La integraci´on de sensores de agricultura de precisi´on en plataformas rob´oticas se utiliza ampliamente en el campo, lo quepermite la estimaci´on precisa de par´ametros agron´omicos como el volumen del dosel. Actualmente, estas metodolog´ıas se basanprincipalmente en mediciones de campo o en la generaci´on de datos lidar para estimar la altura del dosel y el di´ametro de la copa, aproxim´andolos a formas tridimensionales como elipsoides. Este art´ıculo propone una metodolog´ıa para la estimaci´on del volumen del dosel utilizando un vehiculo terrestre no tripulado (UGV), un brazo rob´otico y un lidar 2D para la reconstrucci´on 3D de plantas. La estimaci´on del volumen se realiza mediante un proceso iterativo para determinar el poliedro c´oncavo m´as peque˜no que contiene todos los puntos. El sistema est´a integrado en ROS y se aplica para reconstruir una hilera de 50 plantas en un vi˜nedo ubicado en Yepes, Espa˜na. Los resultados proporcionan reconstrucciones 3D de alta resoluci´on con un tiempo de adquisici´on y procesamiento de aproximadamente 46 segundos.

In Mediterranean farming systems, the semi-arid conditions and agricultural ecosystems have made site-specific management an important approach. This method aims to understand and handle the variability of soil properties and crop management, particularly through the utilization of geospatial information and accessible technology. Over three years in a 30 ha experimental field located in the Alentejo region (Portugal), crop establishment was monitored using data from soil apparent electrical conductivity (ECa), remote sensing (Sentinel-2), and in situ soil sampling. The procedure began with Step 1, involving the acquisition of soil spatial information and spatial interpolation. Subsequently, in Step 2, management zones (MZs) for soil characteristics were delineated using a combination of ECa measurements and soil analysis, and Step 3 spanned over three years of gathering meteorological data and crop remote sensing measurements. In Step 4, site-specific crop MZs were delineated by vegetation indexes (VIs). This article aims to increase the importance of in situ and remote assessments to more accurately identify areas with different productive potential. Results showed three MZs based on the percentage of sand, ECa, altimetry, exchangeable calcium, and exchangeable calcium properties, validated by crop VIs (Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), and Normalized Difference Moisture Index (NDMI)) over time. Although there are many sensorial techniques available for site-specific management, this paper emphasizes a methodology for the farmer to identify different MZs combining remote and in situ evaluations, supporting new opportunities for a more rational use of natural resources. Based on soil parameters, three site-specific management areas could be selected. NDMI was the index that best explained the MZs created according to soil properties.

Near-infrared (NIR) spectroscopy has been used to non-destructively and rapidly evaluate the quality of fresh agricultural produce. In this study, two commercially available portable spectrometers (F-750: Felix Instruments, WA, USA; and SCiO: Consumer Physics, Tel Aviv, Israel) were evaluated in the wavelength range between 740 and 1070 nm to non-invasively predict quality attributes, including the dry matter (DM), and total soluble solids (TSS) content of three fresh table grape cultivars (‘Autumn Royal’, ‘Timpson’, and ‘Sweet Scarlet’) and one peach cultivar (‘Cassie’). Prediction models were developed using partial least-square regression (PLSR) to correlate the NIR absorbance spectra with the invasive quality measurements. In regard to grapes, the best DM prediction models yielded an R2 of 0.83 and 0.81, a ratio of standard error of performance to standard deviation (RPD) of 2.35 and 2.29, and a root mean square error of prediction (RMSEP) of 1.40 and 1.44; and the best TSS prediction models generated an R2 of 0.97 and 0.95, an RPD of 5.95 and 4.48, and an RMSEP of 0.53 and 0.70 for the F-750 and SCiO spectrometers, respectively. Overall, PLSR prediction models using both spectrometers were promising to predict table grape quality attributes. Regarding peach, the PLSR prediction models did not perform as well as in grapes, as DM prediction models resulted in an R2 of 0.81 and 0.67, an RPD of 2.24 and 1.74, and an RMSEP of 1.28 and 1.66; and TSS resulted in an R2 of 0.62 and 0.55, an RPD of 1.55 and 1.48, and an RMSEP of 1.19 and 1.25 for the F-750 and SCiO spectrometers, respectively. Overall, the F-750 spectrometer prediction models performed better than those generated by using the SCiO spectrometer data.

