Search

[EN] Accurate detection and identification of weeds are essential in the implementation of Precision Agriculture (PA). In this study, we addressed the detection and classification of weeds in maize and tomato in their early growth stages by integrating images acquired from UAVs and analysis based on advanced convolutional neural networks. Subsequently, the objective of this research was the creation of geo-referenced maps that would allow localized and selective post-emergence management of weed species. Our results indicate that the combination of UAV-captured imagery and deep learning algorithm-based image analysis provides an effective solution for this purpose. The accuracy and efficiency achieved in the identification of weeds in their early stage were promising, both in the set of species present in the crop and in individual species. This advance is of great relevance in the field of PA, as it would allow a more efficient management of weeds by selecting the herbicide according to the type of weed present, reducing the use of herbicidesand ultimately contributing to the sustainability and profitability of agriculture. In addition, the generation of geo-referenced maps facilitates real-time decision making. In summary, this study suggests that the combination of emerging technologies, such as drones and deep neural networks, may be applicable tools in localized weed management in the context of PA., [ES] La detección precisa y la identificación de malas hierbas son esenciales en la implementación de la Agricultura de Precisión (AP). En este estudio, se abordó la detección y la clasificación de malas hierbas en maíz y tomate en sus etapas tempranas de crecimiento mediante la integración de imágenes adquiridas desde drones y análisis basado en avanzadas redes neuronales convolucionales. Posteriormente, el objetivo de esta investigación fue la creación de mapas georreferenciados que permitieran una gestión en post-emergencia localizada y selectiva de las especies de malas hierbas. Nuestros resultados indican que la combinación de imágenes captadas por drones y un análisis de las mismas basado en algoritmos de aprendizaje profundo proporciona una solución efectiva para este propósito. La precisión y eficiencia alcanzadas en la identificación de malas hierbas en su etapa temprana fueron prometedoras, tanto en el conjunto de especies presentes en el cultivo como en especies individuales. Este avance es de gran relevancia en el ámbito de la AP, ya que permitiría una gestión más eficiente de las malas hierbas mediante la selección del herbicida según el tipo de mala hierba presente, reduciendo el uso de herbicidas y, en última instancia, contribuyendo a la sostenibilidad y la rentabilidad de la agricultura. Además, la generación de mapas georreferenciados facilita la toma de decisiones en tiempo real. En resumen, este estudio sugiere que la combinación de tecnologías emergentes, como drones y redes neuronales profundas, pueden ser herramientas aplicables en el manejo localizado de malas hierbas en el contexto de la AP.

[EN] The use of irrigation in Mediterranean vineyards has increased significantly in recent years. Thus, in the current context of climate change, it is expected that the employment of various irrigation strategies will continue to increase, as rising temperatures and reduced rainfall could compromise the viability of Mediterranean viticulture. Although the application of irrigation could mitigate the effects of water stress on the vine, it could also have other undesirable effects that, to date, have not been deeply studied. In 2021, a field trial was implemented in an experimental vineyard located at Finca El Socorro (IMIDRA, Madrid) in order to analyse the effect of three irrigation strategies (crop coefficients Kc = 0.2, 0.4 and 0.8) on different agri-environmental variables. Soil management in the three treatments was the same: tillage in vineyard inter-rows and a combination of tillage and glyphosate in vineyard rows. In order to assess the impact of irrigation on weed communities, a campaign of vegetation surveys was initiated in 2023 at two different times (spring, summer), measuring the percentage cover of each species both in the vineyard inter-row and vineyard row. Preliminary results indicate that some difficult to control weeds, such as Cirsium arvense, may be favoured at higher irrigation rates (Kc = 0.8), but the effect on the functional identity of the traits analysed is weak and inconsistent., [ES] El uso del riego en los viñedos mediterráneos ha experimentado un aumento significativo en los últimos años. Así, en el contexto actual de cambio climático se prevé que el empleo de diversas estrategias de riego siga aumentando, ya que el aumento de las temperaturas y la reducción de las precipitaciones podrían comprometer la viabilidad de la viticultura mediterránea. Aunque la aplicación de riego puede paliar los efectos del estrés hídrico sobre la viña, también podría tener otros efectos no deseados que, hasta la fecha, no han sido estudiados en profundidad. En 2021, se implantó un ensayo en un viñedo experimental ubicado en la Finca El Socorro (IMIDRA, Madrid) con el fin de analizar el efecto de tres estrategias de riego (coeficientes de cultivo Kc = 0.2, 0.4 y 0.8) sobre diferentes variables agroambientales. El manejo del suelo en los tres tratamientos fue el mismo: laboreo en las calles del viñedo y una combinación de laboreo con intercepa y glifosato en las líneas. Con el fin de evaluar el impacto del riego sobre las comunidades arvenses, en 2023 se inició una campaña de muestreos de vegetación en dos momentos diferentes (primavera, verano), registrando el porcentaje de cobertura de cada especie tanto en la calle como en la línea del viñedo. Los resultados preliminares indican que algunas arvenses de difícil control como Cirsium arvense, podrían verse favorecidas a mayores dosis de riego (Kc = 0.8), pero el efecto sobre la identidad funcional de los rasgos analizados es débil e inconsistente.

