Hortibot: Feasibility study of a plant nursing robot performing weeding operations – part IV

Based on the development of a robotic tool carrier (Hortibot) equipped with weeding tools, a feasibility study was carried out to evaluate the viability of this innovative technology. The feasibility was demonstrated through a targeted evaluation adapted to the obtainable knowledge on the system performance in horticulture. A usage scenario was designed to set the implementation of the robotic system in a row crop of seeded bulb onions considering operational and functional constraints in organic crop, production. This usage scenario together with the technical specifications of the implemented system provided the basis for the feasibility analysis, including a comparison with a conventional weeding system. Preliminary results show that the automation of the weeding tasks within a row crop has the potential of significantly reducing the costs and still fulfill the operational requirements set forth. The potential benefits in terms of operational capabilities and economic viability have been quantified. Profitability gains ranging from 20 to 50% are achievable through targeted applications. In general, the analyses demonstrate the operational and economic feasibility of using small automated vehicles and targeted tools in specialized production settings

Individual plant care in cropping systems

Individual plant care cropping systems, embodied in precision farming, may lead to new opportunities in agricultural crop management. The objective of the project was to provide high accuracy seed position mapping of a field of sugar beet. An RTK GPS was retrofitted on to a precision seeder to map the seeds as they were planted. The average error between the seed map and the actual plant map was about 32 mm to 59 mm. The results showed that the overall accuracy of the estimated plant positions is acceptable for the guidance of vehicles and implements. For subsequent individual plant care, the deviations were not, in all cases, small enough to ensure accurate individual plant targeting

Advanced Non-Chemical and Close to Plant Weed Control system for Organic Agriculture

Use of chemical has been reduced in agriculture for controlling weeds emergence. The use of alternative systems, such as cultural practices (mulching, flame, intercropping etc.) and mechanical system (hoe, tine etc.) has been introduced by various researchers. Automation technique based on sensors controlled system has enhanced the efficiency of the mechanical system for weed control. Mostly, low cost image acquisition sensors and optical sensor to detect the plant ensuring swift operation of vehicles close the crop plants to remove competitive weeds. The available system need to be evaluated to get best possible system for close to plant (CTP) weed removal. In the study various non-chemical weed control measures has been explored and 30 mechanical tools for CTP were evaluated. High precision tillage solutions and thermal weed control by pulsed lasers for eradication of stem or main shoot were found to be the most promising weed control concepts for CTP operation

An Effective Multi-Cue Positioning System for Agricultural Robotics

The self-localization capability is a crucial component for Unmanned Ground Vehicles (UGV) in farming applications. Approaches based solely on visual cues or on low-cost GPS are easily prone to fail in such scenarios. In this paper, we present a robust and accurate 3D global pose estimation framework, designed to take full advantage of heterogeneous sensory data. By modeling the pose estimation problem as a pose graph optimization, our approach simultaneously mitigates the cumulative drift introduced by motion estimation systems (wheel odometry, visual odometry, ...), and the noise introduced by raw GPS readings. Along with a suitable motion model, our system also integrates two additional types of constraints: (i) a Digital Elevation Model and (ii) a Markov Random Field assumption. We demonstrate how using these additional cues substantially reduces the error along the altitude axis and, moreover, how this benefit spreads to the other components of the state. We report exhaustive experiments combining several sensor setups, showing accuracy improvements ranging from 37% to 76% with respect to the exclusive use of a GPS sensor. We show that our approach provides accurate results even if the GPS unexpectedly changes positioning mode. The code of our system along with the acquired datasets are released with this paper.Comment: Accepted for publication in IEEE Robotics and Automation Letters, 201

Session 2: \u3cem\u3eA Vision Guidance System on Agricultural Sprayers Reduces Operator Stress\u3c/em\u3e

Manually operating an agricultural sprayer is a stressful activity. Operators work an average of 15 hrs/day in peak season [1], navigating 38-cm (15-in) tires in 76-cm (30-in) rows. Guidance systems can relieve operator fatigue [2]. A commercially-available vision guidance system (VSN®, Raven Industries) is available for agricultural vehicles. Human subject protocols were approved by the IRB. Three experienced male operators drove manually and with VSN in the same field. Each wore an Empatica E4 wristband to measure their electrodermal activity (EDA). Sprayer steering status was recorded from the sprayer guidance system (RS1TM, Raven Industries). EDA data was filtered with a Hampel filter to remove artifacts [3] (1 s window before and after) and then with an infinite impulse response (MATLAB filtfilt function) [4] with a window of 1 s. Filtered EDA peaks and valleys were calculated (MATLAB findpeaks function) [5] and stressful events were defined as those which exceeded a magnitude threshold of 0.01 μS [6]. The frequency of stressful events, stressful event characteristics (e.g., magnitude, duration and area under the curve) and frequency of steering adjustments were calculated while driving in straight rows (length \u3e 150 m). An ANOVA was performed on each calculated metric with steering type and participant as predictor variables and p \u3c 0.05 considered significant. Thirty-four passes in four fields were analyzed (16 manual, 18 VSN). Operators steering with VSN had 49% fewer stressful events per time compared to manual driving (3.6 versus 7.1 events/min, p \u3c 0.001, Figure 1). These results suggest that steering with VSN considerably reduces the stress on agricultural operators compared to steering manually

