Design and experiments of weeding teleoperated robot spectral sensor for winter rape and weed identification

Tao, Tao; Wu, Shu; Li, Lin; Li, Jinyang; Bao, Sheng; Wei, Xinhua

doi:10.1177/1687814018776741

Cited by 4 publications

(4 citation statements)

References 26 publications

(25 reference statements)

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“…Multispectral sensors also offer other benefits such as facilitating the differentiation of some types of plants from their environment, thanks to the different vegetation indices such as the chlorophyll absorption index and the cellulose absorption index, among others. Moreover, they provide more information during the feature extraction processes, such as those used in the OBIA methods (object-based image analysis) [11,[13][14][15][16], and SVM-based (vector support machines) classification techniques [8,17,18].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images

et al. 2020

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Weed management is one of the most important aspects of crop productivity; knowing the amount and the locations of weeds has been a problem that experts have faced for several decades. This paper presents three methods for weed estimation based on deep learning image processing in lettuce crops, and we compared them to visual estimations by experts. One method is based on support vector machines (SVM) using histograms of oriented gradients (HOG) as feature descriptor. The second method was based in YOLOV3 (you only look once V3), taking advantage of its robust architecture for object detection, and the third one was based on Mask R-CNN (region based convolutional neural network) in order to get an instance segmentation for each individual. These methods were complemented with a NDVI index (normalized difference vegetation index) as a background subtractor for removing non photosynthetic objects. According to chosen metrics, the machine and deep learning methods had F1-scores of 88%, 94%, and 94% respectively, regarding to crop detection. Subsequently, detected crops were turned into a binary mask and mixed with the NDVI background subtractor in order to detect weed in an indirect way. Once the weed image was obtained, the coverage percentage of weed was calculated by classical image processing methods. Finally, these performances were compared with the estimations of a set from weed experts through a Bland–Altman plot, intraclass correlation coefficients (ICCs) and Dunn’s test to obtain statistical measurements between every estimation (machine-human); we found that these methods improve accuracy on weed coverage estimation and minimize subjectivity in human-estimated data.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images

et al. 2020

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“…For data collection, optical sensors are the most widely used technology in weed recognition [10]. Various types of sensors, including machine vision, visible and near infrared (Vis-NIR) spectroscopy [11,12], multi-/hyper-spectral images [13][14][15][16], and distance sensing techniques [17], have been tested. These sensors can be grouped into two categories: airborne remote sensing and ground-based techniques.…”

Section: Weed Detectionmentioning

confidence: 99%

Review of Current Robotic Approaches for Precision Weed Management

Zhang

et al. 2022

Curr Robot Rep

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Purpose of ReviewThe goal of this review is to provide an overview of current robotic approaches to precision weed management. This includes an investigation into applications within this field during the past 5 years, identifying which major technical areas currently preclude more widespread use, and which key topics will drive future development and utilisation. Recent Findings Studies combining computer vision with traditional machine learning and deep learning are driving progress in weed detection and robotic approaches to mechanical weeding. Integrating key technologies for perception, decisionmaking, and control, autonomous weeding robots are emerging quickly. These effectively save effort while reducing environmental pollution caused by pesticide use. Summary This review assesses different weed detection methods and weeder robots used in precision weed management and summarises the trends in this area in recent years. The limitations of current systems are discussed, and ideas for future research directions are proposed.

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“…1/2 (13) where, Y oi and Y pi are, respectively, the i-th observed and predicted R w from N total data.…”

Section: R W =mentioning

confidence: 99%

“…Therefore, the accuracy of a satellite-based weed density map is limited. There are also some researchers mapping weed density using sensors installed on an agricultural vehicle platform, mainly tractors [8,[11][12][13]. In this method, sensors such as lidars, multispectral cameras, and hyperspectral cameras are installed close to the ground.…”

Section: Introductionmentioning

confidence: 99%

A Field Weed Density Evaluation Method Based on UAV Imaging and Modified U-Net

et al. 2021

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Weeds are one of the main factors affecting the yield and quality of agricultural products. Accurate evaluation of weed density is of great significance for field management, especially precision weeding. In this paper, a weed density calculating and mapping method in the field is proposed. An unmanned aerial vehicle (UAV) was used to capture field images. The excess green minus excess red index, combined with the minimum error threshold segmentation method, was used to segment green plants and bare land. A modified U-net was used to segment crops from images. After removing the bare land and crops from the field, images of weeds were obtained. The weed density was evaluated by the ratio of weed area to total area on the segmented image. The accuracy of the green plant segmentation was 93.5%. In terms of crop segmentation, the intersection over union (IoU) was 93.40%, and the segmentation time of a single image was 35.90 ms. Finally, the determination coefficient of the UAV evaluated weed density and the manually observed weed density was 0.94, and the root mean square error was 0.03. With the proposed method, the weed density of a field can be effectively evaluated from UAV images, hence providing critical information for precision weeding.

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Design and experiments of weeding teleoperated robot spectral sensor for winter rape and weed identification

Cited by 4 publications

References 26 publications

A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images

A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images

Review of Current Robotic Approaches for Precision Weed Management

A Field Weed Density Evaluation Method Based on UAV Imaging and Modified U-Net

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