Nitrogen fertilization of silage maize in Central Italy is typically carried out with two applications at early stages of crop development: 2nd (V2) and 6th (V6) leaf respectively. In such conditions, the crop has not yet fully covered the soil and proximal or remote sensing of the canopy is hindered by the strong soil background signal. There is thus great interest in rapid and inexpensive approaches to N fertilization prescription. Therefore, an indirect method for inferring information on yield potential and soil variability, through a field-based clustering of multi-temporal satellite data, has been developed using archive Landsat images to identify temporally constant patterns. This method is potentially useful for the creation of prescription maps. The usefulness of the method was evaluated during an N fertilisation field trial in Maccarese (Central Italy), in 2016. At the V2 stage, both uniform and variable rate applications were performed and compared. A pseudo-cross variogram and a standardized ordinary co-kriging methodology was used to highlight spatially variable significant differences among the treatments.

Field vegetables require large amounts of N and precision N management (PNM) may help increase their productivity, quality and profitability while reducing N leaching in the biosphere. Few studies investigated PNM for field vegetables. This may be explained by the great diversity in crops and cultivars which complicate the broad implementation of PNM discoveries. Field vegetables are often grown in histosols, which have unique properties such as quantity of N mineralized and the spatial pattern of organic soil depth. Finally, research gaps exist in the development of decision support systems adapted to field vegetable crops grown in histosols.

In strawberry production, a balanced and accurate irrigation schedule is essential, because of the high sensitivity of strawberry plants to water deficits and waterlogging. The optimal irrigation management strategy can, however, only be obtained by an accurate crop monitoring system. To replace the current visual inspection methods, which are subjective, time consuming and labour-intensive, the performance of the COmpact hyperSpectral Imaging system (COSI) mounted on an RPAS (Remotely Piloted Aircraft System) was evaluated. The study, focusing on different irrigation treatments in strawberry cultivation, unraveled the potential of the COSI system, to monitor field variations, even at small scale. Growth inhibition and differences in plant physiology due to water deficit could be detected. As such, the COSI system has shown potential for steering irrigation management decisions in strawberry cultivation.

The research concerns the changes of spectral characteristics of reflected radiation (360 to 1 000 nm) of spring wheat leaves under nitrogen deficiency and moderate soil drought. The efficiency of factorial influence (η 2 ) on chlorophyll index was equal to 20% and 4% under nitrogen and water deficiency, respectively. Most significantly soil drought influenced the water index WRI (η 2=55%) and the light diffusion index R800 (η 2=28%), which was caused by changes in leaf structure. At low levels of nitrogen supply, these parameters did not change or changed only slightly (η 2 =2%). It may be deduced that the data base for crop monitoring in precision farming systems must contain a series of optical criteria for assessing specific and non-specific changes in optical characteristics of a crop canopy under the impact of various stress factors.

Farm equipment, including sensors and mobile machinery, create increasing amounts of data, and data can also be gained from third-party services. In order to be able to fully take advantage of this a farmer needs to be able to gather, store, process, and share the data as needed. In this work we describe a prototype for open environment that can gather, combine, store, select, and share data from arbitrary sources and with external partners, as well as use the data in decision making and provide it as input for various services. The environment is built using the Service Oriented Architecture paradigm and is therefore not tied to any specific operating system or software framework. We have tested the environment on the farm scale in Finland. The system was found suitable to improve the work in all tested tasks.

The difficulties of transporting heavy mobile robots limit robotic experiments in agriculture. Virtual reality however, offers an alternative to conduct experiments in agriculture. This paper presents an application of virtual reality in a robot navigational experiment using SolidWorks and simulated into MATLAB. Trajectories were initiated using Probabilistic Roadmap and compared based on travel time, distance and tracking error, and the efficiency was calculated. The simulation results showed that the proposed method was able to conduct the navigational experiment inside the virtual environment. U-turn trajectory was chosen as the best trajectory for crop inspection with 82.7% efficiency.

Crop phenotype is usually expressed in terms of characteristics like plant height, leaf architecture and leaf area index (LAI). In the case of maize, stalk diameter is seldom quantified because its measurement does not readily lend itself to automation. Justification for automating the measurement of stalk diameter and plant spacing is based on the finding that stalk diameter was able to account for about 65% of the variability in maize yield per plant in three irrigated field studies. A high-speed reflectance sensor and simulation apparatus was developed to explore the potential for automating maize stalk diameter assessment. The prototyped system accurately measured both stalk diameter and plant spacing in the laboratory at simulated velocities up to 12 km/h.

This paper discusses a conceptual design of a Ag Data service for the farm industry, compares it to desktops FMIS and discusses some of the main concepts this kind of system may include. Beginning with an introduction to the current situation and how the amount and size of the data is affecting the capacity to process it efficiently, on a personal computer desktop or other devices. Following with a description of the characteristics and components, presenting a case study to demonstrate the way it may function within a farm environment.

There is evidence that well managed winter cereal cover crops can scavenge a goodly amount of post summer cereal harvest residual nitrogen (N), reducing nitrate-N losses to leaching or runoff. The objective of this study was to compare nitrate-N phytoremediation areas derived from five sources of information: site specific, non-site specific, or a combination. The non-site specific source was a single “composite” soil nitrate sample. The site specific sources were: a) a dense soil nitrate-N grid sampling; and b) a N removal map calculated from yield and grain N concentration, both determined at the same grid density as soil nitrate-N. The source combinations were: a) a yield map and a single grain N concentration value taken from published information; and b) a yield map and a single field “composite” grain N concentration value. The results indicated that the published grain N value was inferior to measured grain N values, and that the maize (Zea mays L.) yield map best serves as a stratification tool, delineating similar crop performance areas. Random soil sampling within those areas further optimizes residual nitrate-N recovery management. Site specific technologies can guide establishment of N scavenging cover crops to simultaneously improve resource use efficiency and water quality.

