Plant functional diversity is affected by weed management through processes of trait convergence and divergence.

Weed management involving tillage and/or herbicides has generally led to a decline of plant diversity in agroecosystems, with negative impacts on ecosystem services provision. The use of plant covers has become the predominant alternative in vineyard management, with numerous studies focusing on analyzing the advantages and disadvantages of plant covers compared to the aforementioned management. Although the impacts of weed management on taxonomic diversity have been widely studied, many gaps remain on their effects on plant functional diversity. As plant functional diversity is linked to the delivery of key ecosystem services in agroecosystems, understanding these effects could enable the development of more sustainable practices. From 2008 to 2018, a long-term trial was carried out in a Mediterranean vineyard to assess different agricultural practices. In this article, we examined how weed management, as well as irrigation use, could affect plant functional diversity. Based on 10 functional traits, such as plant height, specific leaf area or seed mass, we measured different indices of functional diversity and used null models to detect processes of trait convergence and divergence. Our results revealed that weed management and irrigation use had a significant effect on plant functional diversity. Mown plots showed the highest functional richness but were functionally convergent, since mowing was a strong functional filter on most of the traits. Tillage also behaved as a functional filter on some vegetative traits, but favored the divergence of certain reproductive traits. Herbicide-treated and irrigated plots showed the highest values of functional divergence by promoting more competitive species with more divergent trait values. The effect of weed management on these community assembly processes was shaped by the use of irrigation in vineyard rows, leading to functional divergence in those vegetative traits related to resource acquisition and seed mass. These results suggest that greater functional diversity may be associated with the bias caused by higher occurrence of competitive species (e.g. Convolvulus arvensis, Sonchus asper) with contrasting values for certain traits. Therefore, since these species are considered harmful to crops, higher plant functional diversity might not be a desirable indicator in agroecosystems.

Use of Under-Vine Living Mulches to Control Noxious Weeds in Irrigated Mediterranean Vineyards.

This article assesses the use of under-vine living mulches in Mediterranean vineyards characterized by limited water resources, one of the reasons why this agronomic practice is currently unusual in these environments. The aim of the study was to test whether the use of this alternative method in Mediterranean vineyards could suppress noxious weeds without hindering optimal vineyard development. For this purpose, four native species were selected as living mulches: Festuca ovina, Pilosella officinarum, Plantago coronopus, and Plantago lanceolata. The variables measured during three years in two different experimental farms were: (a) living mulch cover, as a possible predictor of weed suppression success; (b) weed density and weed biomass, with special attention to noxious weed species; and (c) pruning weights, measured in the last year to analyze the cumulative effect of the treatments on the grapevine vegetative growth. Our results revealed that living mulches with high cover rates (average over 70%) also showed weed suppression of up to 95%, significantly controlling the occurrence of noxious weeds such as Erigeron canadensis. No significant effect of the different treatments on vine vegetative growth was found, although further studies would be necessary. Based on these findings, it can be concluded that under-vine living mulches could be an efficient and environmentally friendly method for weed control in Mediterranean vineyards where irrigation is available.

Is Pasture Cropping a Valid Weed Management Tool.

The aim of the present work was to study the feasibility of pasture cropping under the Mediterranean conditions prevailing in central Spain and its potential as a weed management tool. Three cropping systems were assessed: conventionally grown winter barley and winter barley in pasture cropping with two perennial summer species, Cynodon dactylon and Eragrostis curvula. The results showed that the growth of these two species in a pasture cropping system was limited by the severe drought conditions and high temperatures present during the summer in some of the study years. Although there were no differences in the establishment of winter barley in any of the treatments assessed, pasture cropping reduced winter barley yields up to 50%-60% in years with low rainfall in spring. Regarding weed control, pasture cropping showed a significant suppression of the total weed density and number of weed species. As a conclusion, pasture cropping can be considered as a valid weed management tool. However, the economic feasibility of this system under the climatic conditions of central Spain (characterized by a high risk of severe summer droughts) is still not clear. The availability of supplemental irrigation may reduce competition between pastures and winter crops and ensure a profitable production of summer pastures.

Three-Dimensional Modeling of Weed Plants Using Low-Cost Photogrammetry.

