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The dataset, titled 'Unmanned Aerial Vehicle Imagery for Early Stage Weed Classification and Detection in Maize and Tomato Crops,' comprises RGB images captured from an unmanned aerial vehicle flying at an altitude of 11 meters. These images were taken during the early growing season of two summer crops: Maize (BBCH14) at the CSIC experimental farm La Poveda in Arganda del Rey, Madrid, Spain, and Tomato (BBCH501) in commercial plots located in Santa Amalia, Badajoz, Spain. The dataset includes 33,467 labels representing weeds (Atriplex patula, Chenopodium album, Convolvulus arvensis, Cyperus rotundus, Lolium rigidum, Portulaca oleracea, Salsola kali, Solanum nigrum) and crops (maize, tomato). All images are saved in *.jpg format and have been labeled using partitions extracted from an orthomosaic. The final result consists of a collection of individual images, each corresponding to a specific label, i.e., to each plant of each identified species.

[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.

[EN] Drones are a valuable tool to explore the spatial variability present in crop fields. Although its use is becoming relatively common in technological oriented systems it is very rare in ecological oriented systems. The goal of this presentation is to explore some of the potentialities of this tool to contribute to an improved agroecological weed management. The information obtained in early flights can be used to minimize input use (tillage operations or herbicides). The information obtained at the end of the crop life cycle can be used to assess the efficacy of the control measures, monitoring weed escapes and/or resistant populations. Digital phenotyping from aerial images could be used in breeding weed suppressing varieties. The assessment of agronomic practices under real conditions can be conducted through aerial monitoring farmer`s plots with variable conditions. The large volume of information (big data) provided on soil, crop and weed conditions will facilitate the design and implementation of integrated weed management systems. The large volume of information (big data) provided on soil, crop and weed conditions will facilitate the design and implementation of integrated weed management systems. The use of drones at the landscape scale allow to monitor biodiversity spaces and to assess the ecosystem services provided by these areas., [ES] Los drones constituyen una herramienta muy valiosa para explorar la variabilidad espacial presente en las parcelas de cultivo. Sin embargo, su uso, cada vez más frecuente en sistemas con una orientación tecnológica, es prácticamente desconocida su aplicación dentro de sistemas para una aproximación agro-ecológica. El objetivo de este trabajo es explorar algunas de las potencialidades de los drones para contribuir a un mejor manejo agroecológico de las malas hierbas. La información extraída con vuelos tempranos nos permite minimizar el uso de insumos (labores, herbicidas) y mejorar su eficiencia. Por otra parte, la información obtenida en vuelos realizados al final del ciclo nos permite evaluar la eficacia de las medidas tomadas, detectando la aparición de escapes o de poblaciones resistentes. El fenotipado digital a partir de imágenes de dron puede ser de gran utilidad en los programas de mejora de variedades de cultivos supresoras de malas hierbas. La evaluación de prácticas agronómicas en condiciones reales puede llevarse a cabo mediante la monitorización continuada de parcelas de agricultores en las que exista una diversidad de condiciones. La abundante información suministrada por los drones sobre el estado del suelo, cultivo y poblaciones de plagas y malas hierbas facilitaría el diseño y la implementación de sistemas de gestión integrada. El empleo de drones a escala paisaje nos permite el seguimiento de los espacios de biodiversidad y evaluar los servicios ecosistémicos obtenidos.

[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.

