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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

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%.

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.

The use of robotic systems in organic farming has taken on a leading role in recent years, the Sureveg CORE Organic Cofund ERA-Net project seeks to evaluate the benefits of strip-cropping to produce organic vegetables. This includes, among other objectives, the development of a robotic tool that facilitates the automation of the fertilisation process, allowing the individual treatment (at the plant level). In organic production, the slower nutrient release of the used fertilisers poses additional difficulties, as a tardy detection of deficiencies can no longer be corrected. To improve the detection, as well as counter the additional labour stemming from the strip-cropping configuration, an integrated robotic tool is proposed to detect individual crop deficiencies and react on a single-crop basis. For the development of this proof-of-concept, one of the main objectives of this work is implementing a robust localisation method within the vegetative environment based on point clouds, through the generation of general point cloud maps (G-PC) and local point cloud maps (L-PC) of a crop row. The plants&rsquo, geometric characteristics were extracted from the G-PC as a framework in which the robot&rsquo, s positioning is defined. Through the processing of real-time lidar data, the L-PC is then defined and compared to the predefined reference system previously deduced. Both subsystems are integrated with ROS (Robot Operating System), alongside motion planning, and an inverse kinematics CCD (Cyclic Coordinate Descent) solver, among others. Tests were performed using a simulated environment of the crop row developed in Gazebo, followed by actual measurements in a strip-cropping field. During real-time data-acquisition, the localisation error is reduced from 13 mm to 11 mm within the first 120 cm of measurement. The encountered real-time geometric characteristics were found to coincide with those in the G-PC to an extend of 98.6%.

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., This research was funded by the RYC-2016-20355 agreement and by the Spanish Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER) (AGL2017-83325-C4-3-R).

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.

© 2020 by the authors., Crop 3D modeling allows site-specific management at different crop stages. In recent years, light detection and ranging (LiDAR) sensors have been widely used for gathering information about plant architecture to extract biophysical parameters for decision-making programs. The study reconstructed vineyard crops using light detection and ranging (LiDAR) technology. Its accuracy and performance were assessed for vineyard crop characterization using distance measurements, aiming to obtain a 3D reconstruction. A LiDAR sensor was installed on-board a mobile platform equipped with an RTK-GNSS receiver for crop 2D scanning. The LiDAR system consisted of a 2D time-of-flight sensor, a gimbal connecting the device to the structure, and an RTK-GPS to record the sensor data position. The LiDAR sensor was facing downwards installed on-board an electric platform. It scans in planes perpendicular to the travel direction. Measurements of distance between the LiDAR and the vineyards had a high spatial resolution, providing high-density 3D point clouds. The 3D point cloud was obtained containing all the points where the laser beam impacted. The fusion of LiDAR impacts and the positions of each associated to the RTK-GPS allowed the creation of the 3D structure. Although point clouds were already filtered, discarding points out of the study area, the branch volume cannot be directly calculated, since it turns into a 3D solid cluster that encloses a volume. To obtain the 3D object surface, and therefore to be able to calculate the volume enclosed by this surface, a suitable alpha shape was generated as an outline that envelops the outer points of the point cloud. The 3D scenes were obtained during the winter season when only branches were present and defoliated. The models were used to extract information related to height and branch volume. These models might be used for automatic pruning or relating this parameter to evaluate the future yield at each location. The 3D map was correlated with ground truth, which was manually determined, pruning the remaining weight. The number of scans by LiDAR influenced the relationship with the actual biomass measurements and had a significant effect on the treatments. A positive linear fit was obtained for the comparison between actual dry biomass and LiDAR volume. The influence of individual treatments was of low significance. The results showed strong correlations with actual values of biomass and volume with R2 = 0.75, and when comparing LiDAR scans with weight, the R2 rose up to 0.85. The obtained values show that this LiDAR technique is also valid for branch reconstruction with great advantages over other types of non-contact ranging sensors, regarding a high sampling resolution and high sampling rates. Even narrow branches were properly detected, which demonstrates the accuracy of the system working on difficult scenarios such as defoliated crops., This research was funded by the RYC-2016-20355 agreement and by the Spanish Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER) (AGL2017-83325-C4-3-R). The research leading to these results has received funding from RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (“Robótica aplicada a la mejora de la calidadde vida de los ciudadanos. fase IV”; S2018/NMT-4331), funded by “Programas de Actividades I + D en la Comunidad de Madrid” and cofunded by Structural Funds of the EU.

