A5029236536- Weed Control and Herbicide Applications
- Agronomic Practices and Intercropping Systems
- Plant and soil sciences
- Smart Agriculture and AI
- Biological Control of Invasive Species
- Agriculture, Plant Science, Crop Management
- Crop Yield and Soil Fertility
- Ecology and Vegetation Dynamics Studies
- Allelopathy and phytotoxic interactions
- Remote Sensing in Agriculture
- Agricultural and Food Production Studies
- Plant and animal studies
- Nematode management and characterization studies
- Horticultural and Viticultural Research
- Pesticide and Herbicide Environmental Studies
- Bioenergy crop production and management
- Seed Germination and Physiology
- Banana Cultivation and Research
- Remote Sensing and LiDAR Applications
- Historical and socio-economic studies of Spain and related regions
- Leaf Properties and Growth Measurement
- Mediterranean and Iberian flora and fauna
- Plant Disease Management Techniques
- Spectroscopy and Chemometric Analyses
- Insect-Plant Interactions and Control
Consejo Superior de Investigaciones Científicas
2014-2024
Instituto de Ciencias Agrarias
2014-2024
Universidad Peruana Cayetano Heredia
2015
Instituto Nacional de Investigaciones Agropecuarias
2011
Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria
2011
National Research Council
2010
Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
1996-2008
Instituto de Agricultura Sostenible
2004
Comunidad de Madrid
1988-1995
Feeding the growing global population requires an annual increase in food production. This requirement suggests use of pesticides, which represents unsustainable chemical load for environment. To reduce pesticide input and preserve environment while maintaining necessary level production, efficiency relevant processes must be drastically improved. Within this context, research strived to design, develop, test assess a new generation automatic robotic systems effective weed pest control aimed...
Abstract The developments of information and automation technologies have opened a new era for weed management to fit physical chemical control treatments the spatial temporal heterogeneity distributions in agricultural fields. This review describes site‐specific (SSWM) systems, evaluates their ecological economic benefits gives perspective implementation practical farming. Sensor including 3D cameras, multispectral imaging Artificial Intelligence (AI) classification computer‐based decision...
Summary 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 map presence/abundance at various times spatial scales. Remote sensing from satellites or aircraft can provide accurate maps when images obtained late phenological stages. Cameras located on unmanned aerial vehicles (UAVs) have been shown be adequate for early‐season detection a wide‐row...
The use of depth cameras in precision agriculture is increasing day by day. This type sensor has been used for the plant structure characterization several crops. However, discrimination small plants, such as weeds, still a challenge within agricultural fields. Improvements new Microsoft Kinect v2 can capture details plants. dual methodology using height selection and RGB (Red, Green, Blue) segmentation separate crops, soil. paper explores possibilities this Fusion algorithms to reconstruct...
In this study, the evaluation of accuracy and performance a light detection ranging (LIDAR) sensor for vegetation using distance reflection measurements aiming to detect discriminate maize plants weeds from soil surface was done. The study continues previous work carried out in field Spain with LIDAR exclusively one index, height profile. current system uses combination two mentioned indexes. experiment at growth stage 12–14, 16 different locations selected represent widest possible density...
Accurate weed species identification is crucial for effective site-specific management (SSWM), enabling targeted and timely control measures each in crop field. This study advanced the current approach to species-level during early growth stage by integrating unmanned aerial vehicles (UAVs) imagery with standard convolutional neural networks (CNNs) models such as VGG16, Resnet152 Inception-Resnet-v2. For this, a robust dataset was created 33,467 labels of weeds (Atriplex patula, Chenopodium...
Summary The dispersal of Avena spp. ( A. fatua and sterilis ) by natural dissemination agricultural operations was studied in four experiments conducted Spain Britain. Natural very limited, with a maximum distance 1.5 m. Dispersal higher the geographic direction that downwind than any other three directions. Although plant movement small under no‐tillage, an annual patch displacement 2–3 m tillage observed conventional soil tillage. Ploughing downhill resulted much larger distances ploughing...