[ES] La pérdida generalizada de biodiversidad en los paisajes y ecosistemas agrarios es una preocupación a nivel global. Esta pérdida se relaciona con la intensificación de las prácticas agrícolas, el uso inadecuado de agroquímicos y la fragmentación de hábitats naturales. Todo esto afecta la diversidad vegetal y tiene un impacto en el funcionamiento del ecosistema, limitando los servicios ecosistémicos. Promover una mayor diversidad funcional en los márgenes de nuestros cultivos es una meta alcanzable mediante el manejo de los recursos que nos ofrece la flora arvense. En particular, las especies de la familia Brassicaceae Burnett (en adelante, crucíferas), desempeñan un papel fundamental en los agrosistemas al atraer enemigos naturales de plagas y proporcionar néctar y polen a los polinizadores. El objetivo de este estudio era detectar y estimar los recursos florales ofrecidos por las crucíferas en presencia de otras especies arvenses. Para ello, se diseñó un experimento en la finca “La Poveda” (CSIC, Madrid) en 2023, en el que se llevaron a cabo hasta 4 vuelos con un dron sobre los márgenes florales donde se encontraban tres crucíferas de interés: Diplotaxis tenuifolia, Eruca vesicaria y Moricandia arvensis. Estas especies representan algunos de los rasgos funcionales clave de las crucíferas que suelen estar presentes en nuestros agrosistemas. La detección de estas especies se realizó mediante el análisis de imágenes capturadas por el dron y la implementación de arquitecturas de redes neuronales convolucionales. En este trabajo, presentamos los primeros resultados de los modelos que estamos desarrollando., [EN] The widespread loss of biodiversity in agricultural landscapes and ecosystems is a global concern. This loss is related to the intensification of agricultural practices, the inappropriate use of agrochemicals and the fragmentation of natural habitats. All of this affects plant diversity and has an impact on the functioning of the ecosystem, limiting ecosystem services. Promoting greater functional diversity in the margins of our crops is an achievable goal through the management of the resources offered by the weed flora. In particular, species of the Brassicaceae Burnett family (hereinafter referred to as cruciferous), play a fundamental role in agrosystems by attracting natural enemies of pests and providing nectar and pollen to pollinators. The objective of this study was to detect and estimate the floral resources offered by cruciferous plants in the presence of other weed species. To this end, an experiment was designed at the “La Poveda” farm (CSIC, Madrid) in 2023, in which up to 4 flights were carried out with a drone over the floral margins where three cruciferous plants of interest were found: Diplotaxis tenuifolia, Eruca vesicaria and Moricandia arvensis. These species represent some of the key functional traits of crucifers that are often present in our agrosystems. The detection of these species was carried out through the analysis of images captured by the drone and the implementation of convolutional neural network architectures. In this work, we present the first results of the models we are developing.

Accurate weed species identification is crucial for effective site-specific weed management (SSWM), enabling targeted and timely control measures for each weed in crop field. This study advanced the current approach to species-level weed identification during the early growth stage by integrating unmanned aerial vehicles (UAVs) imagery with standard convolutional neural networks (CNNs) models such as VGG16, Resnet152 and Inception-Resnet-v2. For this, a robust dataset was created with 33,467 labels of weeds (Atriplex patula, Chenopodium album, Convolvulus arvensis, Cyperus rotundus, Lolium rigidum, Portulaca oleracea, Salsola kali, Solanum nigrum) and crops (maize, tomato), which was subjected to different training, validation and test scenarios. Model inputs were adjusted in order to align them with the information represented by the UAV images. Initially, models were developed in balanced scenarios, gradually increasing label numbers to assess their performance. Inception-ResNet-v2 achieved over 90% accuracy with 400 labels, while ResNet152 and VGG16 required 600 and 800 labels, respectively, for similar accuracy. In a more complex and realistic scenarios with unbalanced datasets, Inception-ResNet-v2 outperformed, likely due to its deeper architecture and enhanced capability to capture intricate features and patterns within UAV images. The study emphasized the importance of the minority-to-majority species ratio in unbalanced datasets, which affects minority species classification. To prevent misclassification, it is crucial to determine the right number of labels for CNN model training and validation. Weed maps were generated after species classification using the Faster R–CNN algorithm as an object detector. This advancement in methodology facilitates the precise and efficient implementation of SSWM techniques.