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 (&lsquo, Autumn Royal&rsquo, &lsquo, Timpson&rsquo, and &lsquo, Sweet Scarlet&rsquo, ) and one peach cultivar (&lsquo, Cassie&rsquo, ). 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.

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.

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 di erent 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 di erent treatments: (T0: 0 mL, T1: 6.25 mL, T2: 12.5 mL and T3: 25 mL) and the vermicomposting was added inWeeks 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., This article has been developed as a result of a mobility stay funded by the Erasmus+—KA1 Erasmus Mundus Joint Master Degrees Programme of the European Commission under the PLANT HEALTH Project. The authors acknowledge the financial support for this project provided by transnational funding bodies, being partners of the H2020 ERA-net project, CORE Organic Cofund, and the cofund from the European Commission.

New Super-High-Density (SHD) olive orchards designed for mechanical harvesting are increasing very rapidly in Spain. Most studies have focused in effectively removing the olive fruit, however the machine needs to put significant amount of energy on the canopy that could result in structural damage or extra stress on the trees. During harvest, a series of 3-axis accelerometers were installed on the tree structure in order to register vibration patterns. A LiDAR (Light Detection and Ranging) and a camera sensing device were also mounted on a tractor. Before and after harvest measurements showed significant differences in the LiDAR and image data. A fast estimate of the damage produced by an over-the-row harvester with contactless sensing could be useful information for adjusting the machine parameters in each olive grove automatically in the future.

La demanda de productos de agricultura ecológica está en auge, experimentando un continuo crecimiento. Este aumento de la demanda precisa de un crecimiento de la producción, que debe llevarse acabo sin renunciar al bajo impacto ambiental asociado a este tipo de agricultura. En este proyecto se trabaja en una herramiental robótica para automatizar la fertilización orgánica de cultivos en franjas.

La agricultura actual está en proceso de transformación gracias a la introducción de nuevas tecnologías para conseguir un mejor uso de los recursos. Todo ello se engloba en una rama de la ingeniería que llamamos “Agricultura de Precisión” que hace uso del GPS, el guiado automático de tractores, las redes de sensores en campo, la mecatrónica y sensórica a bordo de la maquinaria, la telemetría de flotas o riegos, el envío a la nube de los datos de campo, los cuadernos digitales de explotación, el análisis avanzado de los datos … y muchas otras que están variando el tópico de un sector productivo primario atrasado y reacio al cambio.En todo este cambio el papel del ingeniero agrónomo sigue siendo fundamental como enlace principal entre la producción agropecuaria y las herramientas de la ingeniería. Sin embargo, es un hecho constatado que las empresas del sector demandan profesionales con destrezas digitales que las actuales carreras no están proporcionando. Es necesario reformular las competencias del “ingeniero agrónomo digital”, no primando únicamente la tecnología, sino facilitando que se ponga al servicio del agricultor y la sociedad en un entorno sostenible.El proyecto de innovación educativa SPARKLE ha sido seleccionado en la Unión Europea para que un grupo de 5 universidades , 3 empresas (incluyendo un gran organismo público de investigación) y 3 productores avanzados estudien y definan cuales son las necesidades formativas para que nuestros estudiantes lleguen a ser verdaderos ingenieros agrónomos digitales. SPARKLE es un proyecto Erasmus+.Las primeras acciones llevadas a cabo por los socios del proyecto han sido la realización de un gran número de encuestas entre alumnos de escuelas de agricultura en todos los países del consorcio, entrevistas con expertos internacionales, estudios del estado del arte sobre la Agricultura de Precisión y la Robótica Agrícola, prospectiva sobre la evolución de las patentes relativas a las nuevas tecnologías en el ámbito agrario, y el estudio detallado de los modelos de negocio de empresas agrícolas que han aplicado con éxito la agricultura digital.Los resultados de las encuestas, especialmente los realizados entre estudiantes de ingeniería en universidades españolas, muestran la inquietud e interés hacia las nuevas tecnologías, pero a la vez una demanda creciente de destrezas y habilidades transversales que complementen su formación.

