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.

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.