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

Plant Pathology and Information Technology: Opportunity for Management of Disease Outbreak and Applications in Regulation Frameworks

In many European rural areas, agriculture is not only an economic activity, but it is strictly linked to environmental and social characteristics of the area. Thus, sometimes, a pathogen can become a social threat, as in the case of Xylella fastidiosa and olive trees ( Olea europaea L.) in Salento. Fast and systemic response to threats represents the key to success in stopping pest invasions, and proves a great help in managing lots of data in a short time or coordinating large-scale monitoring coming from applying Information Technology tools. Regarding the field of applications, the advantages provided by new technologies are countless. However, is it the same in agriculture? Electronic identification tools can be applied for plant health management and certification. Treatments, agrochemical management or impact assessment may also be supported by dematerialization of data. Information Technology solution for urban forestry management or traceability of commodities belonging to "Food from Somewhere" regimes were analyzed and compared to protection from pests of a unique tree heritage such as olive trees in Salento

Fast and robust curve skeletonization for real-world elongated objects

We consider the problem of extracting curve skeletons of three-dimensional, elongated objects given a noisy surface, which has applications in agricultural contexts such as extracting the branching structure of plants. We describe an efficient and robust method based on breadth-first search that can determine curve skeletons in these contexts. Our approach is capable of automatically detecting junction points as well as spurious segments and loops. All of that is accomplished with only one user-adjustable parameter. The run time of our method ranges from hundreds of milliseconds to less than four seconds on large, challenging datasets, which makes it appropriate for situations where real-time decision making is needed. Experiments on synthetic models as well as on data from real world objects, some of which were collected in challenging field conditions, show that our approach compares favorably to classical thinning algorithms as well as to recent contributions to the field.Comment: 47 pages; IEEE WACV 2018, main paper and supplementary materia

Perceptions of Precision Agriculture Technologies in the U.S. Fresh Apple Industry

Advances in precision agriculture technologies provide opportunities to improve the efficiency of agricultural production systems, especially for high-value specialty crops such as fresh apples (Malus domestica). We distributed an online survey to apple growers in Washington, New York, and Michigan to elicit stakeholder perceptions of precision agriculture technologies. Findings from this study demonstrated that growers are willing to adopt precision agriculture technologies when they receive results from applied research projects and are engaged with active extension programs. The availability of customized services and purchasing and rental options may minimize the effects of the economies of size that create barriers to adopting increasing access to technologies. Finally, respondents deemed collaborative efforts between industry and academic institutions crucial for adapting the innovation to better address the needs of growers