Image-based recognition framework for robotic weed control systems

Kounalakis, Tsampikos; Triantafyllidis, Georgios; Nalpantidis, Lazaros

doi:10.1007/s11042-017-5337-y

Cited by 18 publications

(17 citation statements)

References 48 publications

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“…Most reported robotic systems use weed recognition algorithms based on man-defined features able to represent image content information [1,3,6,4,7,10,12,11]. Specifically for the weed recognition of the Broad-leaved dock, the work in [7] proposed the use of texture information for real-time detection of the Broad-leaved dock in grasslands.…”

Section: Related Workmentioning

confidence: 99%

“…The weed recognition framework in [10,11] was based on hand crafted feature-based object and image categorization systems. That framework allowed combinations between feature extraction and detection methods, as well as methods that contribute to the improvement of their discriminatory capabilities.…”

Section: Related Workmentioning

confidence: 99%

“…However-as it will also be experimentally shown in Section 5.6-the main limiting factor of weed recognition systems, such as [10,11,12], is the limited ability of their considered hand-crafted features to effectively represent image content. A strength of Deep learning-based algorithms is that image features are not man-designed, but rather learned.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, a lot of attention has been given by the scientific community and several weed control robotic systems have been presented using Unmanned Aerial Vehicles (UAV) [1,2] and Unmanned Ground Vehicles (UGV) [3,4,5]. Such systems rely on accurate and reliable recognition of weeds from visual or other types of sensory data [1,3,6,4,7,8,9,10,11,12,13]. Such weed control systems are able to detect plants that are not part of the preferred flora and apply treatment to individual plants, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The robot integrates visual weed recognition from high-resolution 2D images captured in the uncontrolled environment of grasslands. While the majority of relevant literature focuses on crops, the much more challenging setting of grasslands has started being explored as well [7,8,9,10,11,12,13]. The weed to be recognized by our proposed system is the Broad-leaved dock (Rumex obtusifolius L.), a weed with wide geographic distribution that constitutes a major problem for dairy farms in many parts of the world.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning-based visual recognition of rumex for robotic precision farming

Kounalakis

Triantafyllidis

Nalpantidis

2019

Computers and Electronics in Agriculture

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In this paper we address the problem of recognising the Broad-leaved dock (Rumex obtusifolius L.) in grasslands from high-resolution 2D images. We discuss and present the determining factors for developing and implementing weed visual recognition algorithms using deep learning. This analysis, leads to the formulation of the proposed algorithm. Our implementation exploits Transfer Learning techniques for deep learning-based feature extraction, in combination with a classifier for weed recognition. A prototype robotic platform has been used to make available an image dataset from a dairy farm containing broad-leaved docks. The evaluation of the proposed algorithm on this dataset shows that it outperforms competing weed/plant recognition methods in recognition accuracy, while producing low false-positive rates under real-world operation conditions.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning-based visual recognition of rumex for robotic precision farming

Kounalakis

Triantafyllidis

Nalpantidis

2019

Computers and Electronics in Agriculture

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RumexWeeds: A grassland dataset for agricultural robotics

Güldenring

Evert

Nalpantidis

2023

Journal of Field Robotics

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Computer vision can lead toward more sustainable agricultural production by enabling robotic precision agriculture. Vision-equipped robots are being deployed in the fields to take care of crops and control weeds. However, publicly available agricultural datasets containing both image data as well as data from navigational robot sensors are scarce. Our real-world dataset RumexWeeds targets the detection of the grassland weeds: Rumex obtusifolius L. and Rumex crispus L. RumexWeeds includes whole image sequences instead of individual static images, which is rare for computer vision image datasets, yet crucial for robotic applications. It allows for more robust object detection, incorporating temporal aspects and considering different viewpoints of the same object. Furthermore, RumexWeeds includes data from additional navigational robot sensors-GNSS, IMU and odometry-which can increase robustness, when additionally fed to detection models. In total the dataset includes 5510 images with 15,519 manual bounding box annotations collected at three different farms and four different days in summer and autumn 2021.Additionally, RumexWeeds includes a subset of 340 ground truth pixels-wise annotations. The dataset is publicly available at https://dtu-pas.github.io/ RumexWeeds/. In this paper we also use RumexWeeds to provide baseline weed detection results considering a state-of-the-art object detector; in this way we are elucidating interesting characteristics of the dataset.

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