A Novel Approach for Weed Type Classification Based on Shape Descriptors and a Fuzzy Decision-Making Method

Herrera, Pedro Javier; Dorado, José; Ribeiro, Ángela

doi:10.3390/s140815304

Cited by 79 publications

(38 citation statements)

References 58 publications

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“…Herrera et al . () proposed a strategy for discriminating between grass and broad‐leaved weeds using a set of shape descriptors (the seven Hu moments and six geometric shape descriptors). This combined strategy worked properly when the weeds were at an early growth stage, before leaves start overlapping.…”

Section: Availability Of Weed Monitoring Technologiesmentioning

confidence: 99%

“…The opportunities and limitations of both detection approaches have been reviewed in the past by various authors (Slaughter et al ., ; López‐Granados, ; Mulla, ; Peteinatos et al ., ). These reviews note the potential of ground‐based approaches (also called proximal sensing) to capture high‐resolution images and, consequently, to allow an early detection of relatively low weed densities and discrimination of the major weed species (Weis & Sökefeld, ; Longchamps et al ., ; Herrera et al ., ; Peteinatos et al ., ). Alternatively, conventional remote sensing platforms (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Is the current state of the art of weed monitoring suitable for site‐specific weed management in arable crops?

Fernández‐Quintanilla

et al. 2018

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Weed monitoring is the first step in any site-specific weed management programme. A relatively large variety of platforms, cameras, sensors and image analysis procedures are available to detect and map weed presence/abundance at various times and spatial scales. Remote sensing from satellites or aircraft can provide accurate weed maps when the images are obtained at late weed phenological stages. Cameras located on unmanned aerial vehicles (UAVs) have been shown to be adequate for early-season weed detection in a variety of wide-row crops, providing images with relatively high spatial resolutions. Alternatively, weed detection/ mapping systems from ground-based platforms can achieve even higher resolutions using a variety of nonimaging and imaging technologies. These ground systems are suited, in some cases, for real-time site-specific weed management. Despite this rich arsenal of technologies, their commercial adoption is, apparently, low. In this study, we describe the state of the art of remotely sensed and ground-based weed monitoring in arable crops and the current level of adoption of these technologies, exploring major constraints for adoption and trying to identify research gaps and bottlenecks.

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Section: Availability Of Weed Monitoring Technologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Is the current state of the art of weed monitoring suitable for site‐specific weed management in arable crops?

Fernández‐Quintanilla

et al. 2018

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show abstract

“…Common methods to recognize Latin texts were revised but do not work well when there is noise in the image. Therefore, it is difficult to extract the regions, calculate descriptors or moments and use decision-making methods as proposed in previous works [12,13]. …”

Section: Resultsmentioning

confidence: 99%

“…It was initially considered extracting the hieroglyphs using a conversion to grey scale and applying a threshold and the Hu moments [11,12,13]. It could be a right method if the color of the hieroglyphs was different.…”

Section: Introductionmentioning

confidence: 99%

Deciphering Egyptian Hieroglyphs: Towards a New Strategy for Navigation in Museums

Domingo

Herrera

Valero

et al. 2017

Sensors

Self Cite

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This work presents a novel strategy to decipher fragments of Egyptian cartouches identifying the hieroglyphs of which they are composed. A cartouche is a drawing, usually inside an oval, that encloses a group of hieroglyphs representing the name of a monarch. Aiming to identify these drawings, the proposed method is based on several techniques frequently used in computer vision and consists of three main stages: first, a picture of the cartouche is taken as input and its contour is localized. In the second stage, each hieroglyph is individually extracted and identified. Finally, the cartouche is interpreted: the sequence of the hieroglyphs is established according to a previously generated benchmark. This sequence corresponds to the name of the king. Although this method was initially conceived to deal with both high and low relief writing in stone, it can be also applied to painted hieroglyphs. This approach is not affected by variable lighting conditions, or the intensity and the completeness of the objects. This proposal has been tested on images obtained from the Abydos King List and other Egyptian monuments and archaeological excavations. The promising results give new possibilities to recognize hieroglyphs, opening a new way to decipher longer texts and inscriptions, being particularly useful in museums and Egyptian environments. Additionally, devices used for acquiring visual information from cartouches (i.e., smartphones), can be part of a navigation system for museums where users are located in indoor environments by means of the combination of WiFi Positioning Systems (WPS) and depth cameras, as unveiled at the end of the document.

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“…The shape descriptors in combination with the fuzzy decision-making method are also employed to classify weed species [7].…”

Section: Introductionmentioning

confidence: 99%

Smart Agricultural Machine with a Computer Vision-Based Weeding and Variable-Rate Irrigation Scheme

Chang

Lin

2018

Robotics

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This paper proposes a scheme that combines computer vision and multi-tasking processes to develop a small-scale smart agricultural machine that can automatically weed and perform variable rate irrigation within a cultivated field. Image processing methods such as HSV (hue (H), saturation (S), value (V)) color conversion, estimation of thresholds during the image binary segmentation process, and morphology operator procedures are used to confirm the position of the plant and weeds, and those results are used to perform weeding and watering operations. Furthermore, the data on the wet distribution area of surface soil (WDAS) and the moisture content of the deep soil is provided to a fuzzy logic controller, which drives pumps to perform variable rate irrigation and to achieve water savings. The proposed system has been implemented in small machines and the experimental results show that the system can classify plant and weeds in real time with an average classification rate of 90% or higher. This allows the machine to do weeding and watering while maintaining the moisture content of the deep soil at 80 ± 10% and an average weeding rate of 90%.

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A Novel Approach for Weed Type Classification Based on Shape Descriptors and a Fuzzy Decision-Making Method

Cited by 79 publications

References 58 publications

Is the current state of the art of weed monitoring suitable for site‐specific weed management in arable crops?

Is the current state of the art of weed monitoring suitable for site‐specific weed management in arable crops?

Deciphering Egyptian Hieroglyphs: Towards a New Strategy for Navigation in Museums

Smart Agricultural Machine with a Computer Vision-Based Weeding and Variable-Rate Irrigation Scheme

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