Hans W. Griepentrog

Evolutionary Agroecology: the potential for cooperative, high density, weed-suppressing cereals

Evolutionary theory can be applied to improve agricultural yields and/or sustainability, an approach we call Evolutionary Agroecology. The basic idea is that plant breeding is unlikely to improve attributes already favored by millions of years of natural selection, whereas there may be unutilized potential in selecting for attributes that increase total crop yield but reduce plants’ individual fitness. In other words, plant breeding should be based on group selection. We explore this approach in relation to crop‐weed competition, and argue that it should be possible to develop high density cereals that can utilize their initial size advantage over weeds to suppress them much better than under current practices, thus reducing or eliminating the need for chemical or mechanical weed control. We emphasize the role of density in applying group selection to crops: it is competition among individuals that generates the ‘Tragedy of the Commons’, providing opportunities to improve plant production by selecting for attributes that natural selection would not favor. When there is competition for light, natural selection of individuals favors a defensive strategy of ‘shade avoidance’, but a collective, offensive ‘shading’ strategy could increase weed suppression and yield in the high density, high uniformity cropping systems we envision.

Seed Mapping of Sugar Beet

Abstract.Individual plant care may well become embodied in precision farming in the future and will lead to new opportunities in agricultural crop management. The objective of this project was to develop and evaluate a data logging system attached to a precision seeder to enable high accuracy seed position mapping of a field of sugar beet. A Real Time Kinematic Global Positioning System (RTK GPS), optical seed detectors and a data logging system were 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 16–43 mm depending on vehicle speed and seed spacing. The results showed that the overall accuracy of the estimated plant positions was acceptable for the guidance of vehicles and implements as well as potential individual plant treatments.

A Mission Planner for an Autonomous Tractor

In this article, a mission planner of field coverage operations for an autonomous agricultural tractor is presented. Missions for a particular autonomous tractor are defined using an XML (extendible markup language) formatted file that can be uploaded to the tractor through the user interface. Using the tree hierarchy of the mission file, several actions are determined, including the sequence of points the tractor has to follow, the type of motion between successive points (e.g., straight motion or maneuvering), the type of predefined turning routine used in maneuvering, and the actions that should be taken once the tractor reaches the desired point (e.g., raising or lowering the attached tool, turning on or turning off the power take-off). In order to automatically create the XML mission files, a program was developed using the MATLAB technical programming language. The program uses data regarding the field (geometry, dimensions, field sub-regions, working direction, initial and final desired locations of the tractor), the operating width, and the operation type (mowing, spraying) as inputs. The planning method is based on an algorithmic approach where field coverage planning is transformed and formulated, via semantic representations, as a vehicle routing problem (VRP). By using this approach, the total non-working distance can be reduced by up to 50% compared to the conventional non-optimized method. Three sets of experiments are presented. In the first set, three fields were separately covered; in the second set, three neighboring fields were covered as part of a single tractor mission; and in the third set of experiments, a single field was covered during a hypothetical spraying operation for two different locations of the refilling facility.

3-D Imaging Systems for Agricultural Applications—A Review

Efficiency increase of resources through automation of agriculture requires more information about the production process, as well as process and machinery status. Sensors are necessary for monitoring the status and condition of production by recognizing the surrounding structures such as objects, field structures, natural or artificial markers, and obstacles. Currently, three dimensional (3-D) sensors are economically affordable and technologically advanced to a great extent, so a breakthrough is already possible if enough research projects are commercialized. The aim of this review paper is to investigate the state-of-the-art of 3-D vision systems in agriculture, and the role and value that only 3-D data can have to provide information about environmental structures based on the recent progress in optical 3-D sensors. The structure of this research consists of an overview of the different optical 3-D vision techniques, based on the basic principles. Afterwards, their application in agriculture are reviewed. The main focus lays on vehicle navigation, and crop and animal husbandry. The depth dimension brought by 3-D sensors provides key information that greatly facilitates the implementation of automation and robotics in agriculture.

3D Maize Plant Reconstruction Based on Georeferenced Overlapping LiDAR Point Clouds

3D crop reconstruction with a high temporal resolution and by the use of non-destructive measuring technologies can support the automation of plant phenotyping processes. Thereby, the availability of such 3D data can give valuable information about the plant development and the interaction of the plant genotype with the environment. This article presents a new methodology for georeferenced 3D reconstruction of maize plant structure. For this purpose a total station, an IMU, and several 2D LiDARs with different orientations were mounted on an autonomous vehicle. By the multistep methodology presented, based on the application of the ICP algorithm for point cloud fusion, it was possible to perform the georeferenced point clouds overlapping. The overlapping point cloud algorithm showed that the aerial points (corresponding mainly to plant parts) were reduced to 1.5%–9% of the total registered data. The remaining were redundant or ground points. Through the inclusion of different LiDAR point of views of the scene, a more realistic representation of the surrounding is obtained by the incorporation of new useful information but also of noise. The use of georeferenced 3D maize plant reconstruction at different growth stages, combined with the total station accuracy could be highly useful when performing precision agriculture at the crop plant level.