Sensing advances in plant phenotyping are of vital importance in basic and applied plant research. Plant phenotyping enables the modeling of complex shapes, which is useful, for example, in decision-making for agronomic management. In this sense, 3D processing algorithms for plant modeling is expanding rapidly with the emergence of new sensors and techniques designed to morphologically characterize. However, there are still some technical aspects to be improved, such as an accurate reconstruction of end-details. This study adapted low-cost techniques, Structure from Motion (SfM) and MultiView Stereo (MVS), to create 3D models for reconstructing plants of three weed species with contrasting shape and plant structures. Plant reconstruction was developed by applying SfM algorithms to an input set of digital images acquired sequentially following a track that was concentric and equidistant with respect to the plant axis and using three different angles, from a perpendicular to top view, which guaranteed the necessary overlap between images to obtain high precision 3D models. With this information, a dense point cloud was created using MVS, from which a 3D polygon mesh representing every plants' shape and geometry was generated. These 3D models were validated with ground truth values (e.g., plant height, leaf area (LA) and plant dry biomass) using regression methods. The results showed, in general, a good consistency in the correlation equations between the estimated values in the models and the actual values measured in the weed plants. Indeed, 3D modeling using SfM algorithms proved to be a valuable methodology for weed phenotyping, since it accurately estimated the actual values of plant height and LA. Additionally, image processing using the SfM method was relatively fast. Consequently, our results indicate the potential of this budget system for plant reconstruction at high detail, which may be usable in several scenarios, including outdoor conditions. Future research should address other issues, such as the time-cost relationship and the need for detail in the different approaches.

Influence of Wind Speed on RGB-D Images in Tree Plantations.

Weather conditions can affect sensors' readings when sampling outdoors. Although sensors are usually set up covering a wide range of conditions, their operational range must be established. In recent years, depth cameras have been shown as a promising tool for plant phenotyping and other related uses. However, the use of these devices is still challenged by prevailing field conditions. Although the influence of lighting conditions on the performance of these cameras has already been established, the effect of wind is still unknown. This study establishes the associated errors when modeling some tree characteristics at different wind speeds. A system using a Kinect v2 sensor and a custom software was tested from null wind speed up to 10 m·s-1. Two tree species with contrasting architecture, poplars and plums, were used as model plants. The results showed different responses depending on tree species and wind speed. Estimations of Leaf Area (LA) and tree volume were generally more consistent at high wind speeds in plum trees. Poplars were particularly affected by wind speeds higher than 5 m·s-1. On the contrary, height measurements were more consistent for poplars than for plum trees. These results show that the use of depth cameras for tree characterization must take into consideration wind conditions in the field. In general, 5 m·s-1 (18 km·h-1) could be established as a conservative limit for good estimations.

An Approach to the Use of Depth Cameras for Weed Volume Estimation.

The use of depth cameras in precision agriculture is increasing day by day. This type of sensor has been used for the plant structure characterization of several crops. However, the discrimination of small plants, such as weeds, is still a challenge within agricultural fields. Improvements in the new Microsoft Kinect v2 sensor can capture the details of plants. The use of a dual methodology using height selection and RGB (Red, Green, Blue) segmentation can separate crops, weeds, and soil. This paper explores the possibilities of this sensor by using Kinect Fusion algorithms to reconstruct 3D point clouds of weed-infested maize crops under real field conditions. The processed models showed good consistency among the 3D depth images and soil measurements obtained from the actual structural parameters. Maize plants were identified in the samples by height selection of the connected faces and showed a correlation of 0.77 with maize biomass. The lower height of the weeds made RGB recognition necessary to separate them from the soil microrelief of the samples, achieving a good correlation of 0.83 with weed biomass. In addition, weed density showed good correlation with volumetric measurements. The canonical discriminant analysis showed promising results for classification into monocots and dictos. These results suggest that estimating volume using the Kinect methodology can be a highly accurate method for crop status determination and weed detection. It offers several possibilities for the automation of agricultural processes by the construction of a new system integrating these sensors and the development of algorithms to properly process the information provided by them.

Functionality of Selected Aromatic Lamiaceae in Attracting Pollinators in Central Spain.

