Site-specific management provides the ability to align the production intensity to demand and thus adjust the expenses to the necessary level. So it is possible to increase the proportion of marketable commodity in the normal sort–size of 40 mm to 60 mm. Planting distances adapted to the soil properties seem to achieve this objective. It is possible to further optimize the proportion of marketable commodity especially in the potato regions where irrigation and fertilization already contribute to a consistently high yield. Different planting distances on the soil sites by EM38 were tested in field trials. Planting distances of 31.50 cm in the row on the light (sandy) soil, 24.50 cm on middle and 27.50 cm on the heavy soil sites seems the best for these three years. There is a yield impact in total, as well as in the proportion of marketable commodity. Depending on the planting strategy, increases in income up to €153 per hectare can be obtained.

One of the significant obstacles in achieving a reliable seed germination and even plant field emergence in no-till seeding is a variation in the desired seeding depth. This is caused by the inappropriate response of the seeder motion dynamics to harsh soil conditions and to high operating speed. In order to assess the dynamic response of a no-till seeder, a mathematical model, which simulated the vertical motion of a seeding aggregate, was developed. A correlation between the simulated and the measured parameters resulted in a root-mean-squared (RMS) error of 17.2% and 6.4% for impact force and pitch angle, respectively. The simulated impact force frequencies of interests were detected at the critical frequencies of the measured forces with high coherence values.

In this research a multi-sensor and data fusion approach was developed to create variable depth tillage zones. Data collected with an electromagnetic sensor was fused with measurements taken with a hydraulic penetrometer and conventionally acquired soil bulk density (BD) and moisture content (MC) measurements. Packing density values were then calculated for eight soil layers to determine the need to cultivate or not. From the results 62% of the site required the deepest tillage at 38 cm, 16% required tillage at 33 cm and 22% required no tillage at all. The resultant maps of packing density were shown to be a useful approach to map layered soil compaction and guide VDT operations.

Chemical thinning in apple orchards is a commonly used technique for improving yield. The objective of this work was to quantify bloom intensity of individual trees using color images, and estimate the time for the peak of the bloom. Image acquisition campaigns were conducted in an apple orchard with Golden Delicious variety during two growing seasons. Image processing algorithms were developed to detect flowers. The correlation between the manual and automatic estimation of bloom intensity at the day of the peak was 0.90 and 0.97 for 2014 and 2015 respectively. Based on the above relationships, maps of blooming intensity were derived and its variability was established.

Different sampling schemes were tested to estimate yield (kg/tree), fruit firmness (kg) and the refractometric index (°Baumé) in a peach orchard. In contrast to simple random sampling (SRS), the use of auxiliary information (NDVI and apparent electrical conductivity, ECa) allowed sampling points to be stratified according to two or three classes (strata) within the plot. Sampling schemes were compared in terms of accuracy and efficiency. Stratification of samples improved efficiency compared to SRS. However, yield and quality parameters may require different sampling strategies. While yield was better estimated using stratified samples based on the ECa, fruit quality (firmness and °Baumé) showed better results when stratifying by NDVI.

LiDAR (Light detection and ranging) technology is an alternative to current manual methods of canopy geometry estimations in orange trees. The objective of this work was to compare different types of canopy volume estimations of orange trees, some inspired on manual methods and others based on a LiDAR sensor. A point cloud was generated for 25 individual trees using a laser scanning system. The convex-hull and the alpha-shape surface reconstruction algorithms were tested. LiDAR derived models are able to represent orange trees more accurately than traditional methods. However, results differ significantly from the current manual method. In addition, different 3D modeling algorithms resulted in different canopy volume estimations. Therefore, a new standard method should be developed and established.

Precision agriculture for banana crops has been little investigated so far. The main difficulty to implement precision agriculture methods lies in the asynchronicity of this crop: after a few cycles, each plant has its own development stage in the field. Indeed, maps of agronomical interest are difficult to produce from plant responses without implementing new methods. The present study explores the feasibility to derive a spatially relevant indicator from the date of flowering and the date of maturity (time to harvest). The time between these dates (TFM) may give insight in spatial distribution of vigor. The study was carried out using production data from 2015 acquired in a farm from Cameroon. Data from individual plants that flowered at different weeks were gathered so as to increase the density of TFM sampling. The temporal variability of TFM, which is induced by weather and operational constraints, was compensated by centering TFM data on their medians (TFMc). The mapping of TFMc was obtained using a classical kriging method. Spatial structures highlighted by TFMc either at the farm level or at the plot level, suggest that such maps could be used to support agronomic decisions.