Aspects of automation of selective cleaning

Cleaning (pre-commercial thinning) is a silvicultural operation, primarily used to improve growing conditions of remaining trees in young stands (ca. 3 - 5 m of height). Cleaning costs are considered high in Sweden and the work is laborious. Selective cleaning with autonomous artificial agents (robots) may rationalise the work, but requires new knowledge. This thesis aims to analyse key issues regarding automation of cleaning; suggesting general solutions and focusing on automatic selection of main-stems. The essential requests put on cleaning robots are to render acceptable results and to be cost competitive. They must be safe and be able to operate independently and unattended for several hours in a dynamic and non-deterministic environment. Machine vision, radar, and laser scanners are promising techniques for obstacle avoidance, tree identification, and tool control. Horizontal laser scannings were made, demonstrating the possibility to find stems and make estimations regarding their height and diameter. Knowledge regarding stem selections was retrieved through qualitative interviews with persons performing cleaning. They consider similar attributes of trees, and these findings and current cleaning manuals were used in combination with a field inventory in the development of a decision support system (DSS). The DSS selects stems by the attributes species, position, diameter, and damage. It was used to run computer-based simulations in a variety of young forests. A general follow-up showed that the DSS produced acceptable results. The DSS was further evaluated by comparing its selections with those made by experienced cleaners, and by a test in which laymen performed cleanings following the system. The DSS seems to be useful and flexible, since it can be adjusted in accordance with the cleaners’ results. The laymen’s results implied that the DSS is robust and that it could be used as a training tool. Using the DSS in automatic, or semi-automatic, cleaning operations should be possible if and when selected attributes can be automatically perceived. A suitable base-machine and thorough research, regarding e.g. safety, obstacle avoidance, and target identification, is needed to develop competitive robots. However, using the DSS as a training-tool for inexperienced cleaners could be an interesting option as of today

Computational Contributions to the Automation of Agriculture

The purpose of this paper is to explore ways that computational advancements have enabled the complete automation of agriculture from start to finish. With a major need for agricultural advancements because of food and water shortages, some farmers have begun creating their own solutions to these problems. Primarily explored in this paper, however, are current research topics in the automation of agriculture. Digital agriculture is surveyed, focusing on ways that data collection can be beneficial. Additionally, self-driving technology is explored with emphasis on farming applications. Machine vision technology is also detailed, with specific application to weed management and harvesting of crops. Finally, the effects of automating agriculture are briefly considered, including labor, the environment, and direct effects on farmers

Development of a tabletop guidance system for educational robots

The guidance of a vehicle in an outdoor setting is typically implemented using a Real Time Kinematic Global Positioning System (RTK-GPS) potentially enhanced by auxiliary sensors such as electronic compasses, rotation encoders, gyroscopes, and vision systems. Since GPS does not function in an indoor setting where educational competitions are often held, an alternative guidance system was developed. This article describes a guidance method that contains a laser-based localization system, which uses a robot-borne single laser transmitter spinning in a horizontal plane at an angular velocity up to 81 radians per second. Sensor arrays positioned in the corners of a flat rectangular table with dimensions of 1.22 m × 1.83 m detected the laser beam passages. The relative time differences among the detections of the laser passages gave an indication of the angles of the sensors with respect to the laser beam transmitter on the robot. These angles were translated into Cartesian coordinates. The guidance of the robot was implemented using a uni-directional wireless serial connection and position feedback from the localization system. Three experiments were conducted to test the system: 1) the accuracy of the static localization system was determined while the robot stood still. In this test the average error among valid measurements was smaller than 0.3 %. However, a maximum of 3.7 % of the measurements were invalid due to several causes. 2) The accuracy of the guidance system was assessed while the robot followed a straight line. The average deviation from this straight line was 3.6 mm while the robot followed a path with a length of approximately 0.9 m. 3) The overall performance of the guidance system was studied while the robot followed a complex path consisting of 33 sub-paths. The conclusion was that the system worked reasonably accurate, unless the robot came in close proximity

Comparison of positional accuracy between RTK and RTX GNSS gased on the autonomous agricultural vehicles under field conditions

Currently, many systems (machine vision, high resolution remote sensing, global positioning systems, and odometry techniques) have been integrated into agricultural e quipment to increase the efficiency, productivity, and safety of the individual in all field activities. This study focused upon assessing a satellite-based localization solution used in straight path guidance of an autonomou s vehicle developed for ag ricultural applica tions. The autonom ous agricultural vehicle was designed and constructed under RHEA (Robot fleets for highly effective agriculture and forestry management) project and is part of a three-unit fleet of similar vehicles. Static tests showed that 99% of all positions are placed within a circle with a 2.9 cm radius centered at the geo-position usi ng real-time satellite corrections (RTX). Dynamic tests between rows demonstrated a mean (N=610) of the standard deviation for real-time base station corrections (RTK) of 1.43 cm and for real-time satellite corrections (RTX) of 2.55 cm. These re sults demonstrate that the tractor was able to track each straight line with high degree of accuracy. The integration of a Global Navigation Satellite System (GNSS) with sensors (e.g., inertial sensor, altimeters, odomet ers, etc.) within the vehicle showed th e potential of autonomous tractors for expanding agricultural applications utilizing this technology.European Union FP7/2007-201

Mapping Wide Row Crops with Video Sequences Acquired from a Tractor Moving at Treatment Speed

This paper presents a mapping method for wide row crop fields. The resulting map shows the crop rows and weeds present in the inter-row spacing. Because field videos are acquired with a camera mounted on top of an agricultural vehicle, a method for image sequence stabilization was needed and consequently designed and developed. The proposed stabilization method uses the centers of some crop rows in the image sequence as features to be tracked, which compensates for the lateral movement (sway) of the camera and leaves the pitch unchanged. A region of interest is selected using the tracked features, and an inverse perspective technique transforms the selected region into a bird’s-eye view that is centered on the image and that enables map generation. The algorithm developed has been tested on several video sequences of different fields recorded at different times and under different lighting conditions, with good initial results. Indeed, lateral displacements of up to 66% of the inter-row spacing were suppressed through the stabilization process, and crop rows in the resulting maps appear straight