This paper will present a dynamic Variable Rate Irrigation System developed by the University of Georgia. The system consists of the EZZone management zone delineation tool, the UGA Smart Sensor Array (UGA SSA) and an irrigation scheduling decision support tool. An experiment was conducted in 2015 and 2016 in two different peanut fields to evaluate the performance of using the UGA SSA to dynamically schedule Variable Rate Irrigation (VRI). For comparison reasons strips were designed within the fields. These strips were irrigated according to either UGA SSA or Irrigator Pro recommendations. The results showed that Irrigator Pro is a conservative irrigation method which results in high yields. On the other hand the UGA SSA recommendations worked very well with the VRI system and in both years it recommended an average of 25% less irrigation water than the Irrigator Pro.

The main goal of this study was assessing the technological and agronomic performances of a centre pivot Variable Rate Irrigation (VRI) system. The study was conducted in 2015 on a 16-ha field cultivated with maize. Irrigation was scheduled in three Management Zones according to data provided by a real-time monitoring system based on an array of soil moisture sensors. First results demonstrated the potential benefits of the VRI system on irrigation performance however a multiyear comparison is requested for evaluating the response to climate variability. VRI resulted in yields comparable to the business-as-usual regime but through a noticeable reduction in irrigation volumes.

Vineyards worldwide are subjected to spatial variability, which can be exhibited in both low and high yield areas. We developed a variable rate drip irrigation (VRDI) concept to reduce such variability. The VRDI system divides the vineyard into 30×30-meter irrigation zones, enabling individual irrigation of each zone according to normalized difference vegetation index (NDVI) maps. The first VDRI system was installed in on a 1.2-hectare vineyard Syrah red grape in Israel. With the VRDI system, the yield, leaf area index (LAI), canopy size, water potential, and primary juice chemical analysis results were very uniformed in comparison to previous years without the system.

A variable rate drip irrigation (VRDI) system was implemented in early 2013 in a 4.05-ha area inside a drip-irrigated Cabernet Sauvignon vineyard measuring 12.5 total ha. The VRDI area was split into 140 15×15-meter irrigation zones which were watered independently during three seasons with weekly schedules based on estimated actual ET. Irrigation was scheduled with the objective of decreasing spatial variability while maintaining high yields. Compared to an adjacent, 4.05-ha, conventionally drip irrigated section of the vineyard (CDI); VRDI increased yield and water use efficiency in all three years and decreased spatial dependency and structure in 2013 and 2015.

This research was carried out in an experimental vineyard grown in artificially transformed karst terrain (Croatia). The experimental design included four water treatments in three replicates: 1) fully watered or based on 100% evapotranspiration (ETc) application; 2) regulated deficit irrigation based on 75% and 50% ETc applications; and 3) non-watered. Hyperspectral images of grapevines were taken in the summer of 2016 using two spectral-radiance (W·sr−1·m−2) calibrated cameras, covering wavelengths from 409 to 988 nm and 950 to 2509 nm. The four treatments were grouped into two new sets: 1) drought (NO); and 2) watered (the remaining three treatments). The watered group was then split into two new sets: 1) 50% treatment; and 2) 75+100% treatment. The images were analyzed using Partial least squares – discriminant analysis (PLS-DA), and Single Vector Machines (SVM). The PLS-DA demonstrated the capability to determine levels of grapevine drought or watered groups with 70 - 80% accuracy. A similar success rate was achieved in distinguishing the 50% group from the 75+100% group.

A sensor fusion method was developed in order to track crop rows, considering various vegetation levels, for various crops. This application consisted to use a laser sensor, an inertial measurement unit and a color camera, in a fusion mode, to get a set of points corresponding to crop rows and eliminate noise like grass or leaves in environment, in real time. After applying a method such as Hough or Least Square (LS) technique for obtaining the geometric data of the crop line, automatic control operations were applied to realize the crop row tracking operation, with the desired lateral deviation parameter, taking into account the robot angular deviation and the temporal aspect, to realize the task with accuracy and without oscillations. The results showed the robustness of fusion method, to get a stable autonomous navigation for crop row tracking, particularly in the vineyards, with many perturbations such as bumps, hole and mud, and speeds between 1 and 2 m s-1. The mean lateral error between desired and obtained trajectory varied between 0.10 and 0.40 m, depending of speed and perturbations.

Knowledge on spatial variability of soil properties is useful for the rational use of inputs, as in the site specific application of lime and fertilizer. Crop-livestock-forest integrated systems (CLFIS) provide a strategy of sustainable agricultural production which integrates annual crops, trees and livestock activities on a same area and in the same season. Since the lime and fertilizer are key factors for the intensification of agricultural systems in acid-soil in the tropics, precision agriculture (PA) is the tool to improve the efficiency of use of these issues. The objective of this research was to map and evaluate the spatial variability of soil properties, liming and fertilizer need of a CLFIS. The field study was carried out in a 30 ha area at Embrapa Pecuária Sudeste in São Carlos, SP, Brazil. Soil samples were collected at 0–0.2 m depth, and each sample represented a paddock. The spatial variability of soil properties and site-specific liming and fertilizer needs were modeled using semi-variograms, the soil fertility information were modeled. Spatial variability soil properties and site specific liming and fertilizer need were modeled by kriging and inverse distance weighting (IDW) techniques. Another approach used was based on lime and fertilizer recommendation considering the paddocks as the minimum management unit. The results showed that geostatistics and GIS were useful tools for revealing soil spatial variability and supporting management strategies. Soil nutrients were used to classify the soil spatial distribution map and design site-specific lime and fertilizer application zones. Spatial analyses of crop needs and requirement can provide management tools for avoiding potential environmental problems, caused by unbalanced nutrient supplies.