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.

The present work investigated the application of detailed airborne images and a resistivity soil sensor (Veris 3100) to detect soil and crop spatial variability to assist in orchard management. The research was carried out in a peach orchard (Prunus persica). Soil apparent electrical conductivity (ECa), NDVI from a multispectral image (0.25 m/pixel) and soil properties at 40 sampling points (0–30 cm) were acquired. The ECa was standardized at 25°C. It showed a strong relationship with former landforms, altered by land levelling. A positive correlation of EC25 with EC1:5, water holding capacity at −1500 kPa and soil depth was found. NDVI was correlated only in the textural fractions coarser than clay. Two types of management zones were proposed: a) to improve the water holding capacity of soils and b) to regulate tree vigour and yield.

This paper evaluates the potential of very high resolution multispectral (Worldview-3) satellite imagery for mapping yield parameters in avocado and macadamia orchards. An evaluation of 18 structural and pigment based vegetation indices (VIs) derived from Worldview-3 imagery identified a positive relationship to nut/ fruit weight (kg/tree) R2>0.69 for macadamia and R2>0.68 for avocado; and nut/ fruit number (per tree) R2>0.6 for macadamia and R2>0.61 for avocado. Using the algorithms derived between the optimal VI and the measured parameter, yield and nut/ fruit number maps were derived for each block. In the absence of a commercial yield monitor, the resulting yield maps offer significant benefit to growers for improving orchard management, harvest scheduling, and forward selling decisions.

In order to enable the wine industry to anticipate in field work and marketing strategies, it is necessary to provide early assessments of vine productivity. The proposed method is designed for the detection and the measurement of grape bunches between the flowering season and the early fruition stages, before ‘groat-size’. The method consists of determining the affiliation of a pixel to a grape cluster based on colorimetric and texture features, using an SVM supervised classifier. The eventual affiliation of the pixels is achieved with an average reliability above 75%, which lets us envision in the near future the possibility of estimating the real number of grape bunches.

Research on precision viticulture on table grapes is relative new compared to wine grapes. The aim of this study was to assess the spatiotemporal stability of yield and quality components in a vineyard of table grapes. The study was conducted in a commercial vineyard (1.4 ha) of table grapes for two successive cultivation years 2015 and 2016. Yield and quality parameters were assessed using destructive and non-destructive methods. The preliminary results revealed that spatiotemporal stability of management zones is affected by weather conditions and is different for each crop parameter, while NDVI presented good performance for delineating management zones.

This paper proposes a methodology aiming at using historical yield data to improve yield sampling and yield estimation. The sampling method is based on a collaboration between historical data (at least three years) and yield measurements of the year performed on some sites within the field. It assumes a temporal stability of within field yield spatial patterns over the years. The first factor of a principal component analysis (PCA) is used to summarize the stable temporal patterns of within field yield data and it represents a large part of the variability of the different years assuming yield temporal stability and a high positive correlation between this factor and the yield. This main factor is then used to choose the best sites to sample (target sampling). Yield measurements are then used to calibrate a model that relates yield values to coordinates on the first factor of the PCA. This sampling method was tested on three vine fields (Vitis vinifera L.) in Chile and France with different varieties (Chardonnay, Cabernet Sauvignon and Syrah). For each of these fields, yield data of several years were available at the within field level. After temporal stability of yield patterns was verified for almost all the fields, the proposed sampling method was applied. Results were compared to those of a classical random sampling method showing that the use of historical yield data allows sampling sites selection to be optimized. Errors in yield estimations were reduced by more than 10% in all the cases, except when yield stable patterns are affected by specific events, i.e. early frost occurring on Chardonnay field.

The goal of this work was the assessment of commercial vineyard water status using on-the-go thermal imaging. On-the-go thermal imaging acquisition was conducted with a thermal camera operating at 1.20 m distance from the canopy, mounted on a quad moving at 5 km/h. Canopy temperature, cross water stress index (CWSI) and stomatal conductance index (Ig) were strongly and significantly correlated to stem water potential (Ψstem) in east and west side of the canopy. For CWSI, the values of the coefficient of determination (R2) were 0.88*** and 0.73*** for east and west sides, respectively. As regards the index Ig, its relationships with Ψstem showed R2=0.89*** and R2=0.77*** for east and west sides, respectively. These results are promising and evidence the potential of on-the-go thermal imaging to become a new tool to evaluate the vineyard water status.

