Postprint (published version

The use of a low-cost tractor-mounted scanning Light Detection and Ranging (LIDAR) system for capable of making non-destructive recordings of tree-row structure in orchards and vineyards is described. Field tests consisted of several LIDAR measurements on both sides of the crop row, before and after defoliation of selected trees. Summary parameters describing the tree-row volume and the total crop surface area viewed by the LIDAR (expressed as a ratio with ground surface area) were derived using a suitable numerical algorithm. The results for apple and pear orchards and a wine producing vineyard were shown to be in reasonable agreement with the results derived from a destructive leaf sampling method. Also, good correlation was found between manual and sensor-based measurements of the vegetative volume of tree-row plantations. The Tree Area Index parameter, TAI, gave the best correlation between destructive and non-destructive (i.e.LIDAR-based) determinants of crop leaf area. The LIDAR system proved to be a powerful technique for low cost, prompt and non-destructive estimates of the volume and leaf-area characteristics of plants.

Estimation of grapevine vigour using mobile proximal sensors can provide an indirect method for determining grape yield and quality. Of the various indexes related to\ud the characteristics of grapevine foliage, the leaf area index (LAI) is probably the most widely used in viticulture. To assess the feasibility of using light detection and ranging (LiDAR) sensors for predicting the LAI, several field trials were performed using a tractormounted LiDAR system. This system measured the crop in a transverse direction along the rows of vines and geometric and structural parameters were computed. The parameters evaluated were the height of the vines (H), the cross-sectional area (A), the canopy volume (V) and the tree area index (TAI). This last parameter was formulated as the ratio of the crop estimated area per unit ground area, using a local Poisson distribution to approximate\ud the laser beam transmission probability within vines. In order to compare the calculated indexes with the actual values of LAI, the scanned vines were defoliated to obtain LAI values for different row sections. Linear regression analysis showed a good correlation (R2 = 0.81) between canopy volume and the measured values of LAI for 1 m long sections.\ud Nevertheless, the best estimation of the LAI was given by the TAI (R2 = 0.92) for the same length, confirming LiDAR sensors as an interesting option for foliage characterization of grapevines. However, current limitations exist related to the complexity of data process and to the need to accumulate a sufficient number of scans to adequately estimate the LAI.Peer ReviewedPostprint (published version

Precision Viticulture (PV) is a concept that is beginning to have an impact on the wine-growing sector. Its practical implementation is dependant on various technological developments: crop sensors and yield monitors, local and remote sensors, Global Positioning Systems (GPS), VRA (Variable-Rate Application) equipment and machinery, Geographic Information Systems (GIS) and systems for data analysis and interpretation. This paper reviews a number of research lines related to PV. These areas of research have focused on four very specific fields: 1) quantification and evaluation of within-field variability, 2) delineation of zones of differential treatment at parcel level, based on the analysis and interpretation of this variability, 3) development of Variable-Rate Technologies (VRT) and, finally, 4) evaluation of the opportunities for site-specific vineyard management. Research in these fields should allow winegrowers and enologists to know and understand why yield variability exists within the same parcel, what the causes of this variability are, how the yield and its quality are interrelated and, if spatial variability exists, whether site-specific vineyard management is justifiable on a technical and economic basis.Additional key words: grape yield maps, local and remote sensors, selective vintage, within-field variability, yield monitor, zonal management. ResumenRevisión. Viticultura de precisión. Líneas de investigación, retos y oportunidades del manejo sitio-específico en viña La Viticultura de Precisión (VP) es un concepto que empieza a tener un cierto impacto en el sector vitivinícola. Su implementación práctica está ligada al desarrollo de cierta tecnología: sensores y monitores de cosecha, sensores locales y remotos, Sistemas de Posicionamiento Global (SPG), equipos y maquinaria de aplicación variable, Sistemas de Información Geográfica (SIG) y sistemas para el análisis y la interpretación de la información. En este trabajo se ha llevado a cabo una revisión de las diferentes líneas de investigación relacionadas con la VP. Dichas áreas de investigación se han centrado en cuatro ámbitos muy concretos: 1) cuantificación y evaluación de la variabilidad intraparcelaria, 2) delimitación a nivel de parcela de zonas de tratamiento diferencial, en base al análisis y la interpretación de dicha variabilidad, 3) desarrollo de tecnologías para la actuación variable en campo (variable-rate technologies, VRT) y, finalmente, 4) evaluación de la oportunidad del manejo sitio-específico en viticultura. La investigación en estos ám-bitos debe permitir a viticultores y enólogos conocer y comprender por qué la cosecha varía dentro de una misma parcela, cúales son las causas de dicha variación, cómo están interrelacionadas la cosecha y su calidad y, ante la existencia de variabilidad espacial, si está justificado técnica y económicamente el manejo diferencial de los viñedos.Palabras clave adicionales: manejo zonal, mapas de vendimia, monitor de cosecha, sensores locales y remotos, variabilidad intraparcelaria, vendimia...

LiDAR sensors are widely used in many areas and, in recent years, that includes agricultural tasks. In this work, a self-developed mobile terrestrial laser scanner based on a 2D light detection and ranging (LiDAR) sensor was used to scan an intensive olive orchard, and different algorithms were developed to estimate canopy volume. Canopy volume estimations derived from LiDAR sensor readings were compared to conventional estimations used in fruticulture/horticulture research and the results prove that they are equivalent with coefficients of correlation ranging from r=0.56 to r=0.82 depending on the algorithms used. Additionally, tools related to analysis of point cloud data from the LiDAR-based system are proposed to extract further geometrical and structural information from tree row crop canopies to be offered to farmers and technical advisors as digital raster maps. Having high spatial resolution information on canopy geometry (i.e. height, width and volume) and on canopy structure (i.e. light penetrability, leafiness and porosity) may result in better orchard management decisions. Easily obtainable, reliable information on canopy geometry and structure may favour the development of decision support systems either for irrigation, fertilization or canopy management, as well as for variable rate application of agricultural inputs in the framework of precision fruticulture/horticulture.