting of environmental parameters, quantity, quality and composition of feed, supplements, water supply, temperature, air pressure, ventilation, lighting, sound, humidity, and any combination thereof.

4. The method according to claim 1, wherein the animal performance parameters (a3) include at least one parameter selected from the group consisting of animal health and mortality, caloric conversion rates, feed conversion rates, body weight gain, and any combination thereof.

5. The method according to claim 1, wherein the animal status parameters (a1) further include initial animal and environmental parameters not obtainable via sensors and/or measurement and evaluation devices.

6. The method according to claim 1, wherein the farm condition parameters further include: (a4) parameters of the condition of the farm; wherein: the parameters of the condition are at least one selected from the group consisting of carbon dioxide emission, ammonia emission, litter quality, aerial pathogen load, ground water quality, water-based waste emission level, and any combination thereof.

7. The method according to claim 1, wherein in (b), the statistical correlations of animal status parameters and process parameters with the animal performance parameters are empirically established frompast animal performance parameters from the farm and/or from the animal performance parameters following changes in the farm operating parameters in past situations.

8. The method according to claim 1, wherein in (b), the statistical correlations of animal status parameters and process parameters with the animal performance parameters are established with semi-reinforcement learning.

9. The method according to claim 1, wherein the calculating and adjusting in (c) is performed with a machine learning procedure operating on a neural network to iteratively adjust the set of farm operating parameters dependent on the obtained animal status parameters and the process parameters; and wherein the animal performance parameters and measured statistical correlations between the animal status and the process parameters are target parameters for training the neural network.

10. The method according to claim 1, wherein the calculating in (c) is performed with a combination of algorithms that are made available via an algorithm library.

11. The method according to claim 1, wherein the calculating in (c) is performed with a prediction engine including a genetic algorithm which optimizes selected animal performance parameters while minimizing losses on non-selected animal performance parameters.

12. The method according to claim 1, wherein the adjusting in (c) is performed with at least one actuator.

13. The method according to claim 1, wherein (c) is performed in a phased, regular or continuous manner.

14. A system for systematically controlling and managing a production cycle of a livestock farm housing a population of animals, the system comprising: (a) one or more sensors and/or manual or machine-based measurement and evaluation devices adapted to obtain at least the following farm condition parameters: (a1) animal status parameters indicative of an animal status; (a2) process parameters descriptive of a production process; and (a3) animal performance parameters indicative of a performance of the animals; (b) a first computing unit configured to establish statistical correlations of the animal status parameters and the process parameters with the animal performance parameters; (c) a second computing unit configured to calculate, depending on the farm condition parameters obtained by the sensors and/or measurement and evaluation devices in (a); and the statistical correlations identified by the first computing unit (b), a set of data set points for farm operating parameters such that at least one of a selected one of the animal performance parameters is optimized; and (d) a control unit adapted to automatically adjust the set of data set points for the farm operating parameters as calculated by the second computing unit.

15. The system according to claim 14, wherein the second computing unit is a prediction engine including a genetic algorithm configured to optimize selected animal performance parameters while minimizing losses on non-selected animal performance parameters.

Description

[0067] A specific embodiment of the present invention is depicted in FIG. 1. The system as a whole includes a real-world process layer, a measurement and monitoring layer, a prediction layer and an action layer. Animal status parameters (a1) and process parameters (a2) which influence the animal performance parameters (a3) are part of the real-world process layer. These parameters were obtained in the measurement and monitoring layer using sensors (incl. middleware), manual or machine-based measurement and evaluation devices or machine readings and inputted into a first computing unit configured to establish statistical correlations of animal status parameters and process parameters with the animal performance parameters. Said first computing unit may be embedded in a knowledge pool. Such knowledge pool further includes a library of algorithms as described above and optionally also means for obtaining scientifically “possible” results e.g. obtained from model systems, such as biological/physical models, as described above.

[0068] Based on the output of the first computing unit (i.e. the statistical correlations of animal status parameters and process parameters with the animal performance parameters), suitable algorithms are selected from the library of algorithms which are then applied in the second computing unit, being a prediction engine. The information processed by the prediction engine also includes business APPs, and optionally also assumptions about unmeasured variables. The output of the prediction engine is matched against scientifically possible results which then serve as input for a recommendation engine which outputs a set of data set points for farm operating parameters that are to be adjusted. Finally, those adjustments are put into practice through controllers and actuators. These process steps are repeated until the end of the production cycle.