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.

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.

For most producers, unmanned aerial vehicles (UAV) are a novelty that has been little employed in their agricultural operations. An UAV will not fix every problem on the farm, but there are some practical applications for which UAVs have demonstrated value. Three examples of how UAVs have been used in weed science applications are presented here; the methods are transferable to other agricultural commodities with similar characteristics. The first of these is quantification of the extent and severity of non-target herbicide injury. The second application is calculation of spray thresholds based on weed populations. The third application is development of site-specific herbicide treatment.

One among many challenges in implementing precision irrigation is the reliable characterization of the soil water content (SWC) across spatially variable fields. For this purpose, commercial retailers are employing apparent soil electrical conductivity (ECa) to create irrigation prescription maps. The accuracy of this method at the field scale has received little attention from the scientific community. Hence, the objective of this study was to characterize spatial distribution of soil water content at the field scale for the purpose of precision irrigation management. Results showed mean SWC to be different across ECa derived management zones, indicating that soil ECa was able to characterize mean differences in SWC across management zones.

A classical approach in precision agriculture consists in validating within field zones defined from high spatial resolution observations by agronomic information (AI). Zones validation generally involves a two-step process. First, AI are obtained on a regular grid or following a target sampling strategy inside the field. Then, a statistical test, most often an ANOVA, is used to determine if the management zones created with the high spatial resolution auxiliary data explain differences in the AI values. Unfortunately, in precision agriculture, many of the works using such an approach omit a necessary condition for the implementation of the aforementioned ANOVA test, i.e. the observations need to be independent from each other. This condition is unfortunately seldom satisfied since AI are often spatially auto-correlated. In order to highlight this problem, simulated datasets with different and known AI spatial autocorrelation were used. Results show that as AI are more and more spatially auto-correlated, ANOVA tests almost always conclude that the management zones obtained with auxiliary data are significant whatever the zoning, i.e. even a completely random one. To overcome this problem, the paper introduces two methods directly inspired from published works in the field of ecology. Two cases were considered: the first one applies when large AI dataset (n>40) is available and the other one applies for small AI dataset (n<40). Both methods are implemented on a real precision viticulture example.

The objective of this study was to determine how much improvement red edge-based vegetation indices (VIs) obtained with the RapidSCAN sensor would achieve for estimating rice nitrogen (N) nutrition index (NNI) at stem elongation stage (SE) as compared with commonly used normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) in Northeast China. Sixteen plot experiments and seven on-farm experiments were conducted from 2014 to 2016 in Sanjiang Plain, Northeast China. The results indicated that the performance of red edge-based VIs for estimation of rice NNI was better than NDVI and RVI. N sufficiency index calculated with RapidSCAN VIs (NSI_VIs) (R2=0.43–0.59) were more stable and more strongly related to NNI than the corresponding VIs (R2=0.12–0.38).

A ground-based hyperspectral imaging system covering the spectral range of 384–1034 nm was used for Sclerotinia Stem Rot (SSR) detection. Two sample sets of oilseed leaves were collected. Four vegetation indices were extracted and evaluated by analysis of variance (ANOVA) combined with linear discriminant analysis (LDA) for the two sample sets. Discriminant models were built using the 4 vegetation indices. The discriminant results of the two sample sets were good with classification accuracies of the calibration set and the prediction set over 85%. The overall results indicated that vegetation indices calculated from ground-based hyperspectral imaging could be used as reliable and accurate indices for SSR detection.

The precise application of pesticides to fruit crops requires information regarding the tree or vine canopy as a system input in order to control the amount of liquid and air being applied. Variations in canopy volume and density occur due to variety, trellis system, growth stage, training system and season. Current practice is to occasionally change liquid volume but seldom to change airflow. This paper details the development and validation of an ultrasonic sensor system to measure not only canopy volume but also canopy density and presence. Sensors fitted to the sprayer can record, in real time, changes in crop characteristics as the sprayer moves along the row. Signals can then send information to variable output nozzles and adjustable air fans. Trials have been conducted and results have proven to be extremely reliable and accurate. The ability to precisely control the spray results in the optimum application rate, leading to better results in the use of pesticides, less environmental pollution (less drift and less leaf runoff) and improved economic viability for the fruit grower.

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.

Bulk apparent soil electrical conductivity (ECa) sensors respond to multiple soil properties, including clay content, water content, and salt content (i.e. salinity). They provide a single sensor value for an entire soil profile down to a sensor-dependent measurement depth, weighted by a nonlinear response function. Because of this, it is generally difficult to elucidate strong relationships between ECa and the measured properties of individual soil layers. This research investigated inversion of the equations that govern the ECa-depth response relationship to reconstruct the soil conductivity in profile layers using data collected in multiple fields in the Midwest US. Layer conductivities obtained by inversion were first validated by comparison with true conductivities measured as a function of depth with an ECa-sensing penetrometer. Then, the validated layer conductivities were related to laboratory- measured soil properties. Inversion worked well but sometimes required iterative adjustment of initial conditions and other input parameters to obtain best results. Strong linear relationships (r2≥0.76) were obtained between inversion-estimated and measured layer conductivity data in all cases, sometimes with a truncated depth range. Layer conductivity data was successfully used to estimate soil texture fractions in the two alluvial fields examined. This was not the case for a claypan soil field, where there appeared to be parameters other than texture strongly affecting the EC response. Further examination of this approach is warranted to potentially provide improved ways to estimate depth-variable soil properties using ECa.