# A Non-Invasive Millimetre-Wave Radar Sensor for Automated Behavioural Tracking in Precision Farming—Application to Sheep Husbandry

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## Abstract

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## 1. Introduction

## 2. Material and Methods

#### 2.1. Sheep

#### 2.2. Arena Test

- -
- In phase 1, the focal sheep could explore the arena for 15 s and see its conspecifics through a grid barrier;
- -
- In phase 2, visual contact between the focal sheep and the social stimuli was disrupted using an opaque panel pulled down from the outside of the pen for 60 s. This phase was used to assess the sociability of the sheep towards its conspecifics;
- -
- In phase 3, visual contact between the focal sheep and its conspecifics was re-established and a human was standing still in front of grid barrier for 60 s. This phase was used to assess the sociability of the focal sheep towards conspecifics in presence of a immobile human.

#### 2.3. Data Collection

#### 2.4. Video and Radar Tracking

#### 2.5. Extraction of New Behavioural Parameters Form the Radar Data

- 1: Behavioural classes;

- 2: Behavioural transitions;

- 3: Space coverage;

^{2}each (i.e., 16 partitions along the arena length and 5 partitions along the arena width). We chose this grid dimension because it is the width of a small lamb [36]. We counted the number of zones (i.e., the heatmap score) the focal sheep remained in for more than 200 ms. This count was used to extract behavioural features for the two last phases of the experiment.

#### 2.6. Outdoor Radar Tracking

#### 2.7. Statistical Analyses

- Analysis of new movement features

- Classification of behavioural types;

## 3. Results

#### 3.1. Radar Tracking Is Faster and More Accurate Than Video Tracking

#### 3.2. New Behavioural Indicators from the Radar Data

- Behavioural classes: detection of slow and fast movements

- Wavelet analysis: detection of erratic behavioural transitions;

- Heatmap analyses: Detection of spatial coverage

#### 3.3. Sheep Behavioural Phenotype

#### 3.4. Outdoor Radar Tracking

## 4. Discussion

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Corridor test. (

**A**) Top view of the focal sheep and the social stimuli in the corridor (example image extracted from video data). (

**B**) Schematic representation of experimental phases 1, 2 and 3. (

**C**) Image of the FMCW radar frontend (phot credit AD). Each rectangle corresponds to patch [22]. (

**D**) Example of a trajectory of a sheep obtained with radar tracking after removing the clutter and normalizing the estimated value. The red rectangle represent the pen walls.

**Figure 2.**Analyses of behavioural classes. (

**A**) Distribution of the four behavioural classes after a Gaussian Mixture Model. (

**B**) Frequency of behavioural classes during phase 2 and phase 3 of the corridor test. (

**C**) Correlation between the proportion of time spent in slow movements and the sociability score of sheep during phase 2 and 3 (see details of models in Table 3). (

**D**) Correlation between the proportion of time spent in slow movements and the docility score of sheep during phase 2 and 3. N = 58 sheep.

**Figure 3.**Wavelet analyses. (

**A**) Example of wavelet transform for lateral movements (X). Red dots correspond to the detection of a change in the displacement at scale factor and time position (i.e., a local maxima of the wavelet transform of the signal position). (

**B**) Example of wavelet transform for longitudinal movements (Y). (

**C**) Relationship between the number of local maxima (red dots in (

**A**,

**B**)) in the wavelet extraction and the degree of sociability of sheep during phases 2 and 3. (

**D**) Relationship between the number of wavelets and the degree of docility of sheep during phases 2 and 3. See details of models in Table 4. N = 58 sheep.

**Figure 4.**Heatmap analyses. Relationship between the numbers of areas occupied by the lambs and the degree of docility in phase 2 and phase 3. (

**A**) Resolution grid (cell dimension: 0.44 × 0.40 m). (

**B**) Relationship between the surface used by the sheep the degree of docility of sheep during phases 2 and 3. See details of models in Table 5. N = 58 sheep.

