A novel vision based row guidance approach for navigation of agricultural mobile robots in orchards

Sharifi, Mostafa; Chen, Xiaoqi

doi:10.1109/icara.2015.7081155

Cited by 29 publications

(20 citation statements)

References 5 publications

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“…A multilayer feedforward neural network and Hough transform [96] The proposed technique is unsupervised. It can be used as a complementary system to the other navigational systems such as LiDAR to provide a robust and reliable navigation system for mobile robots in orchards.…”

Section: Ref Reported Advantages Reported Disadvantagesmentioning

confidence: 99%

Machine Vision Systems in Precision Agriculture for Crop Farming

et al. 2019

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Machine vision for precision agriculture has attracted considerable research interest in recent years. The aim of this paper is to review the most recent work in the application of machine vision to agriculture, mainly for crop farming. This study can serve as a research guide for the researcher and practitioner alike in applying cognitive technology to agriculture. Studies of different agricultural activities that support crop harvesting are reviewed, such as fruit grading, fruit counting, and yield estimation. Moreover, plant health monitoring approaches are addressed, including weed, insect, and disease detection. Finally, recent research efforts considering vehicle guidance systems and agricultural harvesting robots are also reviewed.

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Section: Ref Reported Advantages Reported Disadvantagesmentioning

confidence: 99%

Machine Vision Systems in Precision Agriculture for Crop Farming

et al. 2019

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“…Regarding affordable and low-cost solutions, Riggio et al proposed a low-cost solution based only on a single-channel LIDAR and odometry, but it is greatly affected by the type of canopy and condition of the specific vineyard [33]. Instead, in [16], they proposed a vision based-control system using a clustering algorithm and Hough Transform in order to detect the central path between two rows. However, it is extremely sensitive to illumination conditions and intra-class variations.…”

Section: Related Workmentioning

confidence: 99%

“…Once endowed with the appropriate effectors, these robotic vehicles can harvest [5,6], spray [7][8][9], seed [10] and irrigate [11], and collect trees and crops data for inventory management [12][13][14]; when configured as platforms, they can carry laborers to prune and thin trees, hence reducing inefficiencies and injuries in the workplace [15]. Research on applications of mobile robotic systems in agricultural tasks has been increasing vastly [16]. However, despite the rising in investments and research activities on the subject, many implementations remain experimental and far from being applied on a large scale.…”

Section: Introductionmentioning

confidence: 99%

Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy

2020

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With the advent of agriculture 3.0 and 4.0, in view of efficient and sustainable use of resources, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Autonomous agricultural field machines have been gaining significant attention from farmers and industries to reduce costs, human workload, and required resources. Nevertheless, achieving sufficient autonomous navigation capabilities requires the simultaneous cooperation of different processes; localization, mapping, and path planning are just some of the steps that aim at providing to the machine the right set of skills to operate in semi-structured and unstructured environments. In this context, this study presents a low-cost, power-efficient local motion planner for autonomous navigation in vineyards based only on an RGB-D camera, low range hardware, and a dual layer control algorithm. The first algorithm makes use of the disparity map and its depth representation to generate a proportional control for the robotic platform. Concurrently, a second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block generating high-level motion primitives. Moreover, due to the double nature of the system, after initial training of the deep learning model with an initial dataset, the strict synergy between the two algorithms opens the possibility of exploiting new automatically labeled data, coming from the field, to extend the existing model’s knowledge. The machine learning algorithm has been trained and tested, using transfer learning, with acquired images during different field surveys in the North region of Italy and then optimized for on-device inference with model pruning and quantization. Finally, the overall system has been validated with a customized robot platform in the appropriate environment.

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“…The second approach relies on the extraction of lines representing the tree rows. In [7], [8], the lines are computed in the image space, whereas in [9], [10], [11] and [12] they are extracted from point clouds either by using a Hough transform, or by using a RANSAC algorithm coupled with an extended Kalman filter. In this second approach, the branches and the leaves are taken into account to compute the lines, which leads to inaccuracies when they are not homogeneously distributed around the trunk.…”

Section: A Orchard Navigationmentioning

confidence: 99%

Tree Detection With Low-Cost Three-Dimensional Sensors for Autonomous Navigation in Orchards

Durand-Petiteville

Flécher

Cadenat

et al. 2018

IEEE Robot. Autom. Lett.

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This paper deals with autonomous farming and with the autonomous navigation of an agricultural robot in orchards. These latter are typical semi-structured environments where the dense canopy prevents from using GPS signal and embedded sensors are often preferred to localize the vehicle. To move safely in such environments, it is necessary to provide the robot the ability of detecting and localizing trees. This paper focuses on this problem. It presents a low cost but efficient vision-based system allowing to detect accurately, quickly and robustly the trees. It is made of four stereo cameras which provide a point cloud characterizing the environment. The key idea is to find the tree trunks by detecting their shadows which are materialized by concavities in the obtained point cloud. In this way, branches and leaves are not taken into account, improving the detection robustness and therefore the navigation strategy. The method has been implemented using ROS and validated using data sequences taken in several different orchards. The obtained results definitely validate the approach and its performances show that the processing time (around 1ms) is sufficiently short for the data to be used at the control level. A comparison with other approaches from the literature is also provided.

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A novel vision based row guidance approach for navigation of agricultural mobile robots in orchards

Cited by 29 publications

References 5 publications

Machine Vision Systems in Precision Agriculture for Crop Farming

Machine Vision Systems in Precision Agriculture for Crop Farming

Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy

Tree Detection With Low-Cost Three-Dimensional Sensors for Autonomous Navigation in Orchards

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