Precision agriculture is a concept that encompasses various technologies aimed at optimizing the management of agricultural activities. The main aim of this investigation is to evaluate the environmental and economic performance of precision agriculture practices on the production of a stone fruit crop (nectarine) using a life cycle approach and to consider a cradle-to-farm gate scope. The results have been compared against the traditional uniform application (UA). The analysis considers five impact categories, including climate change, photochemical ozone formation, acidification, eutrophication, and water use. The foreground inventory data was provided by a local producer in Southern Spain, and the background information was sourced from commercial Life Cycle Inventory (LCI) databases. The results show that the manufacturing of crop inputs (mainly fertilizers, but also crop management inputs) is responsible for most of the damage generated in all the impact categories, except for water use. The reduced input requirements associated with the application of VA techniques resulted in significantly lower economic costs and environmental savings throughout the life cycle of the production system, which ranged on average between 12–26%.

To meet the increased demand for organic vegetables and improve their product quality, the Sureveg CORE Organic Cofund ERA-Net project focuses on the benefits and best practices of growing different crops in alternate rows. A prototype of a robotic platform was developed to address the specific needs of this field type at an individual plant level rather than per strip or field section. This work describes a novel method to develop robotic fertilization tasks in crop rows, based on automatic vegetable Detection and Characterization (D.a.C) through an algorithm based on artificial vision and Convolutional Neural Networks (CNN). This network was trained with a data-set acquired from the project’s experimental fields at ETSIAAB-UPM. The data acquisition, processing, anc actuation are carried out in Robot Operating System (ROS). The CNN’s precision, recall, and IoU values as well as characterization errors were evaluated in field trials. Main results show a neural network with an accuracy of 90.5% and low error percentages (<3%) during the vegetable characterization. This method’s main contribution focuses on developing an alternative system for the vegetable D.A.C for individual plant treatments using CNN and low-cost RGB sensors.

This paper set out to explore the precision agriculture (PA)-training needs of students studying in agricultural universities in the Euro-Mediterranean region (Greece, Italy, Portugal and Spain). SPARKLE is a Knowledge Alliance Project, funded by the European Union (EU), and one of its main goals is to narrow the innovation divide between entrepreneurship and the effective application of sustainable PA. During the project, the research conducted in all countries in the Euro-Mediterranean region revealed differences in the PA-training needs of university students. Additionally, this paper set out to explore the socioeconomic characteristics of students that affect their interest and knowledge towards PA. Finally, this paper aimed to understand the scope, present status and strategies for improving PA training in agricultural universities in the Euro-Mediterranean region. The following descriptive statistics and two multivariate analysis techniques were used: Two-Step Cluster Analysis (TSCA) and Categorical Regression (CATREG). Results support the notion that the lack of “PA knowledge/interest” adds to the technological gap amongst university students, slow adoption of PA and lower levels of overall rural economic development. These findings will be used as the fundamental cognition for the development of a joint action plan and several other national plans in the selected regions.