**Figure 5.**(

**A**) Correlations between the two first components (PCs) of the principal component analysis (PCA). Arrows represent the eight behavioural variables on PC1 (movement speed) and PC2 (movement increase between phases). Contribution of variables to the variance explained is color-coded. Each data point represents the PC1 and PC2 scores of a given lamb (N = 58). (

**B**) Relationship between PC1 and sociability. (

**C**) Relationship between PC2 and docility. Blue lines represent linear models (see main text). N = 58 sheep.

**Figure 6.**(

**A**) Picture of the outside corridor used for radar tracking of a sheep (credit AD). The radar was positioned 60 m from the end of the corridor. (

**B**) Example of trajectory of a sheep derived from the radar data. (

**C**) Example of trajectory of a sheep (red) and a man (green) derived from the radar data.

**Figure 7.**Summary of the method described in the study, from the behavioural test and the acquisition of the data with radar to the extraction of the new behavioural parameters form the trajectory data.

Name | Indoor Tracking | Outdoor Tracking | Note |
---|---|---|---|

Operating frequency | 77 GHz | 24 GHz | This frequency is also called the carrier frequency of the frequency-modulated signal transmitted by the radar |

Modulation Bandwidth | 3 GHz | 800 MHz | Frequency interval, centred at the operating frequency, used for the saw-tooth frequency modulation of the transmitted signal |

Ramp time | 256 µs | 1 ms | Up-ramp duration of the saw-tooth frequency-modulated signal (or chirp duration) |

Repetition time | 50 ms | 30 ms | Period of the transmitted frequency-modulated signal (or chirp repetition interval) |

Number of linear arrays of the transmitting antenna array | 4 | 1 | One linear array composed of 8 × 2 rectangular patches radiating elements |

Number of linear arrays of the receiving antenna array | 8 | 2 | Eight linear arrays composed of 8 rectangular patches radiating elements |

Main lobe beamwidth of the transmitting antenna array in the horizontal plane | 50° | 58° | Angular range (or field of view) of the radar illumination in the horizontal plane |

Transmitted power | 100 mW | 100 mW | Power delivered at the input terminals of the transmitting array antenna (the radiated power is defined as the product of the transmitted power by the efficiency of the antenna) |

Tracking Method | Radar | Video |
---|---|---|

Number of measures per second | 50 | 25 |

Read Only Memory (ROM) for all measures of a sheep | 151 Mo | 62 Mo |

Random Access Memory (RAM) per measure | 524 Kb | 3.7 Mb |

Processing time per measure | <20 ms | 250 ms |

Distance to target centre | 1.1 m | 1.5 m |

**Table 3.**Analyses of behavioural classes. Results of the best GLMM (binomial family, after model selection—see Table S1). The model tested the effects of phase, docility, sociability, and dual interaction of each variable with phase, on the proportion of time spent in fast movements (behavioural class 2). Lamb identity was included as a random factor. Significant effects (p < 0.05).

Estimate | Std. Error | z Value | Pr (>|z|) | |
---|---|---|---|---|

(Intercept) | 0.11 | 0.055 | 2.08 | 0.037 |

Sociability | 0.13 | 0.039 | 3.47 | <0.001 |

phase 3 | −1.24 | 0.0086 | −144.04 | <0.001 |

Docility | −0.11 | 0.047 | −2.43 | 0.015 |

sociability:phase 3 | −0.12 | 0.0061 | −19.90 | <0.001 |

Docility: phase 3 | 0.16 | 0.0074 | 21.31 | <0.001 |

**Table 4.**Wavelet analyses. Results of the best GLMM (Gaussian family, after model selection—see details in Tables S2 and S3). The model tested the effects of phase, docility, sociability, and binary interactions of each variable with phase, on the number of wavelets. Lamb identity was included as a random factor. Significant effects (p < 0.05) are shown in bold. Wavelet Y: longitudinal movements. Wavelet X: transversal movements.