[EN] Sorghum halepense (Johnsongrass) is a weed that tends to spatially distribute in an aggregated manner in maize crops. In a two-year study conducted at La Poveda (CSIC, Madrid), the effectiveness of Precision Agriculture (PA) in controlling this weed was investigated by: 1) monitoring the spatio-temporal dynamics of Johnsongrass patches in a maize field using UAV aerial image processing; 2) site-specific application of the herbicide nicosulfuron according to the early-stage map generated from aerial imagery; and 3) quantification of crop yield in Johnsongrass treated and uuntreated areas, as well as weed-free. The analysis of the map prior to herbicide treatment in 2022 revealed a random distribution of Johnsongrass, with patches averaging 0.4 m² in size, ranging from 0.01 m² to 5 m². Site-specifically treated plots showed spatial growth control, evidenced in the subsequent season (2023) with an average patch size of 1.0 m2, ranging from 0.02 m² to 12 m². In contrast, untreated patches significantly increased in size (average 3.8 m2; ranging between 0.01 m² and 122 m²). There was a notable decrease in yield in untreated infested areas compared to treated and/or Johnsongrass-free areas. These findings underscore the advantages of PA in Johnsongrass control, demonstrating how detection and site-specific patch management can reduce herbicide use by up to 95% and mitigate competition with the maize crop., [ES] Sorghum halepense (cañota) es una mala hierba que tiende a distribuirse espacialmente de forma agregada en los cultivos de maíz. En un estudio de dos años realizado en La Poveda (CSIC, Madrid), se investigó la eficacia de la Agricultura de Precisión (AP) en su control, mediante: 1) el seguimiento de la dinámica espacio-temporal de los rodales de cañota en un campo de maíz con el procesamiento de imágenes aéreas UAV; 2) la aplicación localizada del herbicida nicosulfuron según el mapa generado en etapa temprana con imágenes aéreas; y 3) la cuantificación del rendimiento del cultivo en áreas tratadas y no tratadas con cañota, así como libres de mala hierba. El análisis del mapa previo al tratamiento herbicida en 2022 reveló una distribución aleatoria de cañota, con rodales de tamaño medio de 0,4 m2, variando entre 0,01 m² y 5 m². Las parcelas tratadas con herbicida localizado mostraron un control del crecimiento espacial, evidenciado en la campaña siguiente (2023) con un tamaño promedio de 1,0 m2, oscilando entre 0,02 m² y 12 m². En contraste, los rodales no tratados aumentaron significativamente su tamaño (media 3,8 m2; rango entre 0,01 m² y 122 m²). El rendimiento disminuyó notablemente en zonas infestadas sin tratamiento respecto a las zonas tratadas y/o libres de cañota. Estos resultados subrayan las ventajas de la AP en el control de la cañota, demostrando cómo la detección y manejo localizado de rodales podrían reducir hasta un 95% el uso de herbicidas y mitigar la competencia con el cultivo de maíz.