In an effort to reduce pesticide use, agronomists and computer scientists have joined forces to develop site-specific weed detection and classification systems. These systems aim to recognize and locate weed species within a crop field, using precision equipment to apply required herbicides timely and only where needed, with the objective of reducing the sprayable surface required to eliminate the given weed and protect the crop, with both economic and environmental benefits. Yet, with climate change on the rise, common weeds are expected to undergo some changes to adapt to their environment, possibly with new or invasive weeds spreading to areas where they did not exist before. These changes (often morphological) as well as new invasions need to be taken into account by future classifiers and detection algorithms to ensure system robustness and adaptation to new habitats/climate dynamics. This paper proposes a set of experiments evaluating the use of transfer learning and zero-shot learning for weed classification using our novel TomatoWeeds dataset. Residual networks of variable depth, pretrained on the Imagenet and/or DeepWeeds datasets were evaluated. A ResNet50 pretrained on both datasets and fine-tuned on the TomatoWeeds dataset performed best, returning a holdout set accuracy of 77.8%, showing the advantageous use of transfer learning in this domain. Zero-shot learning, using both embeddings of images and morphological and habitat text-based descriptions, is implemented to test the ability of machine learning pipelines of recognizing unseen classes at test time (which may arise e.g. due to changing climate dynamics), a learning task in which the field (and our experiments) are still far from satisfactory results. Further research could benefit from larger weed-specific datasets for transfer learning as well as deeper network architectures to improve model performance. The projection-based ZSL could also benefit from larger datasets and new zero-shot learning

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.

Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks.

The measurement of geometric canopy parameters in woody crops is an important task in Precision Agriculture because of their correlation with crop condition and productivity. In recent years, several technological approaches have been developed as an alternative to manual measurements, which are time- and labour-consuming. Two of the most commonly used 3D canopy characterization technologies are mobile terrestrial laser scanning (MTLS) based on light detection and ranging (LiDAR) sensors, and digital aerial photogrammetry (DAP) using imagery from uncrewed aerial vehicles (UAVs). Although both are state-of-the-art and have been fully tested and validated, a complete comparison between their geometric canopy parameter estimations in different woody crops and training systems has not been carried out. For this reason, a set of geometric parameters (canopy height, projected area, and volume) of a vineyard, an intensive peach orchard, and an intensive pear orchard were measured using UAV-DAP and MTLS-LiDAR. A comparison between both kinds of measurements was performed, accounting for the length of the sections in which the crop hedgerows were divided to extract the geometric parameters. Measurements from the UAV and the MTLS were highly correlated (R2 from 0.82 to 0.94) when considering the data from the three crops together, and the correlations were higher when analysing longer row sections. The canopy geometric parameters estimated using the MTLS-LiDAR always had higher values than those from the UAV-DAP. The results presented in this work provide useful data for a more informed selection of technological approaches for 3D crop characterization in Precision Fruticulture and high-throughput phenotyping.

Invasive plant species are a major cause of crop loss and have high potential to disturb the existing ecological systems [1]. Amarantus palmeri has been recently detected in Mediterranean basin countries, triggering highest alerts of the European Plant Protection Organization [2] and national plant health services, as it is an extremely competitive species native to North America with a high fertility rate and adept at developing herbicide-resistant biotypes [3]. In Spain, A. palmeri has been included among the exotic species that pose a serious threat to productive sectors, pointing to the need for containment efforts and early detection of new cases [4]. In recent years, agricultural web-based tools have made the process of crop monitoring and surveillance easier and more accessible. They are online digital platforms that combine Geographic Information Systems (GPS) technology and big data collected by remote sensing platform or meteorological station, among others, analyze them, and provide graphical and text information in the form of maps and databases. However, they have not been developed for invasive weeds in agricultural domain. Unmanned Aerial Vehicles (UAV) and Artificial Intelligence (AI), as object-based image analysis OBIA and machine learning (ML) procedures, are required to detect and map weeds in challenging scenarios, such as in early season herbaceous crops due to the high spectral similarity of different plant species and the small size of plants in such early stages of development [5,6]. To this end, an open source web-based interface system, called DIGINVASIVE, is being designed to assist end-users by providing an open web mapping and GIS platform to upload, analyze and visualize geo-spatial data that will be able to map the presence of invasive weed plants.

