As the world population is expected to reach 10 billion by 2050, our agricultural production system needs to double its productivity despite a decline of human workforce in the agricultural sector. Autonomous robotic systems are one promising pathway to increase productivity by taking over labor-intensive manual tasks like fruit picking. To be effective, such systems need to monitor and interact with plants and fruits precisely, which is challenging due to the cluttered nature of agricultural environments causing, for example, strong occlusions. Thus, being able to estimate the complete 3D shapes of objects in presence of occlusions is crucial for automating operations such as fruit harvesting. In this paper, we propose the first publicly available 3D shape completion dataset for agricultural vision systems. We provide an RGB-D dataset for estimating the 3D shape of fruits. Specifically, our dataset contains RGB-D frames of single sweet peppers in lab conditions but also in a commercial greenhouse. For each fruit, we additionally collected high-precision point clouds that we use as ground truth. For acquiring the ground truth shape, we developed a measuring process that allows us to record data of real sweet pepper plants, both in the lab and in the greenhouse with high precision, and determine the shape of the sensed fruits. We release our dataset, consisting of almost 7,000 RGB-D frames belonging to more than 100 different fruits. We provide segmented RGB-D frames, with camera intrinsics to easily obtain colored point clouds, together with the corresponding high-precision, occlusion-free point clouds obtained with a high-precision laser scanner. We additionally enable evaluation of shape completion approaches on a hidden test set through a public challenge on a benchmark server.

Conventional crop production, which is essential for providing food, feed, fuel, and fiber for our society, relies heavily on harmful herbicides to control weeds. Instead, agricultural robots could remove weeds more sustainably. However, these robots require a generalizable perception system that can locate weeds, enabling automatic removal of weeds. Specifically, they need to perform crop-weed semantic segmentation, which locates and distinguishes between the crop and the weed plants with pixel-level resolution. However, most existing crop-weed semantic segmentation methods are fully supervised and require expensive and labor-intensive pixel-wise labeling of the training data. To avoid the costly labeling process, we address the problem of unsupervised crop-weed segmentation in this paper. Unlike previous approaches, we leverage the idea that weeds are "weird" plants that occur less frequently and are highly variable in appearance, and reframe the problem as an anomaly segmentation problem. We propose an approach to segment weeds as anomalous plants by categorizing plants in the feature space of a pretrained foundation model. Our approach curates a bag-of-features representation of crop features and models the manifold of crop plants as hyperspheres. During inference, it classifies vegetation segments of the image with features within this manifold as crop plants and all other plants as weeds. Our experiments show that our zero-shot anomaly segmentation method can perform crop-weed segmentation on several datasets from real crop fields.

Crop yield estimation is a relevant problem in agriculture, because an accurate yield estimate can support farmers’ decisions on harvesting or precision intervention. Robots can help to automate this process. To do so, they need to be able to perceive the surrounding environment to identify target objects such as trees and plants. In this paper, we introduce a novel approach to address the problem of hierarchical panoptic segmentation of apple orchards on 3D data from different sensors. Our approach is able to simultaneously provide semantic segmentation, instance segmentation of trunks and fruits, and instance segmentation of trees (a trunk with its fruits). This allows us to identify relevant information such as individual plants, fruits, and trunks, and capture the relationship among them, such as precisely estimate the number of fruits associated to each tree in an orchard. To efficiently evaluate our approach for hierarchical panoptic segmentation, we provide a dataset designed specifically for this task. Our dataset is recorded in Bonn, Germany, in a real apple orchard with a variety of sensors, spanning from a terrestrial laser scanner to a RGB-D camera mounted on different robots platforms. The experiments show that our approach surpasses state-of-the-art approaches in 3D panoptic segmentation in the agricultural domain, while also providing full hierarchical panoptic segmentation. Our dataset is publicly available at https://www.ipb.uni-bonn.de/data/hops/. The open-source implementation of our approach is available at https://github.com/PRBonn/hapt3D.

Interpreting camera data is key for autonomously acting systems, such as autonomous vehicles. Vision systems that operate in real-world environments must be able to understand their surroundings and need the ability to deal with novel situations. This paper tackles open-world se-mantic segmentation, i.e., the variant of interpreting image data in which objects occur that have not been seen during training. We propose a novel approach that performs accu-rate closed-world semantic segmentation and, at the same time, can identify new categories without requiring any ad-ditional training data. Our approach11Code: https://github.com/PRBonn/ContMAV additionally provides a similarity measure for every newly discovered class in an image to a known category, which can be useful information in downstream tasks such as planning or mapping. Through extensive experiments, we show that our model achieves state-of-the-art results on classes known from training data as well as for anomaly segmentation and can distinguish between different unknown classes.