[EN] Efficient invasive weed management is critical to maintain the balance between biodiversity, production and agricultural sustainability. Invasive plants may cause significant yield losses in crops and their control entails a high cost in pesticide products, in addition to the ecological damage they generate. Advances in robotics, digital agriculture, artificial intelligence, etc, allow the inputs’ optimization (e.g. pesticides) through timely and site-specific treatments tailored to the real crop needs, aligned with the principles of precision agriculture. Amaranthus palmeri has been recently detected in Spanish crop fields. It is an extremely competitive species, with a high fertility rate and skilled at developing herbicide-resistant biotypes, which poses a serious threat to Spanish agriculture. Therefore, a digital agriculture tool -called DIGINVASIVE- is being designed for mapping and monitoring of A. palmeri in crops, which consists of an open-source web-based interface system using innovative digital technologies such as drone imagery, Geographic Information Systems, Information and Communication Technologies and powerful Artificial Intelligence algorithms. This interactive tool will allow users to establish suitable prevention and eradication measures to address the control of A. palmeri in agricultural environments., [ES] La gestión eficiente de las malas hierbas invasoras es fundamental para mantener el equilibrio entre biodiversidad, producción y sostenibilidad agrícola. Esto se debe a que las malas hierbas invasoras pueden causar pérdidas significativas de rendimiento en los cultivos y su control supone un elevado coste en productos fitosanitarios, además de los daños ecológicos que generan. Los avances en robótica, digitalización, inteligencia artificial, etc., permiten la optimización de los recursos empleados (e.g. fitosanitarios) mediante la aplicación de tratamientos localizados y ajustados a las necesidades reales del cultivo siguiendo los principios de la agricultura de precisión. Recientemente se ha confirmado la presencia de la planta invasora Amaranthus palmeri en parcelas agrícolas en España. Esta especie es altamente competitiva, presenta una elevada tasa de fecundidad y la capacidad para desarrollar biotipos resistentes a los herbicidas, convirtiéndola en una de las especies exóticas con mayor amenaza para la agricultura española. Para ello, se ha desarrollado una herramienta de agricultura digital -denominada DIGINVASIVE- para el mapeo y seguimiento de A. palmeri en campos agrícolas, consistentes en un servidor de cartografía digital web de código abierto que utiliza tecnologías digitales innovadoras como imágenes de dron, Sistemas de Información Geográfica, Tecnologías de la Información y la Comunicación y potentes algoritmos de Inteligencia Artificial. Esta herramienta interactiva permitirá a los usuarios establecer las medidas de prevención y erradicación necesarias para abordar el control de A. palmeri en escenarios agrícolas.

Water potential explains water transport in the Soil-Plant-Atmosphere Continuum (SPAC), and is gaining interest as connecting variable between ‘pedo-, bio- and atmosphere’. It is primarily used to simulate hydraulics in the SPAC, and is thus essential for studying drought effects. Recent implementations of hydraulics in large-scale Terrestrial Biosphere Models (TBMs) improved their performance under water-limited conditions, while hydraulic features of recent detailed FunctionalStructural Plant Models (FSPMs) open new possibilities for dissecting complex traits for drought tolerance. These developments in models across scales deserve a critical appraisal to evaluate its potential for wider use in FSPMs, but also in crop systems models (CSMs), where hydraulics are currently still absent. After refreshing the physical basis, we first address models where water potential is primarily used for describing water transport along the transpiration pathway from the soil to the leaves, through the roots, the xylem and the leaf mesophyll. Then, we highlight models for three ecophysiological processes, which have well-recognised links to water potential: phloem transport, stomatal conductance and organ growth. We identify water potential as the bridge between soil, root and shoot models, as the physiological variable integrating below- and aboveground abiotic drivers, but also as the link between water status and growth. Models making these connections enable identifying crucial traits for ecosystem resilience to drought and for breeding towards improved drought tolerance in crops. Including hydraulics often increases model complexity, and thus requires experimental data on soil and plant hydraulics. Nevertheless, modelling hydraulics is insightful at different scales (FSPMs, CSMs and TBMs).

Precision viticulture has arisen in recent years as a new approach in grape production. It is based on assessing field spatial variability and implementing site-specific management strategies, which can require georeferenced information of the three dimensional (3D) grapevine canopy structure as one of the input data. The 3D structure of vineyard fields can be generated applying photogrammetric techniques to aerial images collected with Unmanned Aerial Vehicles (UAVs), although processing the large amount of crop data embedded in 3D models is currently a bottleneck of this technology. To solve this limitation, a novel and robust object-based image analysis (OBIA) procedure based on Digital Surface Model (DSM) was developed for 3D grapevine characterization. The significance of this work relies on the developed OBIA algorithm which is fully automatic and self-adaptive to different crop-field conditions, classifying grapevines, and row gap (missing vine plants), and computing vine dimensions without any user intervention. The results obtained in three testing fields on two different dates showed high accuracy in the classification of grapevine area and row gaps, as well as minor errors in the estimates of grapevine height. In addition, this algorithm computed the position, projected area, and volume of every grapevine in the field, which increases the potential of this UAV- and OBIA-based technology as a tool for site-specific crop management applications.