Agriculture has used technology as a tool to reinvent itself over time. Cognitive computing is used in numerous artificial intelligence applications, including expert systems, robotics, virtual reality, and machine learning. Supervised deep learning models require a large amount of labeled data for training, representing a common challenge that affects current studies of weed detection and classification using images acquired from a drone. The data augmentation technique provides a practical solution to alleviate insufficient datasets. In this study, we investigate the capability of a cognitive computational system applied to a real-world scenario for data generation, we use the pre-trained Generative Face Priority (GFP) model, which is employed in blind face restoration by means of spatial feature transformation layers, and this model is based on Generative Adversarial Neural Networks (GAN). Data augmentation was applied for weed species found in the early stages of maize and tomato crops; monocots (Cyperus rotundus, Lolium) and dicots (Atriplex patula, Convolvulus arvensis, Salsola kali and Solanum nigrum). The RGB images were acquired from a drone at an altitude of 12 m. The real and augmented datasets were systematically evaluated with those generated by GFP-GAN, for different scaling factors in the images that constitute the input to the classifiers based on the convolutional neural networks VGG16 and ResNet152. Our results show that the technique used improves the generalization capability of the evaluated classifiers, so the size of the dataset directly influences the performance of the models, noticing an approximate increase of more than 5% in the model accuracy metric; furthermore, the GFP-GAN architecture restores with realistic and faithful details by increasing the spatial resolution of the image. The proposed approach demonstrates the scope of the cognitive system built in a domain different from the target domain.

Characterizing crop canopies is especially important in the management of woody crops. In this article, two systems were compared to characterise a 50 m long vineyard row section. One of the systems was a mobile terrestrial laser scanner based on a light detection and ranging (LiDAR) sensor (MTLS-LiDAR). The other was an uncrewed aerial vehicle (UAV) based system using digital aerial photogrammetry (UAV-DAP). The resulting 3D point clouds were assessed qualitatively and quantitatively. Canopy heights, widths and volumes were obtained in 0.1 m long sections along the studied row. All the parameters derived from the two systems presented statistically significant differences. The coefficients of determination between systems were 0.619 for canopy maximum heights above ground level (agl), 0.686 for 90th percentile (P90) heights agl, and 0.283 and 0.274 for maximum and P90 vegetated heights, respectively. Coefficients of determination between averaged maximum canopy width and P90 canopy width were 0.328 and 0.317, respectively. Coefficients of determination between cross-sectional areas determined from maximum widths, P90 widths and from the occupancy grid method were 0.423, 0.409 and 0.334, respectively. Total canopy volume for the entire row obtained from the three cross section estimation methods differed between 19 m3 and 25 m3. The reasons found were that the MTLS-LiDAR-derived point cloud captured the canopy top and side variability but could be affected by occlusions, mixed pixels and tall grass-like weeds present in the surveyed area. For its part, the UAV-DAP-derived point cloud tended to miss top and side shoots and somewhat smoothed canopy variability. As neither of the systems is optimal, a balance needs to be found according to the specific requirements of the survey. For this purpose, a list of pros and cons is presented to support the selection of one of the two systems for canopy monitoring. The MTLS-LiDAR system should be chosen when high detail is requ

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 res

This chapter reviews the major conceptual approaches and specifications for the design of site-specific weed management decision support systems (SSWM-DSS), recent advances in the use of remote and ground platforms and sensors for information gathering and processing, and initial experiences translating this information into chemical and physical weed control actuations through decision algorithms and models.

[ES] Se proporcionan métodos de mapeo de los surcos de laboreo en uno o más campos agrícolas a partir de imágenes aéreas que incluyen una imagen aérea de al menos una parte de uno o más campos agrícolas, la imagen aérea incluye objetos segmentados como resultado de un proceso de segmentación de objetos, dicha imagen aérea siendo una imagen dividida o no dividida, en el caso de ser una imagen dividida incluyendo estas porciones de imagen como resultado de un proceso de división de la imagen. Estos métodos de mapeo comprenden la identificación de objetos alargados correspondientes a aquellos objetos segmentados que tienen una relación Longitud/Ancho (L/A) por encima de un umbral L/A predefinido. Dichos métodos de mapeo comprenden además determinar segmentos de surcos de laboreo basados en los objetos alargados identificados y generar un mapa de surcos de laboreo basado en los segmentos de surcos de laboreo determinados. También se proporcionan programas de ordenador y sistemas que son adecuados para realizar dichos métodos de mapeo., [EN] Methods of mapping tillage furrows in one or more agricultural fields are provided from aerial images that include an aerial image of at least a portion of one or more agricultural fields, the aerial image includes segmented objects as a result of a process of object segmentation, said aerial image being a split or non-split image, in the case of being a split image including these image portions as a result of an image splitting process. These mapping methods comprise the identification of elongated objects corresponding to those segmented objects that have a Length/Width (L/W) ratio above a predefined L/W threshold. Said mapping methods further comprise determining tillage row segments based on the identified elongated objects and generating a tillage map based on the determined tillage row segments. Computer programs and systems that are suitable for performing such mapping methods are also provided.

