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# A Comparative Study of Pointcloud-based Row Detection Algorithms in Different Simulated Agricultural Scenes

Florian Gaurier, Johann Laconte, Dimia Iberraken, Roland Lenain

► To cite this version:

Florian Gaurier, Johann Laconte, Dimia Iberraken, Roland Lenain. A Comparative Study of Pointcloud-based Row Detection Algorithms in Different Simulated Agricultural Scenes. WORLD FIRA 2023 - Forum international de la robotique agricole en action, Feb 2023, Toulouse, France. 2024. [hal-04873641](https://hal.science/hal-04873641v1)

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https://hal.science/hal-04873641v1

Submitted on 8 Jan 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# A Comparative Study of Pointcloud-based Row Detection Algorithms in Different Simulated Agricultural Scenes

Florian Gaurier, Johann Laconte, Dimia Iberraken and Roland Lenain
INRAE, UR 1471 TSCF, Aubière, France.

## Context

**Agricultural robotics** is identified as a suitable alternative in the **agroecological transition**, offering a promising solution to increase production rates while alleviating the workload on farmers and reducing the environmental impacts. To achieve these goals, crop monitoring, robot navigation system, spraying and other applications require the **crop rows** to be detected precisely and efficiently. With its high precision and effective range, the **lidar sensor** is a promising choice for **environment recognition**. Even though row detection has been widely investigated for camera applications, lidar-based row detection algorithms need to be compared. Using procedural generation for simulated environments, this study provides a benchmark and methodology to evaluate lidar-based crop row detection algorithms.

**Keywords:** Agricultural Robotics, Crop Row Detection, Pointcloud Processing, Automatic Environment Generation.

## Lidar-based Row Detection Algorithms

### Shape matching algorithm

<figure><img src="image_3.png" /></figure>

<table><thead><tr><td>Parameters / Plant type</td><td>Vine</td><td>Maize</td><td>Bean</td></tr></thead><tbody><tr><td>Correlation threshold</td><td>0.35</td><td>0.35</td><td>0.15</td></tr><tr><td>Model height [m]</td><td>1.5</td><td>0.4</td><td>0.05</td></tr><tr><td>Model width [idx]</td><td>50</td><td>10</td><td>5</td></tr></tbody></table>

**Principle**

A pattern (red) is matched inside successive LiDAR scans using the local maxima of the Pearson correlation score.

### Histogram algorithm

<figure><img src="image_4.png" /></figure>

**Principle**

Histogram is computed and filtered on the lateral axis of the robot, generating the global shape of the rows.

<table><thead><tr><td>Parameters / Plant type</td><td>Vine</td><td>Maize</td><td>Bean</td></tr></thead><tbody><tr><td>Interrow distance [m]</td><td>1.2</td><td>0.4</td><td>0.2</td></tr><tr><td>Peak height [%]</td><td>1.0</td><td>1.0</td><td>1.0</td></tr><tr><td>Filter length [idx]</td><td>3</td><td>3</td><td>3</td></tr></tbody></table>

### Iterative RANSAC algorithm

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

**Principle**

Multiple lines, parallel to the robot's heading (up to a maximal angle deviation), are to be matched in the 3D map using the RANSAC algorithm.

<table><thead><tr><td>Parameters / Plant type</td><td>Vine</td><td>Maize</td><td>Bean</td></tr></thead><tbody><tr><td>Threshold distance [m]</td><td>0.6</td><td>0.15</td><td>0.05</td></tr><tr><td>Max angle deviation [°]</td><td>5.0</td><td>2.0</td><td>2.0</td></tr></tbody></table>

## Generation of Simulated environments

**Principle**

Individual plants meshes are used to generate rows with specified parameters, enabling fast and massive data acquisition.

<figure><img src="image_7.png" /></figure>

## Evaluation and Results

### Metrics

<figure><img src="image_8.png" /><figcaption>Example of algorithm output
Summer vine</figcaption></figure>

For each row and algorithm :

**Precision** - distance between algorithm output (points) and associated row.

**Detection rate** - number of detected rows in the output over the number of rows in the input.

### Precision

Results are computed on the central row, only inter-row distance variation is displayed.

<figure><img src="image_9.png" /></figure>

**Maize:**

**Advice:** position the lidar to be able to see ground between each row.

**What algorithm?** Histogram due to less computation and same precision as RANSAC.

<figure><img src="image_10.png" /></figure>

**Bean:**

**Advice:** seed at an inter-row > wheel footprint + 2.5cm + full grown bean size to avoid any damage to crops.

**What algorithm?** Histogram due to less computation and same precision as RANSAC.

<figure><img src="image_11.png" /></figure>

**Vine:**

**Advice:** Variation is due to big foliage, try to position the LiDAR to detect more the trunks.

**What algorithm?** RANSAC if you have lot of points! Do not use Shape-matching for low inter-row: best precision is due to the detection of multiple rows at once.

### Detection rate – Shape-matching algorithm

Results are computed on the central row.

<figure><img src="image_12.png" /></figure>

**Interplant variation:**

Small impact, drops mainly explained by overlap (or gaps for vine) between crops.

<figure><img src="image_13.png" /></figure>

**Interrow variation:**

Impact at lower interrow due to overlaps. Side-rows disturb the detection of the main row.

## Perspectives and future work

Future works will introduce more parameters in the study, beginning with the impact of seasonality (winter vines), sensor noise and crops alignment using the **CropCraft** tool, leading to a paper describing precisely these results. Simulation results are also to be confirmed with real experiments.

Institut national de recherche pour l'Agriculture, l'Alimentation et l'Environnement

**Contact:**

Florian Gaurier – florian.gaurier@inrae.fr

Johann Laconte – johann.laconte@inrae.fr

Roland Lenain – roland.lenain@inrae.fr

INRAE Centre Clermont-Ferrand - UR 1471 TSCF

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