Weed Detection over Between-Row of Sugarcane Fields Using Machine Vision with Shadow Robustness Technique for Variable Rate Herbicide Applicator

Muangkasem, Apimuk; Thainimit, Somying; Keinprasit, Rachaporn; Isshiki, Takaaki

doi:10.3844/erjsp.2010.141.145

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Equation 1identifies a pixel as a plant if its green component (II(:,:,2)) is dominant over its blue (II(:,:,3)) and red (II(:,:,1)) components. Numerous studies have been conducted to segment green plants from the background using RGB indices [4,41]. Considering the camera motion speed (0.10 m/s) and natural condition effects, the optimal threshold of 140 for green channels of RGB color space was adopted (in the range of 20-250), according to the method described in Reference [42], by checking different images for eliminating incomplete green components versus the required processing time.…”

Section: Pre-processing and Segmentationmentioning

confidence: 99%

Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields

Dadashzadeh

Abbaspour‐Gilandeh

Mesri-Gundoshmian

et al. 2020

Plants

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Site-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively.

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Section: Pre-processing and Segmentationmentioning

confidence: 99%

Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields

Dadashzadeh

Abbaspour‐Gilandeh

Mesri-Gundoshmian

et al. 2020

Plants

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“…Previous studies have based their criteria for selection on an Index that stands out green component of source image; Excess Green Index [7,8] and Normalized Difference Vegetation Index [9][10][11] are some methods that use this approach, however, they are aimed to perform on different sunlight and background conditions. In this project, the module for the weed remover robot will have a camera obscura and lamps in order to maintain uniform illumination.…”

Section: Green Plant Detection Algorithmmentioning

confidence: 99%

Machine vision system for weed detection using image filtering in vegetables crops

Pulido-Rojas

Molina-Villa

Solaque-Guzmán

2016

Rev. Fac. Ing. Univ. Antioquia

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124test results obtain between 70.4 and 72.5% classification accuracy. Another work involves two processes: image segmentation and decision [2]. Source image is binarized applying a threshold from RGB format; with plant pixels, crop row direction is estimated; next, a Support Vector Machine classifier is trained and implemented to decide presence of weed offering an optimum performance in terms of computational cost.In [3], a work to identify weeds using an off-line training from RGB images of corn, these images are segmented to differentiate vegetation and soil, estimating a probability density function and partitioning the image into cells formed detecting from culture lines using Hough transform. In operation, the system calculates the cells of a new image and applies Bayes' theorem to make the decision process of belonging to the Weed class. The authors in [4] use a JAI AD-130 machine vision camera capable of simultaneously capturing visible and near-infrared light spectrums; they characterized soil, green and dry plants using difference between red grey level and near infrared grey level (index NIR-R) in crop images, achieving a clear threshold for separate and segment objects in the scene; the results indicate that 0 to 50 grey level for dry plants and 150 to 250 for green plants is a good way to classify vegetation, suggesting that plants and soil can be separated using a threshold from histogram. Another work about weed Machine vision system for weed detection using image filtering in vegetables cropsABSTRACT: This work presents a machine vision system for weed detection in vegetable crops using outdoor images, avoiding lighting and sharpness problems during acquisition step. This development will be a module for a weed removal mobile robot with camera obscura (Latin for "dark room") for lighting controlled conditions. The purpose of this paper is to develop a useful algorithm to discriminate weed, using image filtering to extract color and area features, then, a process to label each object in the scene is implemented, finally, a classification based on area is proposed, including sensitivity, specificity, positive and negative predicted values in order to evaluate algorithm performance. RESUMEN:El presente trabajo expone un sistema de visión de máquina para la detección de maleza en cultivos de hortalizas, usando imágenes exteriores, evadiendo problemas de iluminación y nitidez durante la etapa de adquisición, ya que el presente desarrollo será un módulo para un robot móvil removedor de maleza con una cámara oscura para controlar las condiciones de luz. El enfoque de diseño se enmarca en el desarrollo de un algoritmo útil para discriminar maleza usando filtros en la imagen extrayendo características de color y área, luego se implementa un proceso de etiquetado para cada objeto en la escena. Finalmente, una clasificación basada en área es propuesta incluyendo el cálculo de los índices de sensibilidad, especificidad, valores predictivos positivos y negativos con el fin de evaluar el rendimiento del algor...

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“…Many researches have been conducted to segment green plants from background using indices from RGB or rgb. Excess Green Index (EGI = 2g-r-b) was originally developed by Wobbecke et al (1995) and has been widely cited and used in recent studies (Muangkasem et al, 2010). Normalized Difference Index (NDI = (g-r)/(g+r)) or called pseudo Normalized Difference Vegetation Index (pseudo NDVI) was firstly proposed by Woebbecke et al (1992).…”

Section: The Green Plant Detection Methodsmentioning

confidence: 99%

Development of a Machine Vision System for Weed Detection During Both of Off-Season and in-Season in Broadacre No-Tillage Cropping Lands

Liu

Lee

Saunders

2014

American Journal of Agricultural and Biological Sciences

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More than half of the Australian cropping land is no-tillage and weed control within continuous no-tillage agricultural cropping area is becoming more and more difficult. A major problem is that the heavy herbicide usage causes some of more prolific weeds becoming more resistant to the regular herbicides and therefore more powerful and more expensive options are being pursued. To overcome such problems with aiming at the reduction of herbicide usage, this proposed research focuses on developing a machine vision system which can detect and mapping weeds or do spot spray. The weed detection methods described in this study include three aspects which are image acquisition, a new green plant detection algorithm using hybrid spectral indices and a new inter-row weed detection method taking the advantage of the location of the crop rows. The developed method could detect the weeds both during the non-growing summer period and also within the growing season until the canopy of the crop has closed. The design of the methods focuses on overcoming the challenges of the complex no-tillage background, the faster image acquisition speed and quicker processing time for real-time spot spray. The experiment results show that the proposed method are more suitable for the weed detection in the notillage background than the existing methods and could be used as a powerful tool for the weed control.

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Weed Detection over Between-Row of Sugarcane Fields Using Machine Vision with Shadow Robustness Technique for Variable Rate Herbicide Applicator

Cited by 8 publications

References 7 publications

Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields

Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields

Machine vision system for weed detection using image filtering in vegetables crops

Development of a Machine Vision System for Weed Detection During Both of Off-Season and in-Season in Broadacre No-Tillage Cropping Lands

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