Planting floral margins in agricultural landscapes has been shown to increase the abundance of pollinators in agro-ecosystems. However, to create efficient margins, it is necessary to use attractive, not weedy native plants with different blooming periods to prolong the availability of floral resources. Six native perennial plants of the Lamiaceae with different blooming periods were studied in a randomized block design, with the final aim to select the most efficient plants in floral mixtures by studying relationships between their floral phenology, floral density, and attractiveness to pollinators in Central Spain. In addition, their spatial expansion, i.e., potential weediness, was estimated under the field conditions, as the final purpose of the plants is to be implemented within the agro-ecosystems. The results showed that plant species with higher floral density (Nepeta tuberosa L. and Hyssopus officinalis L.) showed significantly higher attractiveness to pollinators and enhanced the attractiveness of floral mixtures. Species that bloomed in early spring (Salvia verbenaca L.) and in summer (Melissa officinalis L. and Thymbra capitata L.) did not efficiently contribute to the attractiveness of the mixtures to pollinators. In addition, besides high floral density of Salvia officinalis L. and N. tuberosa in the spring, warm and dry weather in spring 2012 enhanced the activity of bees, while cold and rainy weather in spring 2013 enhanced the activity of hoverflies. None of the plants showed weedy growth and so posed no danger of invading adjacent crops.

Matching the best viewing angle in depth cameras for biomass estimation based on poplar seedling geometry.

In energy crops for biomass production a proper plant structure is important to optimize wood yields. A precise crop characterization in early stages may contribute to the choice of proper cropping techniques. This study assesses the potential of the Microsoft Kinect for Windows v.1 sensor to determine the best viewing angle of the sensor to estimate the plant biomass based on poplar seedling geometry. Kinect Fusion algorithms were used to generate a 3D point cloud from the depth video stream. The sensor was mounted in different positions facing the tree in order to obtain depth (RGB-D) images from different angles. Individuals of two different ages, e.g., one month and one year old, were scanned. Four different viewing angles were compared: top view (0°), 45° downwards view, front view (90°) and ground upwards view (-45°). The ground-truth used to validate the sensor readings consisted of a destructive sampling in which the height, leaf area and biomass (dry weight basis) were measured in each individual plant. The depth image models agreed well with 45°, 90° and -45° measurements in one-year poplar trees. Good correlations (0.88 to 0.92) between dry biomass and the area measured with the Kinect were found. In addition, plant height was accurately estimated with a few centimeters error. The comparison between different viewing angles revealed that top views showed poorer results due to the fact the top leaves occluded the rest of the tree. However, the other views led to good results. Conversely, small poplars showed better correlations with actual parameters from the top view (0°). Therefore, although the Microsoft Kinect for Windows v.1 sensor provides good opportunities for biomass estimation, the viewing angle must be chosen taking into account the developmental stage of the crop and the desired parameters. The results of this study indicate that Kinect is a promising tool for a rapid canopy characterization, i.e., for estimating crop biomass production, with several important advantages: low cost, low power needs and a high frame rate (frames per second) when dynamic measurements are required.

Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor.

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.

Accuracy and feasibility of optoelectronic sensors for weed mapping in wide row crops.

The main objectives of this study were to assess the accuracy of a ground-based weed mapping system that included optoelectronic sensors for weed detection, and to determine the sampling resolution required for accurate weed maps in maize crops. The optoelectronic sensors were located in the inter-row area of maize to distinguish weeds against soil background. The system was evaluated in three maize fields in the early spring. System verification was performed with highly reliable data from digital images obtained in a regular 12 m × 12 m grid throughout the three fields. The comparison in all these sample points showed a good relationship (83% agreement on average) between the data of weed presence/absence obtained from the optoelectronic mapping system and the values derived from image processing software ("ground truth"). Regarding the optimization of sampling resolution, the comparison between the detailed maps (all crop rows with sensors separated 0.75 m) with maps obtained with various simulated distances between sensors (from 1.5 m to 6.0 m) indicated that a 4.5 m distance (equivalent to one in six crop rows) would be acceptable to construct accurate weed maps. This spatial resolution makes the system cheap and robust enough to generate maps of inter-row weeds.