Crop Row Detection on Tiny Plants With the Pattern Hough Transform

Winterhalter, Wera; Fleckenstein, Freya; Dornhege, Christian; Burgard, Wolfram

doi:10.1109/lra.2018.2852841

Cited by 90 publications

(49 citation statements)

References 16 publications

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“…However all the aforementioned processes require accurate guidance with respect to crop rows. Some studies showed the interest of the automation of crop row detection for robot navigation [5]- [7], and also for the detection of weeds between rows [8], [9]. In the literature different imaging-based methods have been used for detecting crop rows.…”

Section: Introductionmentioning

confidence: 99%

CRowNet: Deep Network for Crop Row Detection in UAV Images

2020

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Nowadays, the development of robots and smart tractors for the automation of sowing, harvesting, weeding etc. is transforming agriculture. Farmers are moving from an agriculture where everything is applied uniformly to a much more targeted farming. This new kind of farming is commonly referred to as precision agriculture. However for autonomous guidance of these agricultural machines and even sometimes for weed detection an accurate detection of crop rows is required. In this paper we propose a new method called CRowNet which uses a convolutional neural network (CNN) and the Hough transform to detect crop rows in images taken by an unmanned aerial vehicle (UAV). The method consists of a model formed with SegNet (S-SegNet) and a CNN based Hough transform (HoughCNet). The performance of the proposed method was quantitatively compared to traditional approaches and it showed the best and most robust result. A good crop row detection rate of 93.58% was obtained with an IoU score per crop row above 70%. Moreover the model trained on a given crop field is able to detect rows in images of different types of crops. INDEX TERMS Crop row detection, deep learning, weed detection, Hough transform, image processing.

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Section: Introductionmentioning

confidence: 99%

CRowNet: Deep Network for Crop Row Detection in UAV Images

2020

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“…It is visually demanding to differentiate the crop and noncrop region on the inclined fields only based on height. The problem of tiny plants addressed in [ 16 ] and applied Dual Hough transform relative to the crop row pattern. [ 17 ] uses dynamic programming to generate a template using geometric structures of the crop rows and able to detect the straight and curved crop rows.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields

Ponnambalam

Bakken

Moore

et al. 2020

Sensors

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Automated robotic platforms are an important part of precision agriculture solutions for sustainable food production. Agri-robots require robust and accurate guidance systems in order to navigate between crops and to and from their base station. Onboard sensors such as machine vision cameras offer a flexible guidance alternative to more expensive solutions for structured environments such as scanning lidar or RTK-GNSS. The main challenges for visual crop row guidance are the dramatic differences in appearance of crops between farms and throughout the season and the variations in crop spacing and contours of the crop rows. Here we present a visual guidance pipeline for an agri-robot operating in strawberry fields in Norway that is based on semantic segmentation with a convolution neural network (CNN) to segment input RGB images into crop and not-crop (i.e., drivable terrain) regions. To handle the uneven contours of crop rows in Norway’s hilly agricultural regions, we develop a new adaptive multi-ROI method for fitting trajectories to the drivable regions. We test our approach in open-loop trials with a real agri-robot operating in the field and show that our approach compares favourably to other traditional guidance approaches.

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“…The first stage of the corner detection algorithm consists of finding straight lines in the image. For this, the Canny edge detector [40][41][42] and the Hough transform [43][44][45][46] were used, which are available in the OpenCV library.…”

Section: Chessboard Corner Detection and Image Segmentationmentioning

confidence: 99%

Design and Construction of a Cost-Effective Didactic Robotic Arm for Playing Chess, Using an Artificial Vision System

2019

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This paper presents the design and construction of a robotic arm that plays chess against a human opponent, based on an artificial vision system. The mechanical design was an adaptation of the robotic arm proposed by the rapid prototyping laboratory FabLab RUC (Fabrication Laboratory of the University of Roskilde). Using the software Solidworks, a gripper with 4 joints was designed. An artificial vision system was developed for detecting the corners of the squares on a chessboard and performing image segmentation. Then, an image recognition model was trained using convolutional neural networks to detect the movements of pieces on the board. An image-based visual servoing system was designed using the Kanade–Lucas–Tomasi method, in order to locate the manipulator. Additionally, an Arduino development board was programmed to control and receive information from the robotic arm using Gcode commands. Results show that with the Stockfish chess game engine, the system is able to make game decisions and manipulate the pieces on the board. In this way, it was possible to implement a didactic robotic arm as a relevant application in data processing and decision-making for programmable automatons.

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Crop Row Detection on Tiny Plants With the Pattern Hough Transform

Cited by 90 publications

References 16 publications

CRowNet: Deep Network for Crop Row Detection in UAV Images

CRowNet: Deep Network for Crop Row Detection in UAV Images

Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields

Design and Construction of a Cost-Effective Didactic Robotic Arm for Playing Chess, Using an Artificial Vision System

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