In a semiarid Mediterranean site in central Spain, field experiments were conducted on a Calcic Haploxeralf (noncalcic brown soil), which had been managed with three crop rotations and two tillage systems (no-tillage and conventional tillage) since 1987. The crop rotations consisted of barley→vetch, barley→sunflower, and a barley monoculture. The study took place in two growing seasons (1992–1994) to assess the effects of management practices on the weed seedbank. During this period, spring weed control was not carried out in winter crops. In the no-tillage system, there was a significant increase in the number of seeds of different weed species: anacyclus, common purslane, corn poppy, knotted hedge-parsley, mouse-ear cress, spring whitlowgrass, tumble pigweed, venus-comb, andVeronica triphyllos.Conversely, the presence of prostrate knotweed and wild radish was highest in plots under conventional tillage. These results suggest large differences in the weed seedbank as a consequence of different soil conditions among tillage systems, but also the necessity of spring weed control when a no-tillage system is used. With regard to crop rotations, the number of seeds of knotted hedge-parsley, mouse-ear cress, and spring whitlowgrass was greater in the plots under the barley→vetch rotation. Common lambsquarters dominated in the plots under the barley→sunflower rotation, whereas venus-comb was the most frequent weed in the barley monoculture. Larger and more diverse weed populations developed in the barley→vetch rotation rather than in the barley→sunflower rotation or the barley monoculture.
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.
Summary Predictive empirical models of the timing of emergence were developed for ten major weed species in maize crops. Monitoring of seedling emergence was performed over two years in two maize fields located in Central Spain and Tagus Valley in Portugal. Thermal time was used as the independent variable for predicting cumulative emergence. Different non‐linear growth curves were fitted to the data sets of cumulative percent emergence for the different species, sites and years using genetic algorithms. Based on their emergence patterns, weed species were arranged into three groups. Species with early‐season emergence (Abutilon theophrasti, Xanthium strumarium, Datura stramonium, Datura ferox, Sorghum halepense, Digitaria sanguinalis and Echinochloa crus‐galli) reached 70% emergence with less than 700 growing day degrees (GDD). Species with whole‐season emergence (Cyperus rotundus and Solanum nigrum) started early their emergence processes but the emergence continued throughout the maize life‐cycle; they required up to 1300 GDD to reach 70% emergence. The only species with late‐season emergence was Sonchus oleraceus; it required more than 1300 GDD to reach 70% emergence. The results obtained in our experiments have shown a good synchrony between the predictions obtained in different years in the same site. However, no single model was able to predict the timing of emergence in two sites with different environmental conditions, challenging the hypothesis that a single general model, based on temperature only, can be used to predict weed emergence in different geographical locations.
In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12–14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.