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.

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 res

This paper reviewed a set of twenty-one original and innovative papers included in a special issue on UAVs for vegetation monitoring, which proposed new methods and techniques applied to diverse agricultural and forestry scenarios. Three general categories were considered: (1) sensors and vegetation indices used, (2) technological goals pursued, and (3) agroforestry applications. Some investigations focused on issues related to UAV flight operations, spatial resolution requirements, and computation and data analytics, while others studied the ability of UAVs for characterizing relevant vegetation features (mainly canopy cover and crop height) or for detecting different plant/crop stressors, such as nutrient content/deficiencies, water needs, weeds, and diseases. The general goal was proposing UAV-based technological solutions for a better use of agricultural and forestry resources and more efficient production with relevant economic and environmental benefits.

This chapter reviews the major conceptual approaches and specifications for the design of Site-Specific Weed Management Decision Support Systems (SSWM-DSS), recent advances in the use of remote and ground platforms and sensors for information gathering and processing and initial experiences translating this information into chemical and physical weed control actuations through decision algorithms and models

The establishment and management of cover crops are common practices widely used in irrigated viticulture around the world, as they bring great benefits not only to protect and improve the soil, but also to control vine vigor and improve the yield quality, among others. However, these benefits are often reduced when cover crops are infested by Cynodon dactylon (bermudagrass), which impacts crop production due to its competition for water and nutrients and causes important economic losses for the winegrowers. Therefore, the discrimination of Cynodon dactylon in cover crops would enable site-specific control to be applied and thus drastically mitigate damage to the vineyard. In this context, this research proposes a novel, automatic and robust image analysis algorithm for the quick and accurate mapping of Cynodon dactylon growing in vineyard cover crops. The algorithm was developed using aerial images taken with an Unmanned Aerial Vehicle (UAV) and combined decision tree (DT) and object-based image analysis (OBIA) approaches. The relevance of this work consisted in dealing with the constraint caused by the spectral similarity of these complex scenarios formed by vines, cover crops, Cynodon dactylon, and bare soil. The incorporation of height information from the Digital Surface Model and several features selected by machine learning tools in the DT-OBIA algorithm solved this spectral similarity limitation and allowed the precise design of Cynodon dactylon maps. Another contribution of this work is the short time needed to apply the full process from UAV flights to image analysis, which can enable useful maps to be created on demand (within two days of the farmer´s request) and is thus timely for controlling Cynodon dactylon in the herbicide application window. Therefore, this combination of UAV imagery and a DT-OBIA algorithm would allow winegrowers to apply site-specific control of Cynodon dactylon and maintain cover crop-based management systems and their consequent

The perennial and stoloniferous weed, Cynodon dactylon (L.) Pers. (bermudagrass), is a serious problem in vineyards. The spectral similarity between bermudagrass and grapevines makes discrimination of the two species, based solely on spectral information from multi-band imaging sensor, unfeasible. However, that challenge can be overcome by use of object-based image analysis (OBIA) and ultra-high spatial resolution Unmanned Aerial Vehicle (UAV) images. This research aimed to automatically, accurately, and rapidly map bermudagrass and design maps for its management. Aerial images of two vineyards were captured using two multispectral cameras (RGB and RGNIR) attached to a UAV. First, spectral analysis was performed to select the optimum vegetation index (VI) for bermudagrass discrimination from bare soil. Then, the VI-based OBIA algorithm developed for each camera automatically mapped the grapevines, bermudagrass, and bare soil (accuracies greater than 97.7%). Finally, site-specific management maps were generated. Combining UAV imagery and a robust OBIA algorithm allowed the automatic mapping of bermudagrass. Analysis of the classified area made it possible to quantify grapevine growth and revealed expansion of bermudagrass infested areas. The generated bermudagrass maps could help farmers improve weed control through a well-programmed strategy. Therefore, the developed OBIA algorithm offers valuable geo-spatial information for designing site-specific bermudagrass management strategies leading farmers to potentially reduce herbicide use as well as optimize fuel, field operating time, and costs.