Robots that operate in agricultural environments need a robust perception system that can deal with occlusions, which are naturally present in agricultural scenarios. In this paper, we address the problem of estimating 3D shapes of fruits when only partial observations are available. Generally speaking, such a shape completion can be realized by exploiting prior knowledge about the geometry of the fruit. This is typically done by template matching using traditional optimization algorithms, which are slow but accurate, or by encoding such knowledge into the weights of a neural network, leading to faster but often less accurate estimates. Our approach combines the best of both worlds. It exploits the benefit of having a template representing our object of interest with the advantages of using a neural network to learn how to deform a template. Our experimental evaluation demonstrates that our approach yields accurate estimation at a competitively low inference time in challenging greenhouse environments.

Agricultural production is facing challenges in the next decades induced by climate change and the need for more sustainability by reducing its impact on the environment. Advances in field management through robotic intervention, monitoring of crops by autonomous unmanned aerial vehicles (UAVs) supporting breeding of novel and more resilient crop varieties can help to address these challenges. The analysis of plant traits is called phenotyping and is an essential activity in plant breeding; it however involves a great amount of manual labor. With this paper, we provide means to better tackle the problems of instance segmentation to support robotic intervention and automatic fine-grained, organ-level geometric analysis needed for precision phenotyping. As the availability of real-world data in this domain is relatively scarce, we provide a novel dataset that was acquired using UAVs capturing high-resolution images of real breeding trials containing 48 plant varieties and therefore covering a relevant morphological and appearance spectrum. This enables the development of approaches for instance segmentation and autonomous phenotyping that generalize well to different plant varieties. Based on overlapping high-resolution images taken from multiple viewing angles, we provide photogrammetric dense point clouds and provide detailed and accurate point-wise labels for plants, leaves, and salient points as the tip and the base in 3D. Additionally, we include measurements of phenotypic traits performed by experts from the German Federal Plant Variety Office on the real plants, allowing the evaluation of new approaches not only on segmentation and keypoint detection but also directly on actual traits. The provided labeled point clouds enable finegrained plant analysis and support further progress in the development of automatic phenotyping approaches, but also enable further research in surface reconstruction, point cloud completion, and semantic interpretation of point clouds.

The production of food, feed, fiber, and fuel is a key task of agriculture, which has to cope with many challenges in the upcoming decades, e.g., a higher demand, climate change, lack of workers, and the availability of arable land. Vision systems can support making better and more sustainable field management decisions, but also support the breeding of new crop varieties by allowing temporally dense and reproducible measurements. Recently, agricultural robotics got an increasing interest in the vision and robotics communities since it is a promising avenue for coping with the aforementioned lack of workers and enabling more sustainable production. While large datasets and benchmarks in other domains are readily available and enable significant progress, agricultural datasets and benchmarks are comparably rare. We present an annotated dataset and benchmarks for the semantic interpretation of real agricultural fields. Our dataset recorded with a UAV provides high-quality, pixel-wise annotations of crops and weeds, but also crop leaf instances at the same time. Furthermore, we provide benchmarks for various tasks on a hidden test set comprised of different fields: known fields covered by the training data and a completely unseen field.

Crop inspection is a critical part of modern agricultural practices that helps farmers assess the current status of a field and then make crop management decisions. Current crop inspection methods are labour-intensive tasks, which makes them rather slow and expensive to apply. In this paper, we exploit recent advancements in implicit mapping to tackle the challenging context of agricultural environments to create dense maps of crop rows with high enough fidelity to be useful for automated crop inspection. Specifically, we map strawberry and sweet pepper crop rows using RGB images captured by a wheeled mobile field robot inside a greenhouse and then use this data to build 3D maps to document the development of plants and fruits. Our Target-Aware Implicit Mapping system (TAIM) uses a SLAM-based pose initialization strategy for robust pose convergence, an efficient information-guided training sample selection framework for faster loss reduction, and focuses on exploiting training samples for fruit regions of the scene, which are critical for crop inspection tasks, to create more accurate maps in less time.

Monitoring the traits of plants and fruits is a fundamental task in horticulture. With accurate measurements, farmers can predict the yield of their crops and use this information for making informed management decisions, and breeders can use it for variety selection. Agricultural robotic applications promise to automate this monitoring task. In this paper, we address the problem of monitoring fruit growth and investigate the matching of fruits recorded in commercial greenhouses at different growth stages based on data recorded from terrestrial laser scanners. This is challenging as fruits appear highly similar, change over time, and are subject to severe occlusions. We first propose a fruit descriptor, which captures the topology of the fruit surroundings to facilitate the matching between different points in time. We capture and describe the relationship between a fruit and its neighbors such that our descriptors are less affected by the growth over time. Furthermore, we define a matching cost function and use an optimal assignment algorithm to match the fruit observations taken in different weeks. The experiments show that our descriptor achieves a high spatio-temporal matching accuracy, which is superior to the commonly used geometric point cloud descriptors.

