Multi-template matching algorithm for cucumber recognition in natural environment

Bao, Guanjun; Cai, Shibo; Qi, Liyong; Yi, Xun; Zhang, Libin; Yang, Qinghua

doi:10.1016/j.compag.2016.08.001

Cited by 43 publications

(28 citation statements)

References 10 publications

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“…A common approach in fruit detection and counting [153] is by using a single viewpoint, as in the case of a cucumber harvesting robot [23] , or multiple viewpoints [32] with additional sensing from one or multiple vision sensors that are not located on the robot [159] . Examples of the recent achievements include automatic fruit recognition from multiple images [160] or based on the fusion of color and 3D feature [161] , multi-template matching algorithm [162] , symmetry analysis [163] , combined color distance method and RGB-D data analysis for apples [164] and sweet-peppers [41] , stereo vision for apple detection [165,166] , and the use of convolutional neural networks [167] and deep learning algorithms for fruit detection and obstacle avoidance in extremely dense foliage [54,168] . Some of the challenges to be addressed in designing of a complete robotic harvesting are the simultaneous localization of fruit and environment mapping, path planning algorithms, and the number of detectable and harvestable fruits in different plant density conditions.…”

Section: Harvesting Robotsmentioning

confidence: 99%

Research and development in agricultural robotics: A perspective of digital farming

Shamshiri¹,

Weltzien²,

Hameed³

et al. 2018

International Journal of Agricultural and Biological Engineering

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Digital farming is the practice of modern technologies such as sensors, robotics, and data analysis for shifting from tedious operations to continuously automated processes. This paper reviews some of the latest achievements in agricultural robotics, specifically those that are used for autonomous weed control, field scouting, and harvesting. Object identification, task planning algorithms, digitalization and optimization of sensors are highlighted as some of the facing challenges in the context of digital farming. The concepts of multi-robots, human-robot collaboration, and environment reconstruction from aerial images and ground-based sensors for the creation of virtual farms were highlighted as some of the gateways of digital farming. It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information. For the case of robotic harvesting, an autonomous framework with several simple axis manipulators can be faster and more efficient than the currently adapted professional expensive manipulators. While robots are becoming the inseparable parts of the modern farms, our conclusion is that it is not realistic to expect an entirely automated farming system in the future.

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Section: Harvesting Robotsmentioning

confidence: 99%

Research and development in agricultural robotics: A perspective of digital farming

Shamshiri¹,

Weltzien²,

Hameed³

et al. 2018

International Journal of Agricultural and Biological Engineering

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show abstract

“…In the simulated experiments, the normalized correlation coefficient (NCC), 48,49 coefficient of determination (R 2 ), root mean square error (RMSE), SNR, and PSNR 50 between the smoothed spectrum and standard vegetation spectrum were calculated to evaluate the denoising results. For measured canopy spectra of winter wheat, NCC was used to estimate the waveform similarity of the spectra before and after denoising, to assess the results qualitatively, whereas wheat biophysical and biochemical parameters were retrieved using different hyperspectral vegetation indices derived from denoised spectra to quantitatively assess the results.…”

Section: Evaluation Criteriamentioning

confidence: 99%

Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters

Zhang

et al. 2019

J. Appl. Rem. Sens.

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Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters,"Abstract. Spectral noise causes distorted spectra, shifting the central wavelength and thus reducing the accuracy of surface parameter retrieval. A hybrid method combining mathematical-morphology and wavelet-transform (WB)-based filters was used to remove spectral noise. First, a generalized mathematical-morphology (GM) filter was used to remove large-amplitude noise, and then the processed spectra were smoothed using the WT-based filter to remove smallamplitude noise. The simulated noise spectrum and 76 measured canopy spectra for winter wheat were denoised with three filters: the combination filter (CF), GM, and WT. In the simulated experiments, five evaluation indices were calculated to evaluate the denoising effects. For measured spectra, qualitative analyses were performed based on spectral characteristics. Quantitative evaluations were conducted by deriving various vegetation indices from denoised spectra to retrieve wheat's biophysical and biochemical parameters. The results indicated that the CF removed both large-and small-amplitude noise efficiently, improving signal-to-noise ratio and peak signal-to-noise ratio of simulated noise spectrum and retrieval accuracy of leaf water content (LWC) significantly. Meanwhile, it better maintained the waveform and smoothness of spectrum, improving the retrieval accuracies of leaf area index and chlorophyll data slightly. The coefficient of determination (R 2 ) of developed model between the modified normalized difference water index and LWC was improved from 0.428 to 0.622 using the CF, 0.555 using the GM, and 0.549 using the WT. The R 2 and root mean square error between the measured and retrieval LWC were improved from 0.364 and 0.027 to 0.611 and 0.018 using the CF, whereas the corresponding values were 0.504 and 0.022 for the GM, and 0.478 and 0.023 for the WT. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…As the weight must be calculated again for the matched image each time the template is changed, template matching takes considerable time; however, the accuracy is much higher than the starting point search method, therefore, this method was used for study [13] (Figure 1). In continuous conditions, the image function is f (x, y).…”

Section: Template Matching and Invariant Momentsmentioning

confidence: 99%

The Application of Deep Learning and Image Processing Technology in Laser Positioning

et al. 2018

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In this study, machine vision technology was used to precisely position the highest energy of the laser spot to facilitate the subsequent joining of product workpieces in a laser welding machine. The displacement stage could place workpieces into the superposition area and allow the parts to be joined. With deep learning and a convolutional neural network training program, the system could enhance the accuracy of the positioning and enhance the efficiency of the machine work. A bi-analytic deep learning localization method was proposed in this study. A camera was used for real-time monitoring. The first step was to use a convolutional neural network to perform a large-scale preliminary search and locate the laser light spot region. The second step was to increase the optical magnification of the camera, re-image the spot area, and then use template matching to perform high-precision repositioning. According to the aspect ratio of the search result area, the integrity parameters of the target spot were determined. The centroid calculation was performed in the complete laser spot. If the target was an incomplete laser spot, the operation of invariant moments would be performed. Based on the result, the precise position of the highest energy of the laser spot could be obtained from the incomplete laser spot image. The amount of displacement could be calculated by overlapping the highest energy of the laser spot and the center of the image.

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Multi-template matching algorithm for cucumber recognition in natural environment

Cited by 43 publications

References 10 publications

Research and development in agricultural robotics: A perspective of digital farming

Research and development in agricultural robotics: A perspective of digital farming

Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters

The Application of Deep Learning and Image Processing Technology in Laser Positioning

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