As demand for freshwater increases while supply remains stagnant, the critical need for sustainable water use in agriculture has led the EPA Strategic Plan to call for new technologies that can optimize water allocation in real-time. This work assesses the use of cloud-based artificial intelligence to detect early indicators of water stress across six container-grown ornamental shrub species. Near-infrared images were previously collected with modified Canon and MAPIR Survey II cameras deployed via a small unmanned aircraft system (sUAS) at an altitude of 30 meters. Cropped images of plants in no, low-, and high-water stress conditions were split into four-fold cross-validation sets and used to train models through IBM Watson’s Visual Recognition service. Despite constraints such as small sample size (36 plants, 150 images) and low image resolution (150 pixels by 150 pixels per plant), Watson generated models were able to detect indicators of stress after 48 hours of water deprivation with a significant to marginally significant degree of separation in four out of five species tested (p < 0.10). Two models were also able to detect indicators of water stress after only 24 hours, with models trained on images of as few as eight water-stressed Buddleia plants achieving an average area under the curve (AUC) of 0.9884 across four folds. Ease of pre-processing, minimal amount of training data required, and outsourced computation make cloud-based artificial intelligence services such as IBM Watson Visual Recognition an attractive tool for agriculture analytics. Cloud-based artificial intelligence can be combined with technologies such as sUAS and spectral imaging to help crop producers identify deficient irrigation strategies and intervene before crop value is diminished. When brought to scale, frameworks such as these can drive responsive irrigation systems that monitor crop status in real-time and maximize sustainable water use.

This paper aims to evaluate the spatial persistence of wild oat patches in four wheat fields over time to determine the economic feasibility of using late-season wild oat maps for early site-specific weed management (SSWM) next season. The spatial persistence of wild oat patches was analyzed by three tests: land use change detection between years, spatial autocorrelation, and analysis of spreading distance. The temporal trend of wild oat patch distribution showed a clear persistence and a generalized increase in the infested area, with a noticeable level of weed aggregation and a tendency in the new weed patches to emerge close to older ones. To economically evaluate the SSWM, five simulations in four agronomic scenarios, varying wheat yields and losses due to wild oat, were conducted. When yield losses due to wild oat were minimal and for any of the expected wheat yields, some SSWM simulations were more economically profitable than the overall application in most of the fields. Nevertheless, when the yield losses due to wild oat were maximal, all SSWM simulations were less profitable than overall treatment in all the analyzed fields. Although the economic profit variations achieved with SSWM treatments were modest, any of the site-specific treatments tested are preferred to herbicide broadcast over the entire field, in order to reduce herbicide and environmental pollution.

Remote sensing technology allows monitoring the progress of vegetation and crop phenology in large regions. Seasonal vegetation trends are commonly estimated from high temporal resolution but coarse spatial resolution satellite imagery, e.g., from MODIS-NDVI (Moderate Resolution Imaging Spectroradiometer—Normalized Difference Vegetation Index) time-series, which has usually limited their application to scenarios with few land uses or crops covering areas larger than actual parcel sizes. As an alternative, this paper proposes a general and robust procedure to map crop phenology at the level of individual crop parcels, and validates its feasibility in a complex and diverse cropland area located in central California. A first calibration phase consisted of evaluating the three curve-fitting models implemented in the TIMESAT software (i.e., asymmetric Gaussian (AG), double logistic (DL), and adaptive Savitzky–Golay (SG) filtering) and reporting the model and its settings that best adjusted to the MODIS-NDVI profile of each crop studied. Next, based on the selected crop-specific models and with a crop map previously obtained from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) multi-temporal images, the procedure mapped four crop calendar events (i.e., start, end, middle, and length of the season) and five phenology-related metrics (i.e., base, maximum, amplitude, derivatives, and integrals of the NDVI values) of the study region by object-based image analysis (OBIA) of the MODIS-NDVI time-series. To mitigate the impact of mixed pixels, the OBIA procedure was designed to automatically apply a restrictive criterion based on the coverage of MODIS-NDVI pixels in each crop parcel: (1) using only the MODIS-NDVI pixels that were placed 100% within each crop parcel (i.e., “pure” pixels); or (2) if no “pure” pixels exist in any crop parcel, using only pixels with coverage percentages greater than 50%, and in such cases, reporting the mixing percentage in t