La demanda de los productos orgánicos está creciendo, por lo cual los agricultores tradicionales están viendo la necesidad de cambiar sus empresas. Sin embargo, la falta de atención a la biodiversidad y la fertilidad del suelo en la práctica actual pueden dañar la credibilidad de los productos orgánicos en general. Por eso el proyecto SUREVEG se enfoca en la producción orgánica en sistemas de tipo strip-cropping (cultivos mixtos en franjas) y en la aplicabilidad en la agricultura intensiva como manera para mejorar la fertilidad y biodiversidad en toda Europa. El objetivo es mejorar la resiliencia, la sostenibilidad del sistema, el reciclaje local de nutrientes y el almacenamiento de carbono en el suelo. Para contrarrestar la mano de obra adicional inherente a un sistema de cultivos múltiples, uno de los objetivos principales de este proyecto es el desarrollo de maquinaria automatizada para la gestión de los sistemas strip-cropping. Se propone una herramienta robótica suspendida de un carro de amplitud larga, similar a los sistemas de pórtico utilizados en la agricultura de tráfico controlado. Dentro del marco del proyecto, se producirá una versión modular de prueba de concepto, que combinará tecnologías de detección con la actuación en forma de un brazo robótico. Esta prueba de concepto se centrará en las necesidades de fertilización, que se identificarán en tiempo real y se aplicarán a nivel de plantas individuales.Los primeros enfoques de detección consisten en medidas con sensores lidar sobre varios campos de strip-cropping, para poder comparar la arquitectura de cultivos en bandas con su contraparte en monocultivos. La hipótesis es que el rendimiento del cultivo en bandas excede el rendimiento de los cultivos de la configuración tipo monocultivo, con una diferencia adicional esperada entre 'filas centrales' y 'filas de borde'. Esa diferencia se originaría debido a la interacción de especies vegetales mutuamente beneficiosas en los bordes del sistema de cultivos múltiples, lo cual produce un cambio significativo en la estructura de la planta y el volumen del dosel. En este trabajo se ha estudiado repollo en específico, con un enfoque en el volumen de la planta por área y su consistencia en todo el campo en dos configuraciones diferentes (monocultivo, frente a franjas). Con nubes de puntos obtenidos con lidares hacia el final de la temporada de crecimiento, se ha realizado una estimación del volumen del cultivo, que puede correlacionarse con las diferencias de rendimiento. Los primeros resultados confirman la hipótesis y proporcionan información útil para el desarrollo de un futuro vehículo robotizado de fertilización automatizada.

González-de-Santos, Pablo et al., Feeding the growing global population requires an annual increase in food production. This requirement suggests an increase in the use of pesticides, which represents an unsustainable chemical load for the environment. To reduce pesticide input and preserve the environment while maintaining the necessary level of food production, the efficiency of relevant processes must be drastically improved. Within this context, this research strived to design, develop, test and assess a new generation of automatic and robotic systems for effective weed and pest control aimed at diminishing the use of agricultural chemical inputs, increasing crop quality and improving the health and safety of production operators. To achieve this overall objective, a fleet of heterogeneous ground and aerial robots was developed and equipped with innovative sensors, enhanced end-effectors and improved decision control algorithms to cover a large variety of agricultural situations. This article describes the scientific and technical objectives, challenges and outcomes achieved in three common crops., The research leading to these results received funding from the European Union’s Seventh Framework Programme [FP7/2007-2013] under Grant Agreement nº 245986. Support for publishing this article has been provided by CSIC.

Mealiness is known as an important internal quality attribute of fruits/vegetables, which has significant influence on consumer purchasing decisions. Mealiness has been a topic of research interest over the past several decades. A number of destructive and nondestructive techniques are introduced for mealiness detection. Nondestructive methods are more interesting because they are rapid, noninvasive, and suitable for real-time purposes. In this review, the concept of mealiness is presented for potato, apple, and peach, followed by an in-depth discussion about applications of destructive and nondestructive techniques developed for mealiness detection. The results suggest the potential of electromagnetic-based techniques for nondestructive mealiness evaluation. Further investigations are in progress to find more appropriate nondestructive techniques as well as cost and performance.

