The objective of this study was to evaluate the potential of using Multiplex 3, a hand-held canopy fluorescence sensor, to determine rice nitrogen (N) status at different growth stages. In 2013, a paddy rice field experiment with five N fertilizer treatments and two varieties was conducted in Northeast China. Field samples and fluorescence data were collected simultaneously at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. Four N status indicators, leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU) and N nutrition index (NNI), were determined. The preliminary results indicated that different N application rates significantly affected most of the fluorescence variables, especially the simple fluorescence ratios (SFR_G, SFR_R), flavonoid (FLAV), and N balance indices (NBI_G, NBI_R). These variables were highly correlated with N status indicators. More studies are needed to further evaluate the accuracy of rice N status diagnosis using fluorescence sensing at different growth stages.

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 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.

This work was to evaluate the differences of soil and plant analysis development (SPAD) and normalized difference vegetation index (NDVI) readings and their relationship with leaf nitrogen accumulation (LNA). The study explored new indices to diagnose nitrogen (N) status. These indices were obtained by multiplying SPAD readings and leaf area index (LAI). Linear regression relationships between Chlorophyll values and N indicators showed the SPAD readings (Chl: LNA=0.0546×Chl-0.479, R2=0.94***, P<0.001). The projected results suggested that Chl values could play an important role for improving N status diagnosis from stem elongation to heading stages in paddy rice.

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.

APOLLO, a newly funded H2020 EU project will develop an agricultural advisory platform for small farmers based on Copernicus Sentinel satellites. It will provide services for tillage scheduling, irrigation scheduling, crop growth monitoring and yield estimation. The aim of this study was to identify the farmers’ requirements of the APOLLO platform. In total 121 farmers were interviewed in Spain, Serbia and Greece. More than 90% of the farmers pointed out that smart agriculture and use of satellite data in agriculture are important. Additionally, more than 80% want to have access to historical data and a flexible subscription policy to the platform according to their needs and use. However, significant differences exist among farmers of these countries in terms of technology awareness and penetration, which should be taken into consideration for developing a successful platform.

Remote sensing images offer a unique monitoring capacity for agriculture fields. Despite significant drawbacks, free historical remote sensing images are likely to provide valuable information to delineate temporally stable zones, especially for a perennial crop like vines. In order to test the potential of these free images for delineating within field soil zones, an experiment was performed on three vine fields located in southern France. On each vine field, the grower was asked to delineate within field zones that present differences in soil characteristics. The first expert zoning (ZP zoning) was only based on the grower’s knowledge. Two weeks after, 12 visible free remote sensing were provided to the grower to refine the first zoning (ZAI zoning). In order to assess the relevancy of both zonings, a soil apparent electrical conductivity (ECa) survey was performed on each field. The Rv zoning index was then computed on ECa data to assess the potential improvement between ZP and ZAI zonings. Results show that for two fields, remote sensing images improved the zoning while for one field both ZP and ZAI expert zonings were similar. This result highlights a potential interest in historical remote sensing images to improve the knowledge that growers have of their fields. It should be noted, however, that this improvement is still limited in the case of viticulture where the small size of the fields with a large number of manual operations is likely to lead to a good knowledge of within field variability.

This work assesses the potential of Sentinel-2A images in precision agriculture for Barley production in a case study. Two workflows are proposed: 1) images were acquired with a relatively simple methodology to follow the crop development; 2) two images around harvest time were downloaded and processed using a more complex and accurate methodology to calculate four vegetation indices (NDVI, WDRVI, GRVI and GNDVI) to be correlated to yield with linear regression models. Yield data were acquired with a yield monitor installed in a combine harvester. Green-based vegetation indices performed slightly better. However, the highest correlation coefficient was 0.48. Better results may be achieved with earlier imagery and other vegetation indices. Sentinel-2 is a promising tool for precision agriculture in large arable crop fields.

Precision farming advances are providing opportunities in both production agriculture and agricultural research. For growers and agronomists, the benefits of identifying where crops are stressed, the location of weeds and estimating yields on a large scale are clear. Researchers, who have different needs, can benefit from a detailed focus on a specific characteristic, such as one disease (e.g. yellow rust). This paper will review how recent advances in technology are beginning to allow the development of specialised tools within research and agriculture and how current precision agriculture tools can be effective at measuring desirable traits.

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.

In this paper, novel geospatial services are presented which are able to process on the server-side numerous remote sensing data based on big data frameworks like Hadoop and Rasdaman. The developed system itself features several software modules that orchestrate the different image processing algorithms responsible for the production of consistent value-added maps like canopy greenness and leaf area index. Through distributed multitemporal analysis, the entire crop growth cycle can be continuously monitored through the analysis of time-series observations. These observations cover multiple crop growth cycles, offering invaluable information by linking weather statistical data with the start, the end and the duration of each growth cycle enabling critical decisions by direct comparison with the current crop growth state.

