The Use of Agricultural Robots in Orchard Management

Book chapter that summarizes recent research on agricultural robotics in orchard management, including Robotic pruning, Robotic thinning, Robotic spraying, Robotic harvesting, Robotic fruit transportation, and future trends.Comment: 22 page

Machine Vision-Based Crop-Load Estimation Using YOLOv8

Labor shortages in fruit crop production have prompted the development of mechanized and automated machines as alternatives to labor-intensive orchard operations such as harvesting, pruning, and thinning. Agricultural robots capable of identifying tree canopy parts and estimating geometric and topological parameters, such as branch diameter, length, and angles, can optimize crop yields through automated pruning and thinning platforms. In this study, we proposed a machine vision system to estimate canopy parameters in apple orchards and determine an optimal number of fruit for individual branches, providing a foundation for robotic pruning, flower thinning, and fruitlet thinning to achieve desired yield and quality.Using color and depth information from an RGB-D sensor (Microsoft Azure Kinect DK), a YOLOv8-based instance segmentation technique was developed to identify trunks and branches of apple trees during the dormant season. Principal Component Analysis was applied to estimate branch diameter (used to calculate limb cross-sectional area, or LCSA) and orientation. The estimated branch diameter was utilized to calculate LCSA, which served as an input for crop-load estimation, with larger LCSA values indicating a higher potential fruit-bearing capacity.RMSE for branch diameter estimation was 2.08 mm, and for crop-load estimation, 3.95. Based on commercial apple orchard management practices, the target crop-load (number of fruit) for each segmented branch was estimated with a mean absolute error (MAE) of 2.99 (ground truth crop-load was 6 apples per LCSA). This study demonstrated a promising workflow with high performance in identifying trunks and branches of apple trees in dynamic commercial orchard environments and integrating farm management practices into automated decision-making

Tree Structure Retrieval for Apple Trees from 3D Pointcloud

3D reconstruction is a challenging problem and has been an important research topic in the areas of remote sensing and computer vision for many years. Existing 3D reconstruction approaches are not suitable for orchard applications due to complicated tree structures. Current tree reconstruction has included models specific to trees of a certain density, but the impact of varying Leaf Area Index(LAI) on model performance has not been studied. To better manage an apple orchard, this thesis proposes methods for evaluating an apple canopy density mapping system as an input for a variable-rate sprayer for both trellis-structured (2D) and standalone (3D) apple orchards using a 2D LiDAR (Light Detection and Ranging). The canopy density mapping system has been validated for robustness and repeatability with multiple scans. The consistency of the whole row during multiple passes has a correlation R^2 = 0.97. The proposed system will help the decision-making in a variable-rate sprayer. To further study the individual tree structure, this thesis proposes a novel and fast approach to reconstruct and analyse 3D trees over a range of Leaf Area Index (LAI) values from LiDAR for morphology analysis for height, branch length and angles of real and simulated apple trees. After using Principal Component Analysis (PCA) to extract the trunk points, an improved Mean Shift algorithm is introduced as Adapted Mean Shift (AMS) to classify different branch clusters and extract the branch nodes. A full evaluation workflow of tree parameters including trunk and branches is introduced for morphology analysis to investigate the accuracy of the approach over different LAI values. Tree height, branch length, and branch angles were analysed and compared to the ground truth for trees with a range of LAI values. When the LAI is smaller than 0.1, the accuracy for height and length is greater than 90\% and the accuracy for the angles is around 80\%. When the LAI is greater than 0.1, the branch accuracy reduces to 40\%. This analysis of tree reconstruction performance concerning LAI values, as well as the combination of efficient and accurate structure reconstruction, opens the possibility of improving orchard management and botanical studies on a large scale. To improve the accuracy of traditional tree structure analysis, a deep learning approach is introduced to pre-process and classify unbalanced, in-homogeneous, and noisy point cloud data. TreeNet is inspired by 3D U-Net, adding classes and median filters to segment trunk, branch, and leave parts. TreeNet outperformed 3D U-Net and SVM in the case of Kappa, Matthews Correlation Coefficient(MCC), and F1-score value in segmentation. The TreeNet-AMS combined method also showed improvement in tree structure analysis than the traditional AMS method mentioned above. Following on from this research, efficient tree structure analysis on tree height, trunk length, branch position, and branch length could be conducted. Knowing the tree morphology is proved to be closely relevant to thinning, spraying and yield, the proposed work will then largely benefit the relevant studies in agriculture and forestry

