Real-time detection of young spruce using color and texture features on an autonomous forest machine

Hyyti, Heikki; Kalmari, Jouko; Visala, Arto

doi:10.1109/ijcnn.2013.6707122

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…Future work should look for a better way to utilize all the color information available in camera images. Hyyti et al [1], for instance, used EGRBI color transformation to better separate spruce from the surrounding vegetation. Color information could also be used to separate the sky visible through the trees.…”

Section: Discussionmentioning

confidence: 99%

“…It might be a better approach to concentrate only on one feature type or perhaps learn some simple hierarchical features. Hyyti et al [1] achieved a real-time implementation by using this approach and a graphical processing unit.…”

Section: Discussionmentioning

confidence: 99%

“…Fig. 1, for instance, shows the semi-autonomous forest machine used by Hyyti et al [1] for real-time detection of spruce seedlings during automated mechanical point cleaning. The same machine with a different processing head is used for tending of slightly older trees.…”

Section: Introductionmentioning

confidence: 99%

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Detection and species classification of young trees using machine perception for a semi-autonomous forest machine

Vihlman

Hyyti

Kalmari

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-An approach to automatically detect and classify young spruce and birch trees in forest environment is presented. The method could be used in autonomous or semi-autonomous forest machines during tending operations. Detection is done by segmenting laser range images formed by a rotating laser scanner. Classification is done with a two-class Naive Bayes classifier based on image texture features. Multiple combinations of 99 features were tested and the best classifier included eight features from the co-occurrence matrix, local binary patterns, statistical geometrical features and Gabor filter. 79% of spruces and birches in the testing material were detected and 74% of these were correctly classified. Results suggest that the approach is suitable but there are still some challenges in each of the processing steps. Iteration between segmentation and classification is needed to increase reliability.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Detection and species classification of young trees using machine perception for a semi-autonomous forest machine

Vihlman

Hyyti

Kalmari

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

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“…Additionally, a study has explored the use of a vision system comprising a conventional color camera and machine learning algorithm for the automatic detection of spruce seedlings during planting operations (Hyyti et al, 2013).…”

Section: Literature Review About Computer Vision In Forestrymentioning

confidence: 99%

Exploring the feasibility of autonomous forestry operations: Results from the first experimental unmanned machine

La Hera,

Mendoza‐Trejo,

Lindroos

et al. 2024

Journal of Field Robotics

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This article presents a study on the world's first unmanned machine designed for autonomous forestry operations. In response to the challenges associated with traditional forestry operations, we developed a platform equipped with essential hardware components necessary for performing autonomous forwarding tasks. Through the use of computer vision, autonomous navigation, and manipulator control algorithms, the machine is able to pick up logs from the ground and manoeuvre through a range of forest terrains without the need for human intervention. Our initial results demonstrate the potential for safe and efficient autonomous extraction of logs in the cut‐to‐length harvesting process. We achieved a high level of accuracy in our computer vision system, and our autonomous navigation system proved to be highly efficient. This research represents a significant milestone in the field of autonomous outdoor robotics, with far‐reaching implications for the future of forestry operations. By reducing the need for human labor, autonomous machines have the potential to increase productivity and reduce labor costs, while also minimizing the environmental impact of timber harvesting. The success of our study highlights the potential for further development and optimization of autonomous machines in the forestry industry.

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“…Kaizu and Imou [10] developed a dual-spectral camera system that could reduce water surface noise and clearly detect seedling rows. Furthermore, the appearance of weeds and crops can be differentiated by leaf shape and texture features as well [11][12][13][14] .…”

Section: Introduction mentioning

confidence: 99%

Visual detection of rice rows based on Bayesian decision theory and robust regression least squares method

He¹,

Zhang²,

Luo³

et al. 2021

International Journal of Agricultural and Biological Engineering

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Paddy field management is complicated and labor intensive. Correct row detection is important to automatically track rice rows. In this study, a novel method was proposed for accurate rice row recognition in paddy field transplanted by machine before the disappearance of row information. Firstly, Bayesian decision theory based on the minimum error was used to classify the period of collected images into three periods (T1: 0-7 d; T2: 7-28 d; T3: 28-45 d), and resulting in the correct recognition rate was 97.03%. Moreover, secondary clustering of feature points was proposed, which can solve some problems such as row breaking and tilting. Then, the robust regression least squares method (RRLSM) for linear fitting was proposed to fit rice rows to effectively eliminate interference by outliers. Finally, a credibility analysis of connected region markers was proposed to evaluate the accuracy of fitting lines. When the threshold of credibility was set at 40%, the correct recognition rate of fitting lines was 96.32%. The result showed that the method can effectively solve the problems caused by the presence of duckweed, high-density inter-row weeds, broken rows, tilting (±60°), wind and overlap.

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Real-time detection of young spruce using color and texture features on an autonomous forest machine

Cited by 5 publications

References 15 publications

Detection and species classification of young trees using machine perception for a semi-autonomous forest machine

Detection and species classification of young trees using machine perception for a semi-autonomous forest machine

Exploring the feasibility of autonomous forestry operations: Results from the first experimental unmanned machine

Visual detection of rice rows based on Bayesian decision theory and robust regression least squares method

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