No abstract

Farmers are facing severe problems with weed competition in cereal crops. Grass-weeds and perennial weed species became more abundant in Europe mainly due to high percentages of cereal crops in cropping systems and reduced tillage practices combined with continuous applications of herbicides with the same mode of action. Several weed populations have evolved resistance to herbicides. Precision weed hoeing may help to overcome these problems. So far, weed hoeing in cereals was restricted to cropping practices with row distances of more than 200 mm. Hoeing in cereals with conventional row distances of 125–170 mm requires the development of automatic steering systems. The objective of this project was to develop a new automatic guidance system for inter-row hoeing using camera-based row detection and automatic side-shift control. Six field studies were conducted in winter wheat to investigate accuracy, weed control efficacy and crop yields of this new hoeing technology. A three-meter prototype and a 6-meter segmented hoe were built and tested at three different speeds in 150 mm seeded winter wheat. The maximum lateral offset from the row center was 22.53 mm for the 3 m wide hoe and 18.42 mm for the 6 m wide hoe. Camera-guided hoeing resulted in 72–96% inter-row and 21–91% intra-row weed control efficacy (WCE). Weed control was 7–15% higher at 8 km h−1 compared to 4 km h−1. WCE could be increased by 14–22% when hoeing was combined with weed harrowing. Grain yields after camera-guided hoeing at 8 km h−1 were 15–76% higher than the untreated control plots and amounted the same level as the weed-free herbicide plots. The study characterizes camera-guided hoeing in cereals as a robust and effective method of weed control.

The need for herbicide usage reduction and the increased interest in mechanical weed control has prompted greater attention to the development of agricultural robots for autonomous weeding in the past years. This also requires the development of suitable mechanical weeding tools. Therefore, we devised a new weeding tool for agricultural robots to perform intrarow mechanical weed control in sugar beets. A conventional finger weeder was modified and equipped with an electric motor. This allowed the rotational movement of the finger weeders independent of the forward travel speed of the tool carrier. The new tool was tested in combination with a bi-spectral camera in a two-year field trial. The camera was used to identify crop plants in the intrarow area. A controller regulated the speed of the motorized finger weeders, realizing two different setups. At the location of a sugar beet plant, the rotational speed was equal to the driving speed of the tractor. Between two sugar beet plants, the rotational speed was either increased by 40% or decreased by 40%. The intrarow weed control efficacy of this new system ranged from 87% to 91% in 2017 and from 91% to 94% in 2018. The sugar beet yields were not adversely affected by the mechanical treatments compared to the conventional herbicide application. The motorized finger weeders present an effective system for selective intrarow mechanical weeding. Certainly, mechanical weeding involves the risk of high weed infestations if the treatments are not applied properly and in a timely manner regardless of whether sensor technology is used or not. However, due to the increasing herbicide resistances and the continuing bans on herbicides, mechanical weeding strategies must be investigated further. The mechanical weeding system of the present study can contribute to the reduction of herbicide use in sugar beets and other wide row crops.

Plant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tested, but for practical implementations the trade-off between equipment cost, computational resources needed and the fidelity and accuracy in the reconstruction of the end-details needs to be assessed and quantified. This study examined the suitability of two low-cost systems for plant reconstruction. A low-cost Structure from Motion (SfM) technique was used to create 3D models for plant crop reconstruction. In the second method, an acquisition and reconstruction algorithm using an RGB-Depth Kinect v2 sensor was tested following a similar image acquisition procedure. The information was processed to create a dense point cloud, which allowed the creation of a 3D-polygon mesh representing every scanned plant. The selected crop plants corresponded to three different crops (maize, sugar beet and sunflower) that have structural and biological differences. The parameters measured from the model were validated with ground truth data of plant height, leaf area index and plant dry biomass using regression methods. The results showed strong consistency with good correlations between the calculated values in the models and the ground truth information. Although, the values obtained were always accurately estimated, differences between the methods and among the crops were found. The SfM method showed a slightly better result with regard to the reconstruction the end-details and the accuracy of the height estimation. Although the use of the processing algorithm is relatively fast, the use of RGB-D information is faster during the creation of the 3D models. Thus, both methods demonstrated robust results and provided great potential for use in both for indoor and outdoor scenarios. Consequently, these low-cost systems for 3D modeling are suitable for several situations where there is a need for model generation and also provide a favourable time-cost relationship.