Plant Classification Combining Colour and Spectral Cameras for Weed Control Purposes

Komi, P.J.; Jackson, Mike; Parkin, Robert M.

doi:10.1109/isie.2007.4374921

Cited by 7 publications

(4 citation statements)

References 50 publications

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“…Targeted weed control could increase the speed and accuracy and reduce costs to farmers ( Komi et al, 2007 ). The prerequisite of targeted weed control is a reliable weed identification system.…”

Section: Discussionmentioning

confidence: 99%

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

Al-Sarayreh

Irie³

et al. 2021

Front. Plant Sci.

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Weeds can be major environmental and economic burdens in New Zealand. Traditional methods of weed control including manual and chemical approaches can be time consuming and costly. Some chemical herbicides may have negative environmental and human health impacts. One of the proposed important steps for providing alternatives to these traditional approaches is the automated identification and mapping of weeds. We used hyperspectral imaging data and machine learning to explore the possibility of fast, accurate and automated discrimination of weeds in pastures where ryegrass and clovers are the sown species. Hyperspectral images from two grasses (Setaria pumila [yellow bristle grass] and Stipa arundinacea [wind grass]) and two broad leaf weed species (Ranunculus acris [giant buttercup] and Cirsium arvense [Californian thistle]) were acquired and pre-processed using the standard normal variate method. We trained three classification models, namely partial least squares-discriminant analysis, support vector machine, and Multilayer Perceptron (MLP) using whole plant averaged (Av) spectra and superpixels (Sp) averaged spectra from each weed sample. All three classification models showed repeatable identification of four weeds using both Av and Sp spectra with a range of overall accuracy of 70–100%. However, MLP based on the Sp method produced the most reliable and robust prediction result (89.1% accuracy). Four significant spectral regions were found as highly informative for characterizing the four weed species and could form the basis for a rapid and efficient methodology for identifying weeds in ryegrass/clover pastures.

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Section: Discussionmentioning

confidence: 99%

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

Al-Sarayreh

Irie³

et al. 2021

Front. Plant Sci.

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“…Suppose there are two vectors x 1 and x 2 , the Euclidean distance between them can be show in Equation (5).…”

Section: The Construction Of Bpamentioning

confidence: 99%

“…Shape features generally include area, circumference, length-width ratio, and so on. Komi et al [5] recognize the diseased crops through the combination of color feature and leaf spectrograms. However, the image acquisition facilities are expensive, and the spectrograms are difficult to get.…”

Section: Introductionmentioning

confidence: 99%

Rape Plant Disease Recognition Method of Multi-Feature Fusion Based on D-S Evidence Theory

Sun

et al. 2017

MCA

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Abstract:In view of the low accuracy and uncertainty of the traditional rape plant disease recognition relying on a single feature, this paper puts forward a rape plant disease recognition method based on Dempster-Shafer (D-S) evidence theory and multi-feature fusion. Firstly, color matrix and gray-level co-occurrence matrix are extracted as two kinds of features from rape plant images after processing. Then by calculating the Euclidean distance between the test samples and training samples, the basic probability assignment function can be constructed. Finally, the D-S combination rule of evidence is used to achieve fusion, and final recognition results are given by using the variance. This method is used to collect rape plant images for disease recognition, and recognition rate arrives at 97.09%. Compared with other methods, experimental results show that the method is more effective and with lower computational complexity.

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“…by isolating plant from soil pixels). Komi et al (2007) combined spatial information from a low-cost RGB camera with spectral data from a line scan spectral camera (400-1000 nm) to classify the detached leaves of six plant species under halogen lighting with a shaded enclosure. Whilst LEDs provide monochromatic light, halogen lamps provide a continuous spectrum of light from visible to near-infrared wavelengths, thereby making it a suitable illuminator for spectral measurements.…”

Section: Plant Materials Identificationmentioning

confidence: 99%

Applied machine vision of plants: a review with implications for field deployment in automated farming operations

McCarthy

Hancock

Raine

2010

Intel Serv Robotics

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Automated visual assessment of plant condition, specifically foliage wilting, reflectance and growth parameters, using machine vision has potential use as input for real-time variable-rate irrigation and fertigation systems in precision agriculture. This paper reviews the research literature for both outdoor and indoor applications of machine vision of plants, which reveals that different environments necessitate varying levels of complexity in both apparatus and nature of plant measurement which can be achieved. Deployment of systems to the field environment in precision agriculture applications presents the challenge of overcoming image variation caused by the diurnal and seasonal variation of sunlight.From the literature reviewed, it is argued that augmenting a monocular RGB vision system with additional sensing techniques potentially reduces image analysis complexity while enhancing system robustness to environmental variables. Therefore, machine vision systems with a foundation in optical and lighting design may potentially expedite the transition from laboratory and research prototype to robust field tool.

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Plant Classification Combining Colour and Spectral Cameras for Weed Control Purposes

Cited by 7 publications

References 50 publications

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

Rape Plant Disease Recognition Method of Multi-Feature Fusion Based on D-S Evidence Theory

Applied machine vision of plants: a review with implications for field deployment in automated farming operations

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