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.

Durante los últimos años el desarrollo de nuevas tecnologías para la agricultura de precisión ha alcanzado un considerable grado de madurez. En el ámbito específico de la gestión de las malas hierbas se han desarrollado numerosas tecnologías tanto de detección como de actuación. A los procedimientos disponibles para la detección desde satélite o d,esde avión se han unido otros mucho más precisos para la detección desde drones volando a baja altura. Por otro lado, la disponibilidad actual de sensores y cámaras 2D y 3D de muy alta resolución y de ordenadores más rápidos capaces de procesar grandes volúmenes de datos abre la posibilidad de llevar a cabo actuaciones en tiempo real. En este trabajo se expone una breve síntesis de los últimos avances producidos en sistemas de detección aérea y terrestre así como de algunos de los equipos inteligentes disponibles para el control físico o químico de las malas hierbas detectadas.

In Spain, the use of annual cover crops is a crop management practice for irrigated vineyards that allows controlling vineyard vigor and yield, which also leads to improve the crop quality. Recently, Cynodon dactylon (bermudagrass) has been reported to infest those cover crops and colonize the grapevine rows, resulting in significant yield and economic losses due to the competition for water and nutrients. From timely unmanned aerial vehicle (UAV) imagery, the objective of this research was to map C. dactylon patches in order to provide an optimized site-specific weed management. A quadrocopter UAV equipped with a point-and-shoot camera was used to collect a set of aerial red-green-blue (RGB) images over a commercial vineyard plot, coinciding with the dormant period of C. dactylon (February 2016). Object-based image analysis (OBIA) techniques were used to develop an innovative algorithm for early discrimination and mapping of C. dactylon, which had the ability to solve the limitation of spectral similarity of this weed with cover crops or bare soil. As a general result, the classified maps of the studied vineyard showed four main classes, i.e. vine, cover crop, C. dactylon and bare soil, with 85% overall accuracy. These weed maps allow developing new strategies for site-specific control of C. dactylon populations in the context of precision viticulture.

El tipo y la intensidad de la poda del olivo modifican la copa del árbol con diferente grado de severidad lo que afecta al crecimiento y a la calidad de su fruto. En esta investigación se ha desarrollado un conjunto tecnológico que consiste en la combinación de la adquisición de imágenes procedentes de un vehículo aéreo no tripulado (UAV) y el correspondiente análisis de dichas imágenes basado en objetos (OBIA). El objetivo de este trabajo ha sido la monitorización 3D de cada uno de los árboles de una parcela de olivar en la que se llevaron a cabo tres tratamientos distintos de poda (tradicional, adaptada y mecanizada). Se realizaron tres vuelos UAV sobre la parcela en tres momentos diferentes (antes de la poda, un mes después de la poda y un año después de la poda) y se analizaron 3 variables geométricas: altura, área proyectada y volumen de copa de cada árbol. Los resultados obtenidos muestran que existen diferencias significativas entre las variables geométricas analizadas dependiendo de la poda, siendo los olivos bajo poda tradicional los que registraron mayores crecimientos de altura, área proyectada y volumen. Debido a que actualmente no existen referencias científicas que cuantifiquen los efectos de la poda con tecnología UAV y algoritmos de clasificación OBIA, este trabajo tiene potencialidad para aplicaciones en otros cultivos leñosos.