The project “FArming Tools for external nutrient Inputs and water Management” (FATIMA, H2020-SFS2) is developing satellite-based methodologies and information to support effective and efficient water and nitrogen input recommendations in agricultural production. This paper focuses on nitrogen recommendation for winter cereals in Austria and presents preliminary findings from the 2015/16 crop growing season. The Nitrogen Nutrition Index was applied using an empirical relationship to derive dry mass from Leaf Area Index (LAI) and %Na from a chlorophyll index. Results showed a very high correlation between LAI and above ground dry mass (R2=0.95) but a lower correlation between the chlorophyll index and %Na (R2=0.24). Despite various indices tested, the relationship to estimate %Na remains weak. Additional field data and research are needed to further study this aspect.

There is growing interest in monitoring soil biological health to complement the traditional evaluation of soil physical and chemical characteristics in agricultural fields. Activity of soil microorganisms mediates many essential soil processes that affect fertility, and, therefore, essential to the successful adoption of precision agriculture. However, there are technical limitations to cost-effective monitoring of spatial and temporal dynamics of soil biological activity across agricultural landscapes. This paper summarizes three consecutive studies on in situ measurement of soil biological activity. The first study reveals spatial heterogeneity of microbial population growth in three agricultural fields using bio-films. In the second study, microbiological activity was analyzed using a substrate-induced respiration technique. This technique was evaluated through a series of soil toxicity experiments that involved a comparison of fresh and autoclaved soil samples. Finally, the aim of the third study was to develop a portable instrumented system to evaluate carbon dioxide concentrations in soil by extracting air stored within the soil pores. This instrument was tested under various conditions to quantify the effects of soil moisture, compaction and presence of glucose (artificially increased microbial respiration). Optimization of the discussed techniques will allow for detailed mapping of these indices of soil biological health and their interactions with the physical and chemical environment at any specific point in time.

Hill country farms play a critical role in the New Zealand’s economy and precision agriculture solutions have been increasingly utilised to improve their profitability and resilience. Soil moisture, a spatially and temporally variable factor in pasture productivity, was monitored using Wireless Sensor Networks (WSN) technology deployed over a hill country farm in the North Island. Geographical Information System assisted spatial methodologies were applied to design a WSN-topography. The integration of spatially distributed sensors and multi-depth soil moisture measurements from various hillslope positions showed that root zone soil moisture was more homogenous as the mean soil moisture increased. Considerable differences were found in soil water profile response to significant rainfall events on steep, rolling and flat surfaces allowing us to construct a clearer picture of the topographical controls on the variability of soil moisture.

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 multi-sensor approach for terrain estimation is proposed using a combination of complementary optical sensors that cover the visible (VIS), near infrared (NIR) and infrared (IR) spectrum. The sensor suite includes a stereovision sensor, a VIS-NIR camera and a thermal camera, and it is intended to be mounted on board an agricultural vehicle, pointing downward to scan the portion of the terrain ahead. A method to integrate the different sensor data and create a multi-modal dense 3D terrain map is presented. The stereovision input is used to generate 3D point clouds that incorporate RGB-D information, whereas the VIS-NIR camera and the thermal sensor are employed to extract respectively spectral signatures and temperature information, to characterize the nature of the observed surfaces. Experimental tests carried out by an off-road vehicle are presented, showing the feasibility of the proposed approach.

The publicly available Digital Soil Map of Sweden (DSMS) contains topsoil clay content information in a 50 m×50 m grid, and can be used as decision support in precision agriculture. However, it is also common that farmers have undertaken their own soil sampling (one sample per hectare with texture analysed in every third sample). In the present study, such soil samples from 403 farms were used to validate topsoil clay content information derived from 1) DSMS, 2) DSMS locally adapted by residual kriging, 3) DSMS locally adapted by regression kriging and 4) inverse distance weighting interpolation of the soil sample data without using DSMS. The latter has been common practice until now. The best method differed depending on the local accuracy of DSMS, the quality of the soil sampling and the spatial variation structure of the topsoil texture. The ‘Best method’ strategy, which meant to apply all the above methods and choose the one that performed best at each farm, significantly reduced the mean absolute error. We recommend using this strategy to locally adapt regional digital soil maps to derive accurate decision support for use in precision agriculture.

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.

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.

Primary tillage is in many cases crucial for successful crop establishment and weed and pest control. Inversion tillage using a mouldboard plough may be required when a uniform ploughing operation covering the entire field is preferred. The ploughing operation is especially challenging at the interface area between headlands and the main cropping area. Overlapping at the interface causes a mixing of the topsoil, rather than a soil inversion, and poor burial of residues and weeds, especially of concern in organic farming. The aim of the research was to study novel plough section control designs to optimise the interface area. Concept designs with hydraulic control were studied and the preferred was developed and tested in real field operations. The research concluded that the concept was functional and by visual inspection the interface was optimised. In addition, the section control can improve operations in irregularly shaped fields.