The interfaces for Human-Robot interaction in different fields such as precision agriculture (PA) have made it possible to improve production processes, applying specialized treatments that require a high degree of precision at the plant level. The current fertilization processes are generalized for vast cultivation areas without considering each plant's specific needs, generating collateral effects on the environment. The Sureveg Core Organic COfound ERA-Net project seeks to evaluate the benefits of growing vegetables in rows through the support of robotic systems. A robotic platform equipped with sensory, actuation, and communication systems and a robotic arm have been implemented to develop this proof of concept. The proposed method focuses on the development of a human-machine interface (IHM) that allows the integration of information coming from different systems from the robotized platform on the field and suggest to an operator (in a remote station) take a fertilization action based on specific vegetative needs to improve vegetable production. The proposed interface was implemented using Robot Operating System (ROS) and allows: visualizing the states of the robot within the crop by using a highly realistic environment developed in Unity3D and shows specific information of the plants' vegetative data fertilization needs and suggests the user take action. The tests to validate the method have been carried out in the fields of the ETSIAAB-UPM. According to the multi-spectral data taken after (2 weeks after being planted) and before (3 months after growth), main results have shown that NDVI indexes mean values in the row crop vegetables have normal levels around 0.4 concerning initial NDVI values, and its growth was homogeneous, validating the influence of ROBOFERT.

The growing demand for organically produced vegetables requires the adoption of new cropping systems such as strip-cropping. To counteract the additional labour mixed cropping entails, automation and robotics play a key role. This research focuses on the development of a proof-of-concept platform that combines optical sensors and an actuation system for targeted precision fertilization that encircles selected plants rather than a local field area. Two sensor types are used for the detection of a fertilisation need: a multispectral camera and light detection and ranging (LiDAR) devices in order to acquire information on plant health status and three-dimensional characterisation. Specific algorithms were developed to more accurately detect a change in fertilization need. An analysis of their results yields a prescription map for automatic fertilisation through a robotic arm. The relative location of the platform within the prescription map is essential for the correct application of fertilizers, and is acquired through live comparison of a LiDAR pushbroom with the known 3D world model. The geometry of each single plant is taken into account for the application of the sprayed fertiliser. This resulted in a reliable method for the detection of delayed growth and prototype localization within a changing natural environment without relying on external markers.

The development of new sensory and robotic technologies in recent years and the increase in the consumption of organic vegetables have allowed the generation of specific applications around precision agriculture seeking to satisfy market demand. This article analyzes the use and advantages of specific optical sensory systems for data acquisition and processing in precision agriculture for Robotic Fertilization process. The SUREVEG project evaluates the benefits of growing vegetables in rows, using different technological tools like sensors, embedded systems, and robots, for this purpose. A robotic platform has been developed consisting of Laser Sick AG LMS100 × 3, Multispectral, RGB sensors, and a robotic arm equipped with a fertilization system. Tests have been developed with the robotic platform in cabbage and red cabbage crops, information captured with the different sensors, allowed to reconstruct rows crops and extract information for fertilization with the robotic arm. The main advantages of each sensory have been analyzed with an quantitative comparison, based on information provided by each one; such as Normalized Difference Vegetation Index index, RGB Histograms, Point Cloud Clusters). Robot Operating System processes this information to generate trajectory planning with the robotic arm and apply the individual treatment in plants. Main results show that the vegetable characterization has been carried out with an efficiency of 93.1% using Point Cloud processing, while the vegetable detection has obtained an error of 4.6% through RGB images.

In precision agriculture (PA), vegetation indices (VI) are commonly used to evaluate the health of crops with the use of multi-spectral cameras. These are often mounted on drones and used at high altitudes, where the translation of focal points and subsequent changes in perspective do not pose any difficulties. In proximal sensing, however, these translations and distortions pose a significant challenge on data processing. In this work, a Parrot Sequoia camera was mounted at a fixed height of 1.2 m and used at 3 s and 1.5 s intervals on the field beneath. Reference imaging revealed that the multi-spectral lenses suffer from significant barrel distortion of 30%, while the higher resolution RGB lens has a barely distinguishable 1% pincushion distortion. The subsequent field images were stitched together using an open-source panorama software to automatically detect and correct distortions. The resulting mosaics were then shifted to correct the relative position of the separate lenses, allowing for VI calculation with mm accuracy. This method allows for analysing single plants for inter-crop and even intra-crop variation.

