Impact of site-specific weed management on herbicide savings and winter wheat yield

Hamouz, Pavel; Hamouzová, Kateřina; Holec, J.; Tyšer, Luděk

doi:10.17221/599/2012-pse

Cited by 35 publications

(21 citation statements)

References 12 publications

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“…Usually, the herbicide application window depends on weather conditions, weed species and crops, and can be as short as a two-to three-day window. Although some researchers have achieved interesting results in terms of weed detection and herbicide savings based on on-the-go weed detection systems and field surveys [56][57][58], they are time consuming and costly. It could delay the The optimization of weed management practices relies on timing weed control [51], as weed presence in the early crop season is a key component of the weed-crop competition and further yield loss [52,53].…”

Section: Prescription Mapsmentioning

confidence: 99%

An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

et al. 2018

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Accurate and timely detection of weeds between and within crop rows in the early growth stage is considered one of the main challenges in site-specific weed management (SSWM). In this context, a robust and innovative automatic object-based image analysis (OBIA) algorithm was developed on Unmanned Aerial Vehicle (UAV) images to design early post-emergence prescription maps. This novel algorithm makes the major contribution. The OBIA algorithm combined Digital Surface Models (DSMs), orthomosaics and machine learning techniques (Random Forest, RF). OBIA-based plant heights were accurately estimated and used as a feature in the automatic sample selection by the RF classifier; this was the second research contribution. RF randomly selected a class balanced training set, obtained the optimum features values and classified the image, requiring no manual training, making this procedure time-efficient and more accurate, since it removes errors due to a subjective manual task. The ability to discriminate weeds was significantly affected by the imagery spatial resolution and weed density, making the use of higher spatial resolution images more suitable. Finally, prescription maps for in-season post-emergence SSWM were created based on the weed maps-the third research contribution-which could help farmers in decision-making to optimize crop management by rationalization of the herbicide application. The short time involved in the process (image capture and analysis) would allow timely weed control during critical periods, crucial for preventing yield loss.

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Section: Prescription Mapsmentioning

confidence: 99%

An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

et al. 2018

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“…A previous paper by Hamouz et al (2013) demonstrated that when using SSWM in winter wheat herbicide savings of 51-91% were possible for grass weeds and of 16-84% for dicotyledonous weeds in the same field. In winter rape, SSWM provided even greater post-emergent herbicide savings, ranging between 71.9% and 100%, depending on treatment (Table 4).…”

Section: Methodsmentioning

confidence: 99%

“…The threshold values used in winter rape are listed in Table 1. The threshold values and herbicides used in winter wheat are presented in Hamouz et al (2013).…”

Section: Methodsmentioning

confidence: 99%

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Effect of site-specific weed management in winter crops on yield and weed populations

Hamouz¹,

Hamouzová²,

Tyšer³

et al. 2014

PLANT SOIL ENVIRON

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Site-specific weed management (SSWM) methods allow spatially variable treatment of weed populations according to actual weed abundance, thus offering the opportunity for herbicide savings. However, SSWM's effect on weed population dynamics is not sufficiently understood. In this study, SSWM was conducted based on various application thresholds to analyse the effects on crop yield and weed infestation in the succeeding crop. SSWM was used on a 3.07 ha experimental field in winter wheat (2011) and winter oilseed rape (2012). The whole area was split into application cells of 6 × 10 m and abundance of all weed species was evaluated manually in each cell. Four different herbicide treatments were tested. Standard whole-field herbicide application (blanket spraying) was treatment 1. Treatments 2, 3 and 4 comprised SSWM using different thresholds for post-emergent herbicide application. SSWM resulted in savings of post-emergent herbicides ranging from 71.9% to 100%, depending on the application threshold. Differences in winter rape yield among treatments were generally small and statistically insignificant (P = 0.989). Although some minor changes in weed abundances were observable, the experiment showed that none of the site-specific herbicide treatments caused a significant (α = 0.05) increase of weed species abundance compared to the standard treatment.

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“…For Rizzardi and Fleck (2004), densities of 4 or more plants per m -2 of the common weed Bidens pilosa L. can limit the development of soybeans. Hamouz et al (2013) organized the management of weeds for a specific site on a 4 x 1.5-meter grid using a threshold of weed density of 0.02 to 0.20% (0.10 to 15.51 plants m -2 ). They found this procedure led to a reduction in herbicides needed from 15.6 to 100%, depending on the type of herbicide and threshold of application used.…”

Section: Weed Infestation Level Based On Expert Evaluationmentioning

confidence: 99%

Development and testing of image processing algorithm to estimate weed infestation level in corn fields

Santiago¹,

Leite²,

Teruel³

et al. 2016

Aust J Crop Sci

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The challenges of modern agriculture have led to the development of localized management tools which allow the rationalization of the use and application of pesticides, a reduction in production costs and the optimization of agricultural processes. This study was carried out to develop an algorithm capable of orienting weed control in the management of a corn crop, using digital image analysis to identify the level of weed infestation in the field. The seeds of six species of weed were sown in an experimental plot of corn, and daily images were captured for 40 days for the evaluation of the level of weed infestation (low, intermediate or high). The algorithm developed was able to target information about the plants and soil accurately and discriminate the residual information as referring to either the culture or weeds. The proposed algorithm has achieved 90% accuracy in identifying the level of infestation from images already evaluated by experts. The results can thus be used as part of weed control strategy, with the incorporation of the geographic coordinates of the image making possible the construction of a map of the level of weed infestation in the different areas where the crop is growing.

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Impact of site-specific weed management on herbicide savings and winter wheat yield

Cited by 35 publications

References 12 publications

An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

Effect of site-specific weed management in winter crops on yield and weed populations

Development and testing of image processing algorithm to estimate weed infestation level in corn fields

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