Monitoring grapevine canopy size and evolution during time is of great interest for the management of the vineyard. An interesting and cost effective solution for 3D characterization is provided by the Kinect sensor. To assess its practical applicability, field experiments were carried out on two different grapevines varieties (Glera and Merlot) for a three months period. The results from 3D digital imaging were compared with those achieved by direct hand-made measurements. Estimated volume was then effectively correlated to the number of leaves and to the leaf area index. The experiments demonstrated how a low cost 3D sensor can be applied for fast and repeatable reconstruction of vine vegetation, opening up for new potential improvements in variable rate application or pruning

Yield mapping techniques have only recently started to be implemented by the Californian wine grape industry, but the advancement has necessitated new processing methods for large vineyards. The process for mapping large blocks harvested with multiple machines has only recently occurred and implies that their yield monitors have to be calibrated and corrected to the same scale. Here we discuss two methods for processing yield maps at the commercial level. Method 1 depends on many calibrations with delivered fruit weight to a winery. Method 2 normalizes raw files automatically can reduce total processing time by up to 90%.

The aim of this work is to calibrate and validate an empirical approach to predict the date of occurrence of the grapevine phenology (budburst, flowering and veraison) temporally and spatially at the within-field scale. It is based on the collaboration between a classical model of phenology based on climate data and a spatial model calibrated with ancillary data of phenology observations. This approach was tested and validated on a field of cv Cabernet Sauvignon. Results showed that the spatial component improved the fit of the climatic model, allowing the generation of maps of the grapevine phenology with errors lower than 5 days of prediction. Spatio-temporal model errors were mainly associated with the temporal component of the model.

A field study was carried out to investigate the seasonal variations in alfalfa (Medicago sativa L.) water use efficiency (WUE) using Eddy Covariance (EC) measured CO2 and H2O fluxes, aiming at optimizing the use of irrigation water under hyper arid conditions. The EC system used for this study was installed on a center pivot-irrigated 50 ha alfalfa field. Results revealed that the net EC estimated CO2 uptake ranged from 65,00 kg ha−1 (in winter) to 21,500 kg ha−1 (in summer). While, H2O flux was 4,147 m3 ha−1 (in winter) and 20,157 m3 ha−1 (in summer). This resulted in an estimated alfalfa WUE of 1.57 and 1.07 kg m−3 for winter and summer seasons, respectively. However, the actual WUE of harvested alfalfa was calculated at 0.70 and 0.71 kg m−3 for winter and summer, respectively. Therefore, attaining an actual crop WUE of 33–55% lower than the EC measurement (i.e. more water losses were due to leaching and deep-percolation processes, as the EC system could only estimate evapotranspiration over agricultural fields) emphasizes the need of precision irrigation practices, which will enable farmers to apply irrigation water and agrochemicals more precisely and site-specifically to match soil and plant status and needs.

To use VRI systems, a field is divided into irrigation management zones (IMZs). While IMZs are dynamic in nature, most of IMZs prescription maps are static. High-resolution thermal images (TI) coupled with measured atmospheric conditions have been utilized to map the within-field water status variability and to delineate in-season IMZs. Unfortunately, spaceborne TIs have coarse spatial resolution and aerial platforms require substantial financial investments, which may inhibit their large-scale adoption. Three approaches are proposed to facilitate large-scale adoption of TI-based IMZs: 1) increase of the capacity of aerial TI by enhancing their spatial resolution; 2) sharpening the spatial resolution of satellite TI by fusing satellite multi-spectral images in the visible-near-infrared (VIS-NIR) range; 3) increase the capacity of aerial TI by fusing satellite multi-spectral images in the VIS-NIR range. The scientific and engineering basis of each of the approaches is described together with initial results.

An experiment was carried out to assess the performance of an automatic irrigation system based on remotely-sensed soil moisture measurements in blueberries grown in sandy soil under plastic tunnels. The system uses the information provided by soil moisture sensors installed at three soil depths (10, 20 and 30 cm) and user-defined irrigation set-points to trigger automatic irrigation events. An Optimized irrigation treatment in which irrigation set-points and duration of irrigation have been determined from soil hydraulic properties analysis and soil water modelling is compared to the irrigation scheduling criteria established by the grower (Control treatment). The results showed that the Optimized treatment applied 30% of the water applied by the Control treatment, led to mild to moderate crop water stress conditions and had no impact on berry yield and quality.