[ES] En este trabajo se han evaluado variables espectrales, morfológicas (forma, tamaño), contextuales (posición) y texturales que permitan desarrollar un procedimiento automático de clasificación de plántulas de amapola, otras malas hierbas, cultivo y suelo desnudo en imágenes adquiridas con un vehículo aéreo no tripulado (UAV). Las imágenes se tomaron con una cámara modificada para la obtención de información en infrarrojo-color y a una resolución espacial de 0,60 cm/píxel. Se aplicaron técnicas avanzadas de análisis de imágenes basadas en objetos para la obtención de las variables descritas y se desarrolló un modelo tipo árbol de decisión para cuantificar la importancia de cada variable en la clasificación. Los resultados indicaron que la información espectral basada en el índice de vegetación de diferencias normalizadas (NDVI, por sus siglas en inglés) aportó un 46% al modelo de clasificación, principalmente para la discriminación de objetos de vegetación y suelo desnudo. Por otra parte, para la identificación de las plántulas de amapola fue necesario incorporar además variables morfológicas (principalmente, el área [tamaño] del objeto, que aportó un 36% al modelo) y texturales (p.ej., textura media y entropía, con un 11% de contribución en el modelo). Por su parte, la distancia relativa de los objetos a la línea de cultivo tuvo escasa importancia en la clasificación total., [EN] A group of spectral, morphological (shape-based, size), contextual (location), and textural features were evaluated with the aim of automatic classification of Papaver rhoeas seedlings, other weeds, crop plants and bare soil in images collected with an unmanned aerial vehicle (UAV). The images were taken with a color-infrared modified camera at 0,60 cm/pixel of spatial resolution. The features were obtained by applying advanced object-based images techniques, and their contribution to the classification was analyzed with decision tree modeling. Spectral information from the normalized difference vegetation index (NDVI) contributed 46% to the model, mainly due to its capacity to discriminate vegetation and bare soil objects. Additionally, identification of Papaver rhoeas seedlings was possible by incorporating morphological (mainly object size, which contributed to 36% to the model) and textural features (e.g., mean and entropy, which contributed to 11% to the model). Finally, relative distance of the objects to the crop-rows had a low importance in the total classification results.

[ES] El manejo adecuado de las cubiertas vegetales en viñedos de regadío protege el suelo de la erosión y permite equilibrar el vigor y rendimiento de la viña, lo que redunda en una mejora de la calidad de la cosecha. Estas ventajas quedan mermadas con las infestaciones de grama (Cynodon dactylon) en las cubiertas, una especie perenne, altamente competitiva y difícilmente controlable. El objetivo de este trabajo consistió en la detección temprana y mapeo de C. dactylon en viñedos con cubierta vegetal mediante el uso de imágenes UAV (vehículos aéreos no tripulados) y técnicas de análisis de imágenes basadas en objetos (OBIA). El algoritmo desarrollado permitió la clasificación de los 4 usos principales presentes en el viñedo (viña, suelo desnudo, grama y cubierta vegetal) y la generación de mapas de grama para su control localizado, disminuyendo de esta manera el coste económico y medioambiental del tratamiento., [EN] The use of cover crops is a usual management practice for irrigated vineyards that allows controlling vineyard vigor and yield, also improving the crop quality. However, those advantages have been reduced by bermudagrass (C. dactylon) populations infesting cover crop areas. Bermudagrass is a perennial, very competitive grass and tolerant of reap, pretty difficult to control. The objective of this research was the early mapping of C. dactylon patches in order to provide an optimized site-specific weed management. Object-based image analysis (OBIA) techniques applied to unmanned aerial vehicle (UAV) imagery solved the limitation of spectral similarity between bermudagrass and cover crops or bare soil. The classified maps showed the four main classes in the vineyard (vine, cover crop, C. dactylon and bare soil) with 85% overall accuracy, that allow developing new strategies for site-specific control of C. dactylon infestations and decreasing economical and environmental costs.