Addressing the spatial and temporal variability of crops for agricultural management requires intensive and periodical information gathering from the crop fields. Unmanned Aerial Vehicle (UAV) photogrammetry is a quick and affordable method for information collecting; it provides spectral and spatial information when required with the added value of Digital Surface Models (DSMs) that reconstruct the crop structure in 3D using “structure from motion” techniques. In the full process from UAV flights to image analysis, DSM generation is one bottle-neck due to its high processing time. Despite its importance, the optimization of the required forward overlap for saving time in DSM generation has not yet been studied. UAV images were acquired at 50 and 100 m flight altitudes over two olive orchards with the aim of generating DSMs representing the tree crowns. Several DSMs created with different forward laps (in intervals of 5–6% from 58 to 97%) were evaluated in order to determine the optimal generation time according to the accuracy of tree crown measurements computed from each DSM. Based on our results, flying at 100 m altitude and with a 95% forward lap reported the best configuration. From the analysis derived from this configuration, tree volume was estimated with 95% accuracy. In addition, computing time was 85% lower in comparison to the maximum overlap studied (97%). It allowed computing the 3D features of 600 trees in a 3-ha parcel in a highly accurate and quick (a few hours after the UAV flights) manner by using a standard computer.

Unmanned aerial vehicles (UAVs) combined with different spectral range sensors are an emerging technology for providing early weed maps for optimizing herbicide applications. Considering that weeds at very early phenological stages are similar spectrally and in appearance, three major components are relevant: spatial resolution, type of sensor and classification algorithm. Resampling is a technique to create a new version of an image with a different width and/or height in pixels, and it has been used in satellite imagery with different spatial and temporal resolutions. In this paper, the efficiency of resampled-images (RS-images) created from real UAV-images (UAV-images; the UAVs were equipped with two types of sensors, i.e., visible and visible plus near-infrared spectra) captured at different altitudes is examined to test the quality of the RS-image output. The performance of the object-based-image-analysis (OBIA) implemented for the early weed mapping using different weed thresholds was also evaluated. Our results showed that resampling accurately extracted the spectral values from high spatial resolution UAV-images at an altitude of 30 m and the RS-image data at altitudes of 60 and 100 m, was able to provide accurate weed cover and herbicide application maps compared with UAV-images from real flights.

[ES] 5HVXPHQLos vehículos aéreos no tripulados (UAVs) son una tecnología emergente en el estudio de parámetros agrícolas por sus características y por portar sensores en diferente rango espectral. En este trabajo se ha detectado y cartografiado rodales de malas hierbas en fase temprana mediante análisis OBIA para elaborar mapas que optimicen el tratamiento herbicida localizado. Se ha aplicado resampling (resampleo) sobre imágenes tomadas en campo desde un UAV (UAV-I) para crear una nueva imagen con distinta resolución espacial. A las imágenes resampleadas (RS-I) se les evaluó la calidad espacial y espectral y la eficacia de nuestro análisis en la detección de malas hierbas. Los resultados de las imágenes RS-I muestran una precisión similar a las imágenes UAV-I siendo factible su utilización en tecnologías de manejo localizado de malas hierbas. Se discuten las ventajas del uso de la técnica de resampling en imágenes UAV., [EN] Unmanned aerial vehicles (UAV) are an emerging technology for the study of agriculture parameters due to its cha- racteristics and the availability of embedding sensors with different spectral range. In our study, the detection and mapping of weeds in early phenological stage allowed to design a strategy for the optimizing of herbicide treatment. In this work, resampling is used to create a new version of an image with a different spatial resolution, using real UAV imagery. A spatial and spectral quality evaluation was carried out to resampled images (RS-I), and the, our workflow for weed detection applied. The results showed that RS-I and UAV-I showed similar accuracy on weed detection and thus could be used for site-specific weed management achieving a percentage of savings in the herbicide. Opportunities of using RS-I are discussed.

This article describes the technical specifications and configuration of a multirotor unmanned aerial vehicle (UAV) to acquire remote images using a six-band multispectral sensor. Several flight missions were programmed as follows: three flight altitudes (60, 80 and 100 m), two flight modes (stop and cruising modes) and two ground control point (GCP) settings were considered to analyze the influence of these parameters on the spatial resolution and spectral discrimination of multispectral orthomosaicked images obtained using Pix4Dmapper. Moreover, it is also necessary to consider the area to be covered or the flight duration according to any flight mission programmed. The effect of the combination of all these parameters on the spatial resolution and spectral discrimination of the orthomosaicks is presented. Spectral discrimination has been evaluated for a specific agronomical purpose: to use the UAV remote images for the detection of bare soil and vegetation (crop and weeds) for in-season site-specific weed management. These results show that a balance between spatial resolution and spectral discrimination is needed to optimize the mission planning and image processing to achieve every agronomic objective. In this way, users do not have to sacrifice flying at low altitudes to cover the whole area of interest completely.