Computer Vision Problems in 3D Plant Phenotyping

In recent years, there has been significant progress in Computer Vision based plant phenotyping (quantitative analysis of biological properties of plants) technologies. Traditional methods of plant phenotyping are destructive, manual and error prone. Due to non-invasiveness and non-contact properties as well as increased accuracy, imaging techniques are becoming state-of-the-art in plant phenotyping. Among several parameters of plant phenotyping, growth analysis is very important for biological inference. Automating the growth analysis can result in accelerating the throughput in crop production. This thesis contributes to the automation of plant growth analysis. First, we present a novel system for automated and non-invasive/non-contact plant growth measurement. We exploit the recent advancements of sophisticated robotic technologies and near infrared laser scanners to build a 3D imaging system and use state-of-the-art Computer Vision algorithms to fully automate growth measurement. We have set up a gantry robot system having 7 degrees of freedom hanging from the roof of a growth chamber. The payload is a range scanner, which can measure dense depth maps (raw 3D coordinate points in mm) on the surface of an object (the plant). The scanner can be moved around the plant to scan from different viewpoints by programming the robot with a specific trajectory. The sequence of overlapping images can be aligned to obtain a full 3D structure of the plant in raw point cloud format, which can be triangulated to obtain a smooth surface (triangular mesh), enclosing the original plant. We show the capability of the system to capture the well known diurnal pattern of plant growth computed from the surface area and volume of the plant meshes for a number of plant species. Second, we propose a technique to detect branch junctions in plant point cloud data. We demonstrate that using these junctions as feature points, the correspondence estimation can be formulated as a subgraph matching problem, and better matching results than state-of-the-art can be achieved. Also, this idea removes the requirement of a priori knowledge about rotational angles between adjacent scanning viewpoints imposed by the original registration algorithm for complex plant data. Before, this angle information had to be approximately known. Third, we present an algorithm to classify partially occluded leaves by their contours. In general, partial contour matching is a NP-hard problem. We propose a suboptimal matching solution and show that our method outperforms state-of-the-art on 3 public leaf datasets. We anticipate using this algorithm to track growing segmented leaves in our plant range data, even when a leaf becomes partially occluded by other plant matter over time. Finally, we perform some experiments to demonstrate the capability and limitations of the system and highlight the future research directions for Computer Vision based plant phenotyping

3D segmentation and localization using visual cues in uncontrolled environments

3D scene understanding is an important area in robotics, autonomous vehicles, and virtual reality. The goal of scene understanding is to recognize and localize all the objects around the agent. This is done through semantic segmentation and depth estimation. Current approaches focus on improving the robustness to solve each task but fail in making them efficient for real-time usage. This thesis presents four efficient methods for scene understanding that work in real environments. The methods also aim to provide a solution for 2D and 3D data. The first approach presents a pipeline that combines the block matching algorithm for disparity estimation, an encoder-decoder neural network for semantic segmentation, and a refinement step that uses both outputs to complete the regions that were not labelled or did not have any disparity assigned to them. This method provides accurate results in 3D reconstruction and morphology estimation of complex structures like rose bushes. Due to the lack of datasets of rose bushes and their segmentation, we also made three large datasets. Two of them have real roses that were manually labelled, and the third one was created using a scene modeler and 3D rendering software. The last dataset aims to capture diversity, realism and obtain different types of labelling. The second contribution provides a strategy for real-time rose pruning using visual servoing of a robotic arm and our previous approach. Current methods obtain the structure of the plant and plan the cutting trajectory using only a global planner and assume a constant background. Our method works in real environments and uses visual feedback to refine the location of the cutting targets and modify the planned trajectory. The proposed visual servoing allows the robot to reach the cutting points 94% of the time. This is an improvement compared to only using a global planner without visual feedback, which reaches the targets 50% of the time. To the best of our knowledge, this is the first robot able to prune a complete rose bush in a natural environment. Recent deep learning image segmentation and disparity estimation networks provide accurate results. However, most of these methods are computationally expensive, which makes them impractical for real-time tasks. Our third contribution uses multi-task learning to learn the image segmentation and disparity estimation together end-to-end. The experiments show that our network has at most 1/3 of the parameters of the state-of-the-art of each individual task and still provides competitive results. The last contribution explores the area of scene understanding using 3D data. Recent approaches use point-based networks to do point cloud segmentation and find local relations between points using only the latent features provided by the network, omitting the geometric information from the point clouds. Our approach aggregates the geometric information into the network. Given that the geometric and latent features are different, our network also uses a two-headed attention mechanism to do local aggregation at the latent and geometric level. This additional information helps the network to obtain a more accurate semantic segmentation, in real point cloud data, using fewer parameters than current methods. Overall, the method obtains the state-of-the-art segmentation in the real datasets S3DIS with 69.2% and competitive results in the ModelNet40 and ShapeNetPart datasets