[ES] Se ha desarrollado una metodología automática para la detección temprana de malas hierbas dentro y fuera de la línea de cultivo en fase temprana usando como variable discriminante la altura de las plantas (cultivo y malas hierbas), calculada mediante técnicas fotogramétricas. Se utilizó un UAV equipado con una cámara modificada para captar información en las bandas del Rojo, Verde e Infrarrojo Cercano. Se tomaron imágenes sobre una parcela de girasol infestada por diferentes especies de malas hierbas en fase temprana. Las imágenes fueron procesadas para generar una ortoimagen y un modelo tridimensional del cultivo y malas hierbas para su posterior análisis usando métodos orientados a objetos y el clasificador Random Forest. Se comparó la infestación de malas hierbas detectada con la realmente existente en el campo en una serie de marcos de referencia, obteniéndose un coeficiente de determinación de 0,91 entre ambas variables., [EN] An automatic methodology for early season weed detection between and within the crop rows has been developed, where the main innovation was the use of plant height as discriminant feature. An UAV equipped with a conventional camera modified for R (red), G (green) and NIR (near infrared) acquisition was used for taken images over a sunflower plot infested with different weed species in early season. The images were processed to generate an orthomosaic and a digital surface model (DSM) representing both the crop and weeds, and they were analyzed using object based image analysis (OBIA) and Random Forest classifier. Automatically detected weed cover was compared with the real weed cover in the field in a number of reference frames, and the determination coefficient between both variables was 0.91.

El manejo adecuado de las cubiertas vegetales en viñedos permite equilibrar el vigor y rendimiento de la viña, resultando en una mejora de la calidad de la cosecha. Estas ventajas quedan mermadas con las infestaciones de grama (Cynodon dactylon), una mala hierba perenne difícilmente controlable. Por ello se analizaron imágenes obtenidas en viñedos con vehículos aéreos no tripulados (UAV), mediante técnicas basadas en objetos (OBIA) para determinar la posición de los rodales de grama. El algoritmo desarrollado permitió la clasificación automática de las cuatro clases (viña, suelo desnudo, grama y cubierta vegetal) y la generación de mapas de grama para su control localizado. Esta técnica de viticultura de precisión conlleva una disminución del coste económico y medioambiental del tratamiento.

[Background] Tree pruning is a costly practice with important implications for crop harvest and nutrition, pest and disease control, soil protection and irrigation strategies. Investigations on tree pruning usually involve tedious on-ground measurements of the primary tree crown dimensions, which also might generate inconsistent results due to the irregular geometry of the trees. As an alternative to intensive field-work, this study shows a innovative procedure based on combining unmanned aerial vehicle (UAV) technology and advanced object-based image analysis (OBIA) methodology for multi-temporal three-dimensional (3D) monitoring of hundreds of olive trees that were pruned with three different strategies (traditional, adapted and mechanical pruning). The UAV images were collected before pruning, after pruning and a year after pruning, and the impacts of each pruning treatment on the projected canopy area, tree height and crown volume of every tree were quantified and analyzed over time., [Results] The full procedure described here automatically identified every olive tree on the orchard and computed their primary 3D dimensions on the three study dates with high accuracy in the most cases. Adapted pruning was generally the most aggressive treatment in terms of the area and volume (the trees decreased by 38.95 and 42.05% on average, respectively), followed by trees under traditional pruning (33.02 and 35.72% on average, respectively). Regarding the tree heights, mechanical pruning produced a greater decrease (12.15%), and these values were minimal for the other two treatments. The tree growth over one year was affected by the pruning severity and by the type of pruning treatment, i.e., the adapted-pruning trees experienced higher growth than the trees from the other two treatments when pruning intensity was low (<10%), similar to the traditionally pruned trees at moderate intensity (10–30%), and lower than the other trees when the pruning intensity was higher than 30% of the crown volume., [Conclusions] Combining UAV-based images and an OBIA procedure allowed measuring tree dimensions and quantifying the impacts of three different pruning treatments on hundreds of trees with minimal field work. Tree foliage losses and annual canopy growth showed different trends as affected by the type and severity of the pruning treatments. Additionally, this technology offers valuable geo-spatial information for designing site-specific crop management strategies in the context of precision agriculture, with the consequent economic and environmental benefits.