The feasibility of automated individual crop plant care in vegetable crop fields has increased, resulting in improved efficiency and economic benefits. A systems-based approach is a key feature in the engineering design of mechanization that incorporates precision sensing techniques. The objective of this study was to design new sensing capabilities to measure crop plant spacing under different test conditions (California, USA and Andalucía, Spain). For this study, three different types of optical sensors were used: an optical light-beam sensor (880 nm), a Light Detection and Ranging (LiDAR) sensor (905 nm), and an RGB camera. Field trials were conducted on newly transplanted tomato plants, using an encoder as a local reference system. Test results achieved a 98% accuracy in detection using light-beam sensors while a 96% accuracy on plant detections was achieved in the best of replications using LiDAR. These results can contribute to the decision-making regarding the use of these sensors by machinery manufacturers. This could lead to an advance in the physical or chemical weed control on row crops, allowing significant reductions or even elimination of hand-weeding tasks Junta de Andalucía P12-AGR-1227 Ministerio de Economía, Industria y Competitividad AGL2013-46343-R

Active optical sensing (LIDAR and light curtain transmission) devices mounted on a mobile platform can correctly detect, localize, and classify trees. To conduct an evaluation and comparison of the different sensors, an optical encoder wheel was used for vehicle odometry and provided a measurement of the linear displacement of the prototype vehicle along a row of tree seedlings as a reference for each recorded sensor measurement. The field trials were conducted in a juvenile tree nursery with one-year-old grafted almond trees at Sierra Gold Nurseries, Yuba City, CA, United States. Through these tests and subsequent data processing, each sensor was individually evaluated to characterize their reliability, as well as their advantages and disadvantages for the proposed task. Test results indicated that 95.7% and 99.48% of the trees were successfully detected with the LIDAR and light curtain sensors, respectively. LIDAR correctly classified, between alive or dead tree states at a 93.75% success rate compared to 94.16% for the light curtain sensor. These results can help system designers select the most reliable sensor for the accurate detection and localization of each tree in a nursery, which might allow labor-intensive tasks, such as weeding, to be automated without damaging crops. European Union 245986

Soil erosion is a serious environmental threat in the Mediterranean region due to torrential rainfalls, and it contributes to the degradation of agricultural land. Techniques such as rainwater harvesting may improve soil water storage and increase agricultural productivity, which could result in more effective land usage. Reservoir tillage is an effective system of harvesting rainwater, but it has not been scientifically evaluated like other tillage systems. Its suitability for the conditions in Spain has not been determined. To investigate and quantify water storage from reservoir tillage and how it could be adapted to improve infiltration of harvested rainwater, a laboratory-scale rainfall simulator was developed. Rainfall characteristics, including rainfall intensity, spatial uniformity and raindrop size, confirm that natural rainfall conditions are simulated with sufficient accuracy. The simulator was auto-controlled by a solenoid valve and three pressure nozzles were used to spray water corresponding to five rainfall intensities ranging from 36 to 112 mm h-1 for 3 to 101-year return period with uniformity coefficients between 83 and 94%. In order to assess the reservoir tillage method under surface slopes of 0, 5, and 10%, three soil scooping devices with identical volume were used to make depressions in the following forms: a) truncated square pyramid, b) triangular prism, and c) truncated cone. These depressions were compared to a control soil surface with no depression. For the loam soil used in this study, results show that reservoir tillage was able to reduce soil erosion and surface runoff and significantly increase infiltration. There was significant difference between the depressions and the control. Compared to the control, depression (a) reduced surface runoff by about 61% and the sediment yield concentration by about 79%.

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited., In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12-14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying. © 2013 by the authors; licensee MDPI, Basel, Switzerland.