A systems analysis of information system requirements for an experimental farm

A systems analysis and design of information requirements for an experimental farm is presented. This study was carried out on the university farm (UF) at the University of Copenhagen in Denmark. The UF has several farm sites, many employees and clients, and collects data on the spatial variability of sites for site-specific management, and is responsible for running field trials. Soft and hard system analyses were performed to better understand the information needs and design an information system for the UF. Soft systems methodology was used to analyse the human activities and to identify user requirements, while a hard systems methodology was used to structure the data handling inside the farm office. The resulting information management system (IMS) includes modules for storage, processing and presentation of spatio-temporal data for research trials and site-specific management. A GIS-based farm IMS including the necessary interfaces was implemented and validated by the UF manager and staff. Limitations and constraints to the full implementation of the IMS in an experimental farm are also discussed.

Route planning for orchard operations

Mission and route planning for an agricultural robot.Orchards is a well-suited operational environment for the application of deterministic behaviour robotic systems.Modelling of inter- and intra-row orchards operations.Reduction in the non-working time ranged between 10.7% and 32.4%. Orchard operations are considered a promising area for the implementation of robotic systems because of the inherent structured operational environment that arises from time-independent spatial tree configurations. In this paper, a route planning approach is developed and tested using a deterministic behaviour robot (named AMS - autonomous mechanisation system). The core of the planning method is the generation of routing plans for intra- and inter-row orchard operations, based on the adaptation of an optimal area coverage method developed for arable farming operations (B-patterns). Experiments have verified that operational efficiencies can be improved significantly compared with the conventional, non-optimised method of executing orchard operations. Specifically, the experimental results showed that the non-working time reduction ranged between 10.7% and 32.4% and that the reduction in the non-working distance ranged between 17.5% and 40.2% resulting to savings in the total travelled distance ranged between 2.2% and 6.4%.

Autonomous Systems for Plant Protection

Advances in automation are demanded by the market mainly as a response to high labor costs. Robotic outdoor systems are ready to allow not only economically viable operations but also increased efficiency in agriculture, horticulture and forestry. The aim of this chapter is to give examples of autonomous operations related to crop protection probably commercially available in the near future. Scouting and monitoring together with the efficient application of chemicals or mechanical treatments are operations which can be successful automated. Drawbacks are that current systems are lacking robust and safe behaviors. In general the potential of saving e.g. of herbicides are huge when high precision targeting based on individual weed plant detections is used.

Total station data assessment using an industrial robotic arm for dynamic 3D in-field positioning with sub-centimetre accuracy

AB lines, U-Turn, and Pattern-8 experiments were performed.As the speed increased so did the relative XTE.Changing the position of the TS from inline to perpendicular gave a better accuracy.The maximum mean horizontal XTE value was 4.01mm for Pattern-8 experiment.The vertical relative XTE did not exceed 10mm including the outliers. For agricultural tasks related to precision farming, accurate in-field positioning is a necessity. The accuracy of some centimetres that the real time kinematic-global navigation satellite system (RTK-GNSS) can provide is adequate for many applications, such as auto-steering navigation and section control for spraying or fertiliser applications. Nevertheless, the demand for higher in-field accuracy at a mm level is increasing. A device that is gaining a lot of attention in the agricultural sector for its increased accuracy is a robotic total station (TS) that can track a prism mounted on a vehicle. With the aim to be able to use this device under realistic conditions for dynamic 3D in-field positioning at a sub-centimetre level, the accuracy of the TS was assessed utilising an industrial robotic arm. The robotic arm had a repeatability factor of 0.1mm and was placed outdoors under normal environmental conditions for agriculture practice. Straight AB lines but also U-turn and Pattern-8 experiments were performed. The absolute error of the robotic arm had a maximum mean value of 0.33mm for the Pattern-8 experiment, while the highest error, equal to 1.30mm, was detected in the 95th percentile of the same experiment. The horizontal and vertical relative cross-track error (XTE) between the TS and the robotic arm data was calculated for various speeds and for two different positions of the TS. From the results, it was evident that as the speed increased so did the horizontal relative XTE. Furthermore, changing the position of the TS from in line to perpendicular, in respect to the direction of motion, proved to result in a higher accuracy. The maximum mean horizontal relative XTE value of all experiments was 4.01mm for Pattern-8, which also had the maximum value for the 95th percentile, i.e. 12.86mm. The vertical relative XTE for all experiments did not exceed 10mm including the outliers.

A survey of future farm automation – a descriptive analysis of survey responses

Today, especially in Europe, operational efficiency of machines is an important product development goal because further capacity increases by size seem to be limited. Efficient semi-autonomous or autonomous machine operations are likely to be the next step in automation strategy in agriculture. The aim of this paper is to present descriptive results of survey responses that explored the perception of future advanced mechanization systems by German farmers including the likely adoption of automated farming machinery. In general, the farmers emphasized their high interest in advanced future techniques. This is confirmed already by their investment in fully automatic guidance systems. However, farmers are still sceptical about the use of autonomous machines on their farms in terms of reliability and safety.