Robotic Fertilisation Using Localisation Systems Based on Point Clouds in Strip-Cropping Fields

Abstract

The use of robotic systems in organic farming has taken on a leading role in recent years; the Sureveg CORE Organic Cofund ERA-Net project seeks to evaluate the benefits of strip-cropping to produce organic vegetables. This includes, among other objectives, the development of a robotic tool that facilitates the automation of the fertilisation process, allowing the individual treatment (at the plant level). In organic production, the slower nutrient release of the used fertilisers poses additional difficulties, as a tardy detection of deficiencies can no longer be corrected. To improve the detection, as well as counter the additional labour stemming from the strip-cropping configuration, an integrated robotic tool is proposed to detect individual crop deficiencies and react on a single-crop basis. For the development of this proof-of-concept, one of the main objectives of this work is implementing a robust localisation method within the vegetative environment based on point clouds, through the generation of general point cloud maps (G-PC) and local point cloud maps (L-PC) of a crop row. The plants’ geometric characteristics were extracted from the G-PC as a framework in which the robot’s positioning is defined. Through the processing of real-time lidar data, the L-PC is then defined and compared to the predefined reference system previously deduced. Both subsystems are integrated with ROS (Robot Operating System), alongside motion planning, and an inverse kinematics CCD (Cyclic Coordinate Descent) solver, among others. Tests were performed using a simulated environment of the crop row developed in Gazebo, followed by actual measurements in a strip-cropping field. During real-time data-acquisition, the localisation error is reduced from 13 mm to 11 mm within the first 120 cm of measurement. The encountered real-time geometric characteristics were found to coincide with those in the G-PC to an extend of 98.6%.

Trend Technologies for Robotic Fertilization Process in Row Crops

“… • Acquisition of vegetable characteristics in cultivation rows through a lidar system in Krus et al (2020) . • The platform location system within the crop using a point cloud processing based system described in Cruz Ulloa et al (2021) . …”

“…• The platform location system within the crop using a point cloud processing based system described in Cruz Ulloa et al (2021) .…”

“…On the other hand, the main applications of laser-type sensors are reconstructing vegetative environments for their analysis using clustering techniques and point cloud processing. ( Krus et al, 2020 ; Mesas-Carrascosa et al, 2020 ; Cruz Ulloa et al, 2021 ; Schunck et al, 2021 ).…”

CMPC: An Innovative Lidar-Based Method to Estimate Tree Canopy Meshing-Profile Volumes for Orchard Target-Oriented Spray

“…The original point cloud data obtained from LiDAR scans of the target tree were extracted using MATLAB. The data were sorted using scanning angles of −45–225° in 0.5° increments by converting polar coordinates to Cartesian coordinates [ 28 , 38 , 39 ]. The (x) values of the points that exceeded the horizontal distance between the LiDAR device the and tree trunk (2 m) were excluded from the dataset.…”

Detecting the Early Flowering Stage of Tea Chrysanthemum Using the F-YOLO Model

“…The task of harvesting chrysanthemum at a specific maturity stage usually requires shortening the reasoning time on small devices, which poses a serious challenge to computer vision algorithms. Although some methods are specially designed for mobile CPUs [4,29,30], the depth-wise separable convolution techniques adopted by these methods are not compatible with industrial integrated circuit (IC) design, examples of which include application-specific integrated circuits (ASICs) and edge computing systems. In view of this, a lightweight network based on feature fusion is proposed in this paper, which can be deployed on a mobile GPU [31] without compromising performance.…”