This paper proposes a simple method for categorizing fields on a regional level, with respect to intra-field variations. It aims to identify fields where the potential benefits of applying precision agricultural practices are highest from an economic and environmental perspective. The categorization is based on vegetation indices derived from Sentinel-2 satellite imagery. A case study on 7678 winter wheat fields is presented, which employs open data and open source software to analyze the satellite imagery. Furthermore, the method can be automated to deliver categorizations at every update of satellite imagery, hence coupling the geospatial data analysis to direct improvements for the farmers, contractors, and consultants.

Producers are often faced with information from industry touting a generic return on investment of precision agriculture technologies that are inflated through the use of simple techniques that ignore the time value of money. The economics of precision technologies are as site-specific as the technology. Therefore, educating producers (and agribusiness) on how to determine the return on investment by following the correct investment analysis techniques within a proper decision-making framework helps ensure an accurate site-specific return on investment for precision agriculture technologies.

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.

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.

A method able to overcome multiple scattering effects in close range hyperspectral imagery of vegetation scenes is presented. It has been developed using canopy and light propagation simulation tools and evaluated on real crop plants in the context of nitrogen content assessment.

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.

New remote sensing technologies have provided unprecedented results in vineyard monitoring. The aim of this work was to evaluate different sources of images and processing methodologies to describe spatial variability of spectral-based and canopy-based vegetation indices within a vineyard, and their relationship with productive and qualitative vine parameters. Comparison between image-derived indices from Sentinel 2 NDVI, unfiltered and filtered UAV NDVI and with agronomic features have been performed. UAV images allow calculating new non-spectral indices based on canopy architecture that provide additional and useful information to the growers with regards to within-vineyard management zone delineation.

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 integration of conservation agriculture with the benefits of precision farming represents an innovative feature aimed to achieve better economic and environmental sustainability. The synergy between these principles was assessed through a technical feasibility and energy efficiency to define the best approach depending on different agricultural systems, spatial and temporal field variability. The study compares three conservation tillage techniques supported by precision farming with conventional tillage in a specific crop rotation: wheat, rapeseed, corn and soybean. The preliminary results show a positive response of precision farming in all the conservation tillage systems, increasing yields until 22%. The energy efficiency achieves highest level in those techniques supported by precision farming, gaining peak of 9% compared to conventional tillage.

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 human population is expected to reach 9 billion by 2050 and thus high yield crop varieties need to be developed. Remote sensing can estimate crop parameters non-destructively and quickly. The aim of this study was to compare and evaluate the use of a commercial RGB camera with an expensive canopy sensor in the crop development of two legumes. The RGB camera based vegetation index (NGRDI) was compared with the canopy sensor derived vegetation indices (NDVI and NDRE) for estimating legume crop growth parameters. The results indicated that the use of a simple digital camera RGB can in some cases replace spectral canopy sensors.

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.

The objective of this study was to evaluate the performance of a Crop Circle sensor-based precision nitrogen (N) management (PNM) strategy in different winter wheat cropping systems under on-farm conditions in North China Plain (NCP). Four farmer’s fields were selected for on-farm experiments in Laoling County, Shandong Province of NCP in 2015-2016. In each field, the PNM strategy was evaluated in two winter wheat cropping systems: farmer’s conventional management (FCM) and regional optimum crop management (ROCM). In each cropping system, there were two N management strategies: 1) FCM or ROCM; 2) PNM. The results indicated that the PNM strategy significantly increased partial factor productivity (PFP) by 29% in the FCM system, but did not have any significant improvement in the ROCM system. The ROCM system, using either regional optimum N management or PNM, significantly increased both grain yield and PFP than the FCM system.

Crop growth models including CERES-Maize and CROPGRO-Soybean have been used in the past to evaluate causes of spatial yield variability and to evaluate economic consequences of variable rate prescriptions. However, these modelling techniques have not been widely used because of an absence of user-friendly software. In this work, a nitrogen prescription model to simulate the consequences of different nitrogen prescriptions using the DSSAT crop growth models is developed. The objective is to describe a site-specific nitrogen prescription and economic optimizer program developed for computing optimum spatial nitrogen rates for maize using the CERES-Maize model. The application of the model is demonstrated on two different fields in Germany and the US. The program simulated optimum N applications that averaged 42% (McGarvey field, US) and 39% (Riech field, Germany) lower than the uniform rates actually applied in the fields. The software is written in Python and will ultimately be distributed in the public domain as a plug-in to the QGIS software.

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.

A whole farm economic analysis was performed to maximize net returns utilizing variable maturity groups of corn and soybeans over different soil types. Demand for drying and storage equipment throughout harvest was generated based on profit-maximizing combinations of grain types, their respective maturity groups, and yield potential over different topsoil depths. Two marketing strategies were considered: cash and futures contract sales. It was found that drying equipment became a limiting factor in the proposed system. This prevented storage facilities from reaching full capacity and additional grain from capturing value in the futures market.

Disease detection and control is thus one of the main objectives of vineyard research in France. Monitoring diseases manually is fastidious and time consuming, so current research aims to develop an automatic detection of vineyard diseases. This project explored the use of a high-resolution multi-spectral camera embedded on a UAV (Unmanned Aerial Vehicle) to identify the infected zones in a field. In-field spectrometry studies were performed to identify the best spectral bands for the sensor design. The best models were found to be the function of the grapevine variety considered and the 520-600-650-690-730-750-800 nm bands were found to be the most efficient for all types of grapevines, with an overall classification accuracy of more than 94%.

Advances in agricultural machinery, information and sensor technology have led to an increasing amount of data that is available spatially both pre and within season. The case is compelling for the spatialisation of existing, non-spatial (field-scale) crop models that can accommodate this ‘big data’ and lead to more precise predictions of yield and quality and an improved field management. This study explores the conceptual spatial models based on the potato crop models that simulate crop physical and physiological processes and predict yields and graded yields at a field-scale. Through exploring the possible spatial scales and model application approaches considering spatial variation an optimal and more effective solution is expected. Issues concerning model quality and uncertainty are also discussed.