Agricultural robots have the prospect to enable more efficient and sustainable agricultural production of food, feed, and fiber. Perception of crops and weeds is a central component of agricultural robots that aim to monitor fields and assess the plants as well as their growth stage in an automatic manner. Semantic perception mostly relies on deep learning using supervised approaches, which require time and qualified workers to label fairly large amounts of data. In this paper, we look into the problem of reducing the amount of labels without compromising the final segmentation performance. For robots operating in the field, pre-training networks in a supervised way is already a popular method to reduce the number of required labeled images. We investigate the possibility of pre-training in a self-supervised fashion using data from the target domain. To better exploit this data, we propose a set of domain-specific augmentation strategies. We evaluate our pre-training on semantic segmentation and leaf instance segmentation, two important tasks in our domain. The experimental results suggest that pre-training with domain-specific data paired with our data augmentation strategy leads to superior performance compared to commonly used pre-trainings. Furthermore, the pre-trained networks obtain similar performance to the fully supervised with less labeled data.

Plant phenotyping is a central task in agriculture, as it describes plants' growth stage, development, and other relevant quantities. Robots can help automate this process by accurately estimating plant traits such as the number of leaves, leaf area, and the plant size. In this paper, we address the problem of joint semantic, plant instance, and leaf instance segmentation of crop fields from RGB data. We propose a single convolutional neural network that addresses the three tasks simultaneously, exploiting their underlying hierarchical structure. We introduce task-specific skip connections, which our experimental evaluation proves to be more beneficial than the usual schemes. We also propose a novel automatic post-processing, which explicitly addresses the problem of spatially close instances, common in the agricultural domain because of overlapping leaves. Our architecture simultaneously tackles these problems jointly in the agricultural context. Previous works either focus on plant or leaf segmentation, or do not optimise for semantic segmentation. Results show that our system has superior performance compared to state-of-the-art approaches, while having a reduced number of parameters and is operating at camera frame rate.

Perception is crucial for robots that act in real-world environments, as autonomous systems need to see and understand the world around them to act properly. Panoptic segmentation provides an interpretation of the scene by computing a pixelwise semantic label together with instance IDs. In this paper, we address panoptic segmentation using RGB-D data of indoor scenes. We propose a novel encoder-decoder neural network that processes RGB and depth separately through two encoders. The features of the individual encoders are progressively merged at different resolutions, such that the RGB features are enhanced using complementary depth information. We propose a novel merging approach called ResidualExcite, which reweighs each entry of the feature map according to its importance. With our double-encoder architecture, we are robust to missing cues. In particular, the same model can train and infer on RGB-D, RGB-only, and depth-only input data, without the need to train specialized models. We evaluate our method on publicly available datasets and show that our approach achieves superior results compared to other common approaches for panoptic segmentation.

Monitoring plants and fruits at high resolution play a key role in the future of agriculture. Accurate 3D information can pave the way to a diverse number of robotic applications in agriculture ranging from autonomous harvesting to precise yield estimation. Obtaining such 3D information is non-trivial as agricultural environments are often repetitive and cluttered, and one has to account for the partial observability of fruit and plants. In this paper, we address the problem of jointly estimating complete 3D shapes of fruit and their pose in a 3D multi-resolution map built by a mobile robot. To this end, we propose an online multi-resolution panoptic mapping system where regions of interest are represented with a higher resolution. We exploit data to learn a general fruit shape representation that we use at inference time together with an occlusion-aware differentiable rendering pipeline to complete partial fruit observations and estimate the 7 DoF pose of each fruit in the map. The experiments presented in this paper, evaluated both in the controlled environment and in a commercial greenhouse, show that our novel algorithm yields higher completion and pose estimation accuracy than existing methods, with an improvement of 41 % in completion accuracy and 52 % in pose estimation accuracy while keeping a low inference time of 0.6 s in average.

