A Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV) can map the overflown environment in point clouds. Mapped canopy heights allow for the estimation of crop biomass in agriculture. The work presented in this paper contributes to sensory UAV setup design for mapping and textual analysis of agricultural fields. LiDAR data are combined with data from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors to conduct environment mapping for point clouds. The proposed method facilitates LiDAR recordings in an experimental winter wheat field. Crop height estimates ranging from 0.35–0.58 m are correlated to the applied nitrogen treatments of 0–300 $\mathrm{kg}\frac{\mathrm{N}}{\mathrm{ha}}$ . The LiDAR point clouds are recorded, mapped, and analysed using the functionalities of the Robot Operating System (ROS) and the Point Cloud Library (PCL). Crop volume estimation is based on a voxel grid with a spatial resolution of 0.04 × 0.04 × 0.001 m. Two different flight patterns are evaluated at an altitude of 6 m to determine the impacts of the mapped LiDAR measurements on crop volume estimations.

The safe in-field operation of autonomous agricultural vehicles requires detecting all objects that pose a risk of collision. Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. In this paper, the problem is posed as anomaly detection instead, where convolutional autoencoders are applied to identify any objects deviating from the normal pattern. Training an autoencoder network to reconstruct normal patterns in agricultural fields makes it possible to detect unknown objects by high reconstruction error. Basic autoencoder (AE), vector-quantized variational autoencoder (VQ-VAE), denoising autoencoder (DAE) and semisupervised autoencoder (SSAE) with a max-margin-inspired loss function are investigated and compared with a baseline object detector based on YOLOv5. Results indicate that SSAE with an area under the curve for precision/recall (PR AUC) of 0.9353 outperforms other autoencoder models and is comparable to an object detector with a PR AUC of 0.9794. Qualitative results show that SSAE is capable of detecting unknown objects, whereas the object detector is unable to do so and fails to identify known classes of objects in specific cases.

The stricter legislation within the European Union for the regulation of herbicides that are prone to leaching causes a greater economic burden on the agricultural industry through taxation. Owing to the increased economic burden, research in reducing herbicide usage has been prompted. High-resolution images from digital cameras support the studying of plant characteristics. These images can also be utilized to analyze shape and texture characteristics for weed identification. Instead of detecting weed patches, weed density can be estimated at a sub-patch level, through which even the identification of a single plant is possible. The aim of this study is to adapt the monocot and dicot coverage ratio vision (MoDiCoVi) algorithm to estimate dicotyledon leaf cover, perform grid spraying in real time, and present initial results in terms of potential herbicide savings in maize. The authors designed and executed an automated, large-scale field trial supported by the Armadillo autonomous tool carrier robot. The field trial consisted of 299 maize plots. Half of the plots (parcels) were planned with additional seeded weeds; the other half were planned with naturally occurring weeds. The in-situ evaluation showed that, compared to conventional broadcast spraying, the proposed method can reduce herbicide usage by 65% without measurable loss in biological effect.

Unmanned Aerial Systems (UAS) operations are evolving towards autonomous flight beyond visual line of sight (BVLOS), which requires moving assessments normally conducted by the remote pilot to the autonomous software. An important assessment currently conducted by the remote pilot is whether the current weather conditions pose a safety risk to the flight. This work deals with the development of a software framework for analysing weather data. The developed framework is capable of providing a weather analysis report to a remote supervisor or to an autonomous decision-making software. The software framework has been tested using historical weather data for a full calendar year provided by IBM. The weather data was applied to four different UAS ranging from small UAS to a passenger UAS. The results obtained show that during a full year, flight was possible from 53.9 % to 95.8 % of the time depending on the UAS. We consider the software framework an important step towards improving the operational safety for autonomous UAS operations under BVLOS conditions.

Vision-based object detection is essential for safe and efficient field operation for autonomous agricultural vehicles. However, one of the challenges in transferring state-of-the-art object detectors to the agricultural domain is the limited availability of labeled datasets. This paper seeks to address this challenge by utilizing two object detection models based on YOLOv5, one pre-trained on a large-scale dataset for detecting general classes of objects and one trained to detect a smaller number of agriculture-specific classes. To combine the detections of the models at inference, we propose an ensemble module based on a hierarchical structure of classes. Results show that applying the proposed ensemble module increases mAP@.5 from $0.575$ to $0.65$ on the test dataset and reduces the misclassification of similar classes detected by different models. Furthermore, by translating detections from base classes to a higher level in the class hierarchy, we can increase the overall mAP@.5 to $0.701$ at the cost of reducing class granularity.

Rapid robotic system development has created a demand for multi-disciplinary methods and tools to explore and compare design alternatives. In this paper, we present a collaborative modeling technique that combines discrete-event models of controller software with continuous-time models of physical robot components. The proposed co-modeling method utilizes the Vienna development method (VDM) and MATLAB for discrete-event modeling and 20-sim for continuous-time modeling. The model-based development of a mobile robot mink feeding system is used to illustrate the collaborative modeling method. Simulations are used to evaluate the robot model output response in relation to operational demands. An example of a load-carrying challenge in relation to the feeding robot is presented, and a design space is defined with candidate solutions in both the mechanical and software domains. Simulation results are analyzed using design space exploration (DSE), which evaluates candidate solutions in relation to preselected optimization criteria. The result of the analysis provides developers with an overview of the impacts of each candidate solution in the chosen design space. Based on this overview of solution impacts, the developers can select viable candidates for deployment and testing with the actual robot.