The paper proposes a geostatistical framework to solve the issues of heterogeneous support for spatial estimation. Apparent soil electrical conductivity (ECa) was measured in a field cropped with San Marzano tomato using a multiple frequency electromagnetic profiler with 6 operating frequencies. Mixed support kriging was used to estimate ECa taking into account the change of support. The method includes punctual kriging with the error being the dispersion variance associated with each frequency. The individual ECa maps were weighted by the dispersion variance to obtain a map which was used for field partition in management zones.

Yield maps are a powerful tool with regard to managing upcoming crop productions but can contain a large amount of defective data that might result in misleading decisions. The objective of this work is to help improve and compare yield data filtering algorithms by generating simulated datasets as if they had been acquired directly in the field. Two stages were implemented during the simulation process (i) the creation of spatially correlated datasets and (ii) the addition of known yield sources of errors to these datasets. A previously published yield filtering algorithm was applied on these simulated datasets to demonstrate the applicability of the methodology. These simulated datasets allow results of yield data filtering methods to be compared and improved.

Harvest mechanization in sugarcane results in an intense vehicle traffic inside the crop areas. When using transshipment trailers, keeping them in the correct path is not simple. The aim of this study was to evaluate the error path of a set trailered with and without the use of an automatic steering system during sugarcane harvesting. We used a combination of a tractor and two transshipment trailers with three axles each. The results show that the errors of the transshipments are above the acceptable and the use of automatic steering on the tractor minimizes offset errors in the transshipments trajectory and the slope of the terrain is a factor that interferes with the displacement as a whole. Despite the use of automatic steering systems in the auxiliary tractor to minimize the errors suffered by transshipments, there is a need for active systems linked to these to be maintained in the correct route.

The probabilistic Rasch model is used to get objective measures of production potential at some locations of an olive orchard located in Badajoz, southwestern Spain. Nine soil properties (soil apparent electrical conductivity, clay, sand, and silt content, organic matter, total nitrogen, available phosphorous and potassium, and cation exchange capacity), taken at 40 locations in the field, were considered and, after being integrated in the model, a ranking of all locations according to the soil production potential and the influence on the production potential of each individual soil property were determined. Moreover, those soil samples or properties which had any anomaly where highlighted; this information can be necessary to conduct site-specific treatments, leading to a more cost-effective and sustainable field management. Additionally, estimates using geostatistical algorithms were utilised to map soil production potential and to delineate with a rational basis the management zones in the field.

Infield route planning is used to optimise field operations in order to decrease operational costs and environmental impacts. Route planners must be able to plan operations within real fields and account for real situations such as irregular shapes and obstacles. Therefore, a representative set of fields is required to robustly test the route planner. Instead of choosing randomly, which may result in a non-representative sample of the diversity of fields; a stratification strategy was used to separate the field dataset into strata. Proportional sampling from each stratum provided a representative sample of 217 fields, out of the original set of 603,218 from the Danish field database.

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.

This paper presents a framework for the implementation of a decision support system that considers spatial, temporal and managerial factors in assessing the potential impact of crop amendments on the cost of a given production scenario. The proposed system includes a database and a numeric simulation model. The database is linked to previously recorded crop responses for a given agricultural input under different conditions while the numeric simulation model determines the probability of different levels of profit for each decision option. This system then determines the optimal uniform rate of application of an amendment to maximize profits, or to define the range of such rates for a case of variable rate application. Uncertainty-based treatment of each model input allows for a balance between the potential results of under-application or over-application.

Potential yield is one of the criteria used as an input to nitrogen (N) fertilizer management decisions when using SIX EASY STEPS (6ES), the fertilizer recommendation tool used in the Australian sugar industry. Most commonly, 6ES is implemented using a district yield potential (DYP). In this study, we use analysis of sugar mill and yield monitor data from the Herbert River cane growing district to demonstrate that yield is markedly spatially variable, with this variability following the same patterns from year to year. There would therefore be value in a more location specific consideration of potential yield and application of 6ES. Similar analyses could be readily conducted in other sugar producing regions with potentially important implications for fertilizer use efficiency and the minimization of nutrient accessions to the Great Barrier Reef.

This research has multidisciplinary characteristics with a focus on cotton fiber production and computational solutions to improved data exchange. The research is divided into three parts, the identification of the cotton fiber production processes, the formal ontology for identifying the data classes, and finally the proposal of a specific metadata standard for cotton fiber production. The absence of a specific standard for this segment favors the heterogeneity in the various data sources. The contribution of the research lies in improving the information exchange used in agricultural systems providing identification of each individual responsible for steps in the cotton production chain.

Aflatoxin contamination of food can cause liver cancer in humans and animals. Identification of aflatoxin risk areas allows farmers to adapt management strategies before planting, during growth and at harvest. Aflatoxin contamination is driven by high temperatures and drought conditions and crops grown on light textured soil in the south eastern USA are at particular risk. Aflatoxin assessment is expensive so a role of extension services in precision farming is to identify the areas most at risk of contamination so that farmers can adapt irrigation or planting strategies. This paper extends a county-level risk factors approach developed by Kerry et al. (2017) by investigating the use of NDVI and thermal IR data to indicate drought stress and thus aflatoxin contamination risk at the sub-county level.

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.

This paper is focusing on how to eliminate the required time for importing all the necessary data into a farm management information system (FMIS). This process was automated by using ISO 11783 and SAE J1939 communication information from the tractor’s CAN-Bus. Using a data logger and a machine to machine (M2M) gateway inside the tractor’s cabin, CAN-Bus data were recorded and transmitted to the cloud-based server of the FMIS. There, a script was responsible for parsing and aggregating the raw machine data into specific agricultural tasks and then importing them into the FMIS. The operator could choose the type of the performed task by a number of switches connected with the digital inputs of the data logger.

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.