Efficient and timely post-emergence weed control based on herbicide use is a crucial task in crop production because a wrong early weed management not only reduces the crop yield (due to weed competition) but also increases the negative impacts of herbicides on the environment.
Inappropriate weed management is often related with two main problems. The first is applying herbicides when weeds are not in the suitable phenological stage (generally the correct growth stage is when weeds have 4-6 true leaves, although this slightly depends on specific weed species or group of species), the second is broadcasting herbicides over the whole field, even when weed-free areas are present due to the widely demonstrated weed patchy distribution. The first problem is usually addressed using the expert knowledge of farmers. The other problem can be overcome by developing site-specific weed management (SSWM) strategies according to weed emergence. These strategies may consist of both a single herbicide treatment to weed patches where a unique group of weeds is present (for example either grass or broadleaved weeds), or use of several herbicides according to the presence of different weed species or group composition, such as grass, broadleaved weeds or a specific problematic weed (i.e., a herbicide resistant weed).
To determine the weed emergence, it is necessary to generate the weed cover maps, which allow the translation of the spatial distribution of the weed infestation into site-specific herbicide treatment maps. Remote sensing using imagery from an unmanned aerial vehicle (UAV) is now the principal source to monitor weeds at very early phenological stage in a cost effective way due they can carry (even simultaneously) different sensors to register information at diverse spectral ranges, fly at different altitudes to adjust the desired high spatial resolution, and be programmed on demand at key stages of the crops. This is critical when detecting weeds in crops for early post-emergence SSWM when crops and weeds are at the same early phenological stage and they show spectral and visual similarities.
This article is a part of a wide research included in an overall program to investigate the prospects and limitations of UAV imagery in the accurate mapping of a set of problematic and perennial weeds for sustainable and precise use of herbicides by covering a range of technological and methodological key components. Summarizing the following aspects were developed and evaluated:
i) the versatility of the configuration of onboard sensors,
ii) flight altitude,
iii) flight planning,
iv) the percentage of forward (lateral) and side (longitudinal) overlapped imagery and the corresponding ortho-rectification to create an accurately geo-referenced orthomosaicked image of the entire plot for further classification,
v) the development of object-based-image-analysis for an early weed detection emerged between the crop-rows, and finally
vi) the selection of patterns and features not only for a between but also for a within crop-row early weed detection and mapping.
The results clearly demonstrate the feasibility of the UAV-orthomosaicked imagery for both the early detection and mapping of weeds in crops and the saving of herbicides (up to 70% depending on the weed infestation). These findings thus are very useful for designing a field program for the site-specific herbicide applications in the entire field at very early post-emergence. This also provides a base for a future study focused on a number of specific and frequent field conditions, such as the early detection of weeds in curved crop rows.
This global work was supported by the projects AGL2014-52465-C4-4-R (Spanish MINECO, EU-FEDER funds) and RECUPERA-2020 (an agreement between CSIC
and Spanish MINECO, EU-FEDER funds).
Figure credit: Dr. Francisca López-Granados, CSIC, Spanish National Research Council
María Pérez-Ortiz 1,3 , José Manuel Peña1 , Pedro Antonio Gutiérrez2 , Jorge Torres-Sánchez1 , César Hervás-Martínez2 , Francisca López-Granados1 . Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery. Expert Systems with Applications, Volume 47, 2016, Pages 85–94.Show Affiliations
- Institute for Sustainable Agriculture, CSIC, P.O. Box 4084, 14080-Córdoba, Spain.
- Department of Computer Science and Numerical Analysis, University of Córdoba, Rabanales Campus, C2 building, 14071-Córdoba, Spain.
- Department of Mathematics and Engineering, Universidad Loyola Andalucía, third building, 14004-Córdoba, Spain.
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