New super-high-density (SHD) olive orchards designed for mechanical harvesting using over-the-row harvesters are becoming increasingly common around the world. Some studies regarding olive SHD harvesting have focused on the effective removal of the olive fruits; however, the energy applied to the canopy by the harvesting machine that can result in fruit damage, structural damage or extra stress on the trees has been little studied. Using conventional analyses, this study investigates the effects of different nominal speeds and beating frequencies on the removal efficiency and the potential for fruit damage, and it uses remote sensing to determine changes in the plant structures of two varieties of olive trees (‘Manzanilla Cacereña’ and ‘Manzanilla de Sevilla’) planted in SHD orchards harvested by an over-the-row harvester. ‘Manzanilla de Sevilla’ fruit was the least tolerant to damage, and for this variety, harvesting at the highest nominal speed led to the greatest percentage of fruits with cuts. Different vibration patterns were applied to the olive trees and were evaluated using triaxial accelerometers. The use of two light detection and ranging (LiDAR) sensing devices allowed us to evaluate structural changes in the studied olive trees. Before- and after-harvest measurements revealed significant differences in the LiDAR data analysis, particularly at the highest nominal speed. The results of this work show that the operating conditions of the harvester are key to minimising fruit damage and that a rapid estimate of the damage produced by an over-the-row harvester with contactless sensing could provide useful information for automatically adjusting the machine parameters in individual olive groves in the future. Ministerio de Economía y Competitividad AGL2013-46343-R Junta de Andalucía P12-AGR-1227

Nondestructive and destructive measures of color and firmness were studied to determine the feasibility of predicting the level of damage to clingstone peaches during mechanical pitting. Nondestructive and destructive measures of firmness were equally variable when measuring the firmness at three equatorial cheek locations (coefficient of variation of about 17%), both had inverse relationships with the level of pitting damage (r2 ranged from 0.70 to 0.83), and could classify peaches into two categories (those subject to and those not subject to pitting damage) with classification accuracies of 75.2% and 81.7%, respectively. Destructive firmness was not a good predictor of nondestructive firmness in clingstone peaches. Skin color was not a good predictor of flesh color in clingstone peaches, and flesh color was not a good predictor of potential for damage to clingstone peaches during mechanical pitting.

There is a need for in-situ soil moisture conservation in arid and semi-arid regions due to insufficient rainfall for agriculture. For this purpose, a combination implement [integrated reservoir tillage system (RT)] comprised of a single-row chisel plow, single-row spike tooth harrow, modified seeder, and spiked roller was developed and compared to the popular tillage practices, viz ., minimum tillage (MT) and conventional tillage (CT) in an arid Mediterranean environment in Egypt. The different tillage practices were conducted at tillage depths of 15, 20, and 25 cm and forward speeds of 0.69, 1, 1.25, and 1.53 m s −1 . Some soil physical properties, runoff, soil loss, water harvesting efficiency and yield of wheat were evaluated. The different tillage practices caused significant differences in soil physical properties as the RT increased soil infiltration, producing a rate of 48% and 65% higher than that obtained in MT and CT, respectively. The lowest values of runoff and soil loss were recorded under RT as 4.91 mm and 0.65 t ha −1 , whereas the highest values were recorded under CT as 11.36 mm and 1.66 t ha −1 , respectively. In conclusion, the RT enhanced the infiltration rate, increased water harvesting efficiency, reduced runoff and achieved the highest yield of wheat. The best tillage operating parameters appeared to be at a tillage depth of 20 cm and speed between 1.00 and 1.25 m s −1 .

The requirements for a good stand in a no-till field are the same as those for conventional planting as well as added field and machinery management. Among the various factors that contribute towards producing a successful maize crop, seed depth placement is a key determinant. Although most no-till planters on the market work well under good soil and residue conditions, adjustments and even modifications are frequently needed when working with compacted or wet soils or with heavy residues. The main objective of this study, carried out in 2010, 2011 and 2012, was to evaluate the vertical distribution and spatial variability of seed depth placement in a maize crop under no-till conditions, using precision farming technologies and conventional no-till seeders. The results obtained indicate that the seed depth placement was affected by soil moisture content and forward speed. The seed depth placement was negatively correlated with soil resistance and seeding depth had a significant impact on mean emergence time and the percentage of emerged plants. Shallow average depth values and high coefficients of variation suggest a need for improvements in controlling the seeders’ sowing depth mechanism or more accurate calibration by operators in the field.