Weed recognition framework for robotic precision farming

Kounalakis, Tsampikos; Triantafyllidis, Georgios; Nalpantidis, Lazaros

doi:10.1109/ist.2016.7738271

Cited by 23 publications

(26 citation statements)

References 15 publications

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“…This negative impact might be dramatically reduced if the weed control could be more targeted by application in a way such that the amount of herbicide usage is reduced to a minimum and its application more targetd by spot spraying only where weeds exist. In view of this, a few research works have investigated weed detection/recognition in grasslands by using conventional machine learning methods [2,3,4].…”

Section: Literature Reviewmentioning

confidence: 99%

Broad-Leaf Weed Detection in Pasture

Zhang

Hansen

Volonakis

et al. 2018

2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)

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Weed control in pasture is a challenging problem that can be expensive and environmentally unfriendly. This paper proposes a novel method for recognition of broad-leaf weeds in pasture such that precision weed control can be achieved with reduced herbicide use. Both conventional machine learning algorithms and deep learning methods have been explored and compared to achieve high detection accuracy and robustness in real-world environments. In-pasture grass/weed image data have been captured for classifier training and algorithm validation. The proposed deep learning method has achieved 96.88% accuracy and is capable of detecting weeds in different pastures under various representative outdoor lighting conditions.

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Section: Literature Reviewmentioning

confidence: 99%

Broad-Leaf Weed Detection in Pasture

Zhang

Hansen

Volonakis

et al. 2018

2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)

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“…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%

“…Therefore, several design considerations contributed in the final pipeline of the proposed algorithm. Each considered frame acquired from a top-view camera mounted on the robot [13], is partitioned in several blocks, corresponding to spatial regions, using the proposed experimental framework in [10]. In order to perform the recognition process and compensate for limitation of deep learning techniques to process high-resolution images, we exploit Transfer Learning, where the content of each image is represented with features computed from a pre-trained Convolutional Neural Network (CNN) [15,16,17,18,19].…”

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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“…Both SIFT and SURF are invariant to illumination and colour while providing strong performance against noise. The SIFT descriptor has been used for weed classification and recognition in several recent studies (Kazmi et al, 2015;Kounalakis, Triantafyllidis, & Nalpantidis, 2016;Wilf et al, 2016). Using the SIFT descriptor, Wilf et al (2016) proposed a leaf identification procedure based on a machine learning approach.…”

Section: Introductionmentioning

confidence: 99%

Sugar beet and volunteer potato classification using Bag-of-Visual-Words model, Scale-Invariant Feature Transform, or Speeded Up Robust Feature descriptors and crop row information

Suh

Hofstee

IJsselmuiden

et al. 2018

Biosystems Engineering

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One of the most important steps in vision-based weed detection systems is the classification of weeds growing amongst crops. In EU SmartBot project it was required to effectively control more than 95% of volunteer potatoes and ensure less than 5% of damage of sugar beet. Classification features such as colour, shape and texture have been used individually or in combination for classification studies but they have proved unable to reach the required classification accuracy under natural and varying daylight conditions. A classification algorithm was developed using a Bag-of-Visual-Words (BoVW) model based on Scale-Invariant Feature Transformation (SIFT) or Speeded Up Robust Feature (SURF) features with crop row information in the form of the Out-of-Row Regional Index (ORRI). The highest classification accuracy (96.5% with zero false-negatives) was obtained using SIFT and ORRI with Support Vector Machine (SVM) which is considerably better than previously reported research although its 7% false-positives deviated from the requirements. The average classification time of 0.10-0.11 s met the real-time requirements. The SIFT descriptor showed better classification accuracy than the SURF, but classification time did not vary significantly. Adding location information (ORRI) significantly improved overall classification accuracy. SVM showed better classification performance than random forest and neural network. The proposed approach proved its potential under varying natural light conditions, but implementing a practical system, including vegetation segmentation and weed removal may potentially reduce the overall performance and more research is needed.

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Weed recognition framework for robotic precision farming

Cited by 23 publications

References 15 publications

Broad-Leaf Weed Detection in Pasture

Broad-Leaf Weed Detection in Pasture

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

Sugar beet and volunteer potato classification using Bag-of-Visual-Words model, Scale-Invariant Feature Transform, or Speeded Up Robust Feature descriptors and crop row information

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