Lipid extraction using the traditional, destructive Soxhlet method is not able to measure oil content (OC) on a single olive. As the color and near infrared spectrum are key parameters to build an oil estimation model (EM), this study grouped olives with similar color and NIR for building EM of oil content obtained by Soxhlet from a cluster of similar olives. The objective was to estimate OC of individual olives, based on clusters of similar color and NIR in two seasons. This study was performed with Arbequina olives in 2016 and 2017. The descriptor of the cluster consisted of the three color channels of c1c2c3 color model plus 11 reflectance points between 1710 and 1735 nm of each olive, normalized with the Z-score index. Clusters of similar color and NIR spectrum were formed with the k-means++ algorithm, leaving a sufficient number of olives to perform the Soxhlet analysis of OC, as reference value of EM. The training of EM was based on Support Vector Machine. The test was performed with Leave One-Out Cross Validation in different training-testing combinations. The best EM predicted the OC with 6 and 13% deviation with respect to the real value when one season was tested with itself and with another season, respectively. The use of clustering in EM is discussed.

Within the framework of the European project named 'SPARKLE, an online course was created after studying educational needs on precision agriculture (PA), state of the art of technologies and a prospective study of the commercial sector. Five educational and research institutions, high-tech farms and enterprises specializing in technology transfer created the syllabus of the course and the platform contents. The course was designed to provide 30 h of student dedication, via online presentations, documents and videos for each topic. A free pilot course started in April 2020 and 385 students from Italy, Portugal, Greece & Spain enrolled. To trace performance and acquisition of competences, questionnaires were completed by students for each topic and a final overall test. Students' opinions about the course were also registered using anonymous polls, and results evaluated, to be able to enhance the Sparkle course for subsequent editions. Students also took part in a business model competition, to solve real challenges proposed by farms, related to the use of these technologies.

A key point in the process of information flow between the different subsystems that take part in the Precision Agriculture management systems is how machinery, software and other electronics systems communicate one each other. Several worldwide standards have been established (such as ISO 11783, known as "ISOBUS") that define precisely the way of transferring information and control commands by the internal electronic communications within farm machinery. Other standards (established or de facto) are also used, such as wireless communication in agriculture.

Many species of insect pests can be detected and monitored automatically. Several systems have been designed in order to improve integrated pest management (IPM) in the context of precision agriculture. Automatic detection traps have been developed for many important pests. These techniques and new technologies are very promising for the early detection and monitoring of aggressive and quarantine pests. The aim of the present paper is to review the techniques and scientific state of the art of the use of sensors for automatic detection and monitoring of insect pests. The paper focuses on the methods for identification of pests based in infrared sensors, audio sensors and image-based classification, presenting the different systems available, examples of applications and recent developments, including machine learning and Internet of Things. Future trends of automatic traps and decision support systems are also discussed.

The SUREVEG project focuses on improvement of biodiversity and soil fertility in organic agriculture through strip-cropping systems. To counter the additional workforce a robotic tool is proposed. Within the project, a modular proof of concept (POC) version will be produced that will combine detection technologies with actuation on a single-plant level in the form of a robotic arm. This article focuses on the detection of crop characteristics through point clouds obtained with two lidars. Segregation in soil and plants was successfully achieved without the use of additional data from other sensor types, by calculating weighted sums, resulting in a dynamically obtained threshold criterion. This method was able to extract the vegetation from the point cloud in strips with varying vegetation coverage and sizes. The resulting vegetation clouds were compared to drone imagery, to prove they perfectly matched all green areas in said image. By dividing the remaining clouds of overlapping plants by means of the nominal planting distance, the number of plants, their volumes, and thereby the expected yields per row could be determined.

This topic will provide an overview of the technologies available for georeferencing machinery or any agricultural equipment on the Earth’s surface. Principles of GNSS (global navigation satellite systems) will be presented, along with current satellite constellations such as NAVSTAR GPS, GLONASS, Beidou, Galileo, etc. Error correction based on SBAS services and RTK technology. RTK networks. Definition of static and dynamic errors and accuracy.