Unmanned aerial vehicles (UAVs) are frequently used for aerial mapping and general monitoring tasks. Recent progress in deep learning enabled automated semantic segmentation of imagery to facilitate the interpretation of large-scale complex environments. Commonly used supervised deep learning for segmentation relies on large amounts of pixelwise labeled data, which is tedious and costly to annotate. The domain-specific visual appearance of aerial environments often prevents the usage of models pretrained on publicly available datasets. To address this, we propose a novel general planning framework for UAVs to autonomously acquire informative training images for model retraining. We leverage multiple acquisition functions and fuse them into probabilistic terrain maps. Our framework combines the mapped acquisition function information into the UAV's planning objectives. In this way, the UAV adaptively acquires informative aerial images to be manually labeled for model retraining. Experimental results on real-world data and in a photorealistic simulation show that our framework maximizes model performance and drastically reduces labeling efforts. Our map-based planners outperform state-of-the-art local planning.

Monitoring individual plants and computing precise 3D reconstructions is highly relevant for crop breeding. In the conventional breeding approach, humans measure phenotypic traits by hand, requiring substantial manual labor. This paper addresses precise 3D plant reconstructions in a crop field or breeding plot based on UAV imagery. We explicitly address the challenges resulting from the thin structures of leaves and naturally occurring self-occlusions. We combine photogrammetric bundle adjustment with a template-based matching approach and produce accurate 3D models that allow us to derive common, geometric traits used by breeders to phenotype plants. We provide a thorough experimental evaluation on commercially used sugar beet breeding plots to illustrate the capabilities of our method as well as its real world applicability.

Semantic segmentation of aerial imagery is an important tool for mapping and earth observation. However, supervised deep learning models for segmentation rely on large amounts of high-quality labelled data, which is labour-intensive and time-consuming to generate. To address this, we propose a new approach for using unmanned aerial vehicles (UAVs) to autonomously collect useful data for model training. We exploit a Bayesian approach to estimate model uncertainty in semantic segmentation. During a mission, the semantic predictions and model uncertainty are used as input for terrain mapping. A key aspect of our pipeline is to link the mapped model uncertainty to a robotic planning objective based on active learning. This enables us to adaptively guide a UAV to gather the most informative terrain images to be labelled by a human for model training. Our experimental evaluation on real-world data shows the benefit of using our informative planning approach in comparison to static coverage paths in terms of maximising model performance and reducing labelling efforts.

A detailed analysis of a plant’s phenotype in real field conditions is critical for plant scientists and breeders to understand plant function. In contrast to traditional phenotyping performed manually, vision-based systems have the potential for an objective and automated assessment with high spatial and temporal resolution. One of such systems’ objectives is to detect and segment individual leaves of each plant since this information correlates to the growth stage and provides phenotypic traits, such as leaf count, cover-age, and size. In this paper, we propose a vision-based approach that performs instance segmentation of individual crop leaves and associates each with its corresponding crop plant in real fields. This enables us to compute relevant basic phenotypic traits on a per-plant level. We employ a convolutional neural network and operate directly on drone imagery. The network generates two different representations of the input image that we utilize to cluster individual crop leaf and plant instances. We propose a novel method to compute clustering regions based on our network’s predictions that achieves high accuracy. Furthermore, we com-pare to other state-of-the-art approaches and show that our system achieves superior performance. The source code of our approach is available1.

In modern agriculture, measuring phenotypic traits helps breeders monitor plant growth, increase yield, and provide food, feed, and fiber. Traditional phenotyping requires intensive manual work, partially being intrusive. In this paper, we investigate the challenge of measuring phenotypic traits in an automated fashion through mobile robots operating in field environments. In particular, we want to measure plants from images acquired by mobile robots instead of using data from a static scanning environment. We propose to use a differentiable rendering approach to deform a generic 3D template of a plant to fit the observation recorded by a robot while ensuring a coherent deformation of the plant template. The experiments presented in this paper suggest that our approach allows for 3D reconstruction of different plant species at different growth stages using single images. From that model, we can compute important phenotypic traits, such as the leaf area index.

Plant phenotyping, i.e., the task of measuring plant traits to describe the anatomy and physiology of plants, is a central task in crop science and plant breeding. Standard methods often require intrusive or time-consuming operations involving a lot of manual labor. Cameras or range sensors, paired with 3D reconstructions methods, can support phenotyping but the task yields several challenges in practice such as plant growth over time. In this paper, we address the problem of finding correspondences between plants recorded at different points in time to track phenotypic traits in an automated fashion. Our approach makes use of semantic segmentation and unsupervised clustering to compute keypoints from plant point clouds. We extract a compact representation of the considered scan that encodes both, topology and semantic information. Through our approach, we are able to tackle the data association problem for 4D point cloud data of plants effectively. We tested our approach on different 3D plus time, i.e., 4D, sequences of plant point clouds of different plant species. The experiments presented in this paper suggest that our 4D matching approach allows for non-rigid registration of the plants that change over time. Moreover, we show that our method allows for tracking different phenotyping traits at an organ level, forming a basis for automated temporal phenotyping.