(1) A mathematical model for simulating the population dynamics of Avena sterilis ssp. ludoviciana (Dur.) Nyman has been constructed using previously reported data. The considers age structure seedlings as well effects density on plant survivorship and reproduction. (2) is used to describe behaviour in absence control practices predict various strategies. In control, under continuous winter cereal cropping, grows hyperbolically, reaching equilibrium at a 535 plants m-2. Annual application...
Sensing advances in plant phenotyping are of vital importance basic and applied research. Plant enables the modeling complex shapes, which is useful, for example, decision-making agronomic management. In this sense, 3D processing algorithms expanding rapidly with emergence new sensors techniques designed to morphologically characterize. However, there still some technical aspects be improved, such as an accurate reconstruction end-details. This study adapted low-cost techniques, Structure...
Summary Predictive empirical models of the timing emergence were developed for ten major weed species in maize crops. Monitoring seedling was performed over two years fields located Central Spain and Tagus Valley Portugal. Thermal time used as independent variable predicting cumulative emergence. Different non‐linear growth curves fitted to data sets percent different species, sites using genetic algorithms. Based on their patterns, arranged into three groups. Species with early‐season (...
A ndújar D, E scolà A, D orado J & F ernández‐ Q uintanilla C (2011). Weed discrimination using ultrasonic sensors. Research 51 , 543–547. Summary new approach is described for automatic between grasses and broad‐leaved weeds, based on their heights. An sensor was mounted the front of a tractor, pointing vertically down in inter‐row area, with control system georeferencing registering echoes reflected by ground or various leaf layers. Static measurements were taken at locations different...
Abstract Habitat management, with the aim of conserving pollinators in agro‐ecosystems, requires selection most suitable floral species terms their attractiveness to and a simplicity agronomic management. A randomized block design including 12 herbaceous plants was used study (insect visitation), efficiency (a combination duration flowering insect response two different management practices (growing mixed versus mono‐specific stands; tillage no‐tillage) potential weediness. The flowers...
Poplar is considered one of the forest crops with greatest potential for lignocellulose production, so rapid and non-destructive measurements tree growth (in terms height biomass) essential to estimate productivity poplar plantations. As an alternative tedious costly manual sampling trees, this study evaluated ability UAV technology monitor a one-year-old plantation (with trees 4.3 meters high, on average), specifically, assess dry biomass from spectral information (based Normalized...
Accurate information on the spatial distribution of weeds is key to effective site-specific weed management and efficient sustainable use control measures. This work focuses early detection johnsongrass, common cocklebur velvetleaf present in a corn field using high resolution airborne hyperspectral imagery acquired when plants were four six leaf growth stage. Following appropriate radiometric geometric corrections, two supervised classification techniques, such as spectral angle mapper...
Summary For implementation of simple yield loss models into threshold‐based weed management systems, a thorough validation is needed over great diversity sites. Yield losses by competition wsth Sinapis alba L. (white mustard) as model weed, were studied in 12 experiments sugar beet ( Beta vulgaris L.) and 11 spring wheat Triticum aestivum L.). Most data sets heller described based on the relative leaf area than hyperbolic density. This accounted for (part of) effect different emerging times...
The main objectives of this study were to assess the accuracy a ground-based weed mapping system that included optoelectronic sensors for detection, and determine sampling resolution required accurate maps in maize crops. located inter-row area distinguish weeds against soil background. was evaluated three fields early spring. System verification performed with highly reliable data from digital images obtained regular 12 m × grid throughout fields. comparison all these sample points showed...
Summary Transdisciplinary weed research ( TWR ) is a promising path to more effective management of challenging problems. We define as an integrated process inquiry and action that addresses complex problems in the context broader efforts improve economic, environmental social aspects ecosystem sustainability. seeks integrate scholarly practical knowledge across many stakeholder groups (e.g. scientists, private sector, farmers extension officers) levels local, regional landscape)....