Wavelet Y | Estimate | Std. Error | Df | t Value | Pr (>|t|) |
---|---|---|---|---|---|

(Intercept) | 514 | 6.64 | 110 | 77.3 | <0.001 |

sociability | 17 | 4.68 | 110 | 3.63 | <0.001 |

phase 3 | −91.5 | 9.07 | 55 | −10.1 | <0.001 |

docility | −3.12 | 5.72 | 110 | −0.545 | 0.587 |

Sociability:phase 3 | −14.4 | 6.4 | 55 | −2.25 | 0.05 |

Docility:phase 3 | 4.7 | 7.81 | 55 | 0.602 | 0.55 |

Wavelet X | Estimate | Std. Error | df | t Value | Pr (>|t|) |

(Intercept) | 467 | 6.04 | 110 | 77.3 | <0.001 |

sociability | 0.526 | 4.26 | 110 | 0.124 | 0.902 |

phase 3 | −53.2 | 8.26 | 55 | −6.43 | <0.001 |

Docility | −9.61 | 5.2 | 110 | −1.85 | 0.0673 |

Sociability:phase 3 | 7.36 | 5.82 | 55 | 1.26 | 0.212 |

Docility: phase 3 | 19.2 | 7.11 | 55 | 2.7 | <0.05 |

**Table 5.**Heatmap analyses. Results of the best GLMM (Gaussian family, after model selection—see details in Table S4). The model tested the effects of phase, docility, sociability, and dual interactions of each variable with phase, on the number of areas where the lamb spent more than 1 s. Lamb identity was included as a random factor. Significant effects (p < 0.05) are shown in bold.

Heatmap | Estimate | Std. Error | z Value | Pr (>|z|) |
---|---|---|---|---|

(Intercept) | 2.95 | 0.037 | 79.00 | <2 × 10^{−16} |

docility | −0.066 | 0.031 | 2.07 | 0.039 |

phase 3 | −0.77 | 0.053 | 14.27 | <2 × 10^{−16} |

sociability | 0.048 | 0.023 | 2.022 | 0.043 |

phase 3: docility | 0.099 | 0.046 | 2.15 | 0.032 |

phase 3: sociability | −0.020 | 0.038 | 0.52 | 0.60 |

**Table 6.**Eigenvalue for each component (PC) of the Principal Component Analysis using the eight behavioural features extracted using the radar tracking.

Component | Eigenvalue | Variance Explained |
---|---|---|

PC 1 | 2.893 | 30.65 |

PC 2 | 1.738 | 19.31 |

PC 3 | 0.974 | 13.04 |

PC 4 | 0.833 | 9.27 |

PC 5 | 0.564 | 7.20 |

PC 6 | 0.492 | 6.69 |

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**MDPI and ACS Style**

Dore, A.; Pasquaretta, C.; Henry, D.; Ricard, E.; Bompa, J.-F.; Bonneau, M.; Boissy, A.; Hazard, D.; Lihoreau, M.; Aubert, H.
A Non-Invasive Millimetre-Wave Radar Sensor for Automated Behavioural Tracking in Precision Farming—Application to Sheep Husbandry. *Sensors* **2021**, *21*, 8140.
https://doi.org/10.3390/s21238140

**AMA Style**

Dore A, Pasquaretta C, Henry D, Ricard E, Bompa J-F, Bonneau M, Boissy A, Hazard D, Lihoreau M, Aubert H.
A Non-Invasive Millimetre-Wave Radar Sensor for Automated Behavioural Tracking in Precision Farming—Application to Sheep Husbandry. *Sensors*. 2021; 21(23):8140.
https://doi.org/10.3390/s21238140

**Chicago/Turabian Style**

Dore, Alexandre, Cristian Pasquaretta, Dominique Henry, Edmond Ricard, Jean-François Bompa, Mathieu Bonneau, Alain Boissy, Dominique Hazard, Mathieu Lihoreau, and Hervé Aubert.
2021. "A Non-Invasive Millimetre-Wave Radar Sensor for Automated Behavioural Tracking in Precision Farming—Application to Sheep Husbandry" *Sensors* 21, no. 23: 8140.
https://doi.org/10.3390/s21238140