European farm sector is currently at a crossroad: embracing the future or staying stuck in old practices. The future is represented by Precision Agriculture (PA), which is a new mind-set approach and a new network of different technologies. For this reason, the farmer of the future, the so-called “agripreneur 4.0”, needs a mix of competencies such as agricultural, entrepreneurial, digital and environmental skills. One of the main goals of the SPARKLE project is creating a new e-Learning course for agricultural students, in order to enhance their business-oriented skills and entrepreneurial activity in a smart environment. In this paper the SPARKLE e-Learning Platform for Sustainable Precision Agriculture is presented.

A non-destructive measuring technique was applied to test major vine geometric traits on measurements collected by a contactless sensor. Three-dimensional optical sensors have evolved over the past decade, and these advancements may be useful in improving phenomics technologies for other crops, such as woody perennials. Red, green and blue-depth (RGB-D) cameras, namely Microsoft Kinect, have a significant influence on recent computer vision and robotics research. In this experiment an adaptable mobile platform was used for the acquisition of depth images for the non-destructive assessment of branch volume (pruning weight) and related to grape yield in vineyard crops. Vineyard yield prediction provides useful insights about the anticipated yield to the winegrower, guiding strategic decisions to accomplish optimal quantity and efficiency, and supporting the winegrower with decision-making. A Kinect v2 system on-board to an on-ground electric vehicle was capable of producing precise 3D point clouds of vine rows under six different management cropping systems. The generated models demonstrated strong consistency between 3D images and vine structures from the actual physical parameters when average values were calculated. Correlations of Kinect branch volume with pruning weight (dry biomass) resulted in high coefficients of determination (R2 = 0.80). In the study of vineyard yield correlations, the measured volume was found to have a good power law relationship (R2 = 0.87). However due to low capability of most depth cameras to properly build 3-D shapes of small details the results for each treatment when calculated separately were not consistent. Nonetheless, Kinect v2 has a tremendous potential as a 3D sensor in agricultural applications for proximal sensing operations, benefiting from its high frame rate, low price in comparison with other depth cameras, and high robustness.

A crop monitoring system was developed for the supervision of organic fertilization status on tomato plants at early stages. An automatic and nondestructive approach was used to analyze tomato plants with different levels of water-soluble organic fertilizer (3 + 5 NK) and vermicompost. The evaluation system was composed by a multispectral camera with five lenses: green (550 nm), red (660 nm), red edge (735 nm), near infrared (790 nm), RGB, and a computational image processing system. The water-soluble fertilizer was applied weekly in four different treatments: (T0: 0 mL, T1: 6.25 mL, T2: 12.5 mL and T3: 25 mL) and the vermicomposting was added in Weeks 1 and 5. The trial was conducted in a greenhouse and 192 images were taken with each lens. A plant segmentation algorithm was developed and several vegetation indices were calculated. On top of calculating indices, multiple morphological features were obtained through image processing techniques. The morphological features were revealed to be more feasible to distinguish between the control and the organic fertilized plants than the vegetation indices. The system was developed in order to be assembled in a precision organic fertilization robotic platform.

The color and NIR spectrum are key to build an oil estimation model, thus it requires individual olives clustering before the Sohlext oil extraction method can be applied. The objective was to analyze an OC estimation model of individual olives, based on cluster of similar color and NIR spectrum in different combination of the first and/or the second season. This study was performed with Chilean Arbequina olives in 2016 and 2017. The descriptor of the cluster consisted of the 3 color channels of c1, c2, c3 color model plus 11 reflectance points between 1710 and 1735 nm of each olive, normalized with the Z-score index. Clusters of similar color and NIR spectrum were formed with the k-means++ algorithm, leaving a sufficient amount of olives to be able to perform the Sohlext analysis of OC, as reference value. The estimation models were based on the Support Vector Machine. The test was carried out with the Leave One-Out Cross Validation in different training-testing combinations. The best model predicted the OC with 6% and 13%deviation respect to the real value in one season by itself and when one season tested with another season, respectively. The use of clustering in estimation model is discussed.