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.

This paper presents schematic review for the smart agricultural model in Japan using data-on-demand information exchange based on smart agricultural machinery systems (SAMS). Four machines were developed in this study, namely Smart rice trans-planter with on-the-go soil sensor; Smart 2nd fertilizer applicator based on CropSpecTM; Yield monitor combine harvester with on-the-go lodging analysis system; and Farm Activity Record Management System (FARMS). The study obtained 450,000 datasets of topsoil accompanied by 65,000 datasets of crop status and 1 million images of lodging information from 50 ha of rice fields, taken in 2016. The results conclude that the field mapping using FARMS was available not only for manager’s decision on fertilizer application, but also for information sharing between employees. A two year feasibility study showed improvement of 20% fertilizer reduction and 30% harvest efficiency than conventional management. The study suggests that SAMS would play an important role for technology succession in the near future.

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.

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.

In Spain, the use of annual cover crops is a crop management practice for irrigated vineyards that allows controlling vineyard vigor and yield, which also leads to improve the crop quality. Recently, Cynodon dactylon (bermudagrass) has been reported to infest those cover crops and colonize the grapevine rows, resulting in significant yield and economic losses due to the competition for water and nutrients. From timely unmanned aerial vehicle (UAV) imagery, the objective of this research was to map C. dactylon patches in order to provide an optimized site-specific weed management. A quadrocopter UAV equipped with a point-and-shoot camera was used to collect a set of aerial red-green-blue (RGB) images over a commercial vineyard plot, coinciding with the dormant period of C. dactylon (February 2016). Object-based image analysis (OBIA) techniques were used to develop an innovative algorithm for early discrimination and mapping of C. dactylon, which had the ability to solve the limitation of spectral similarity of this weed with cover crops or bare soil. As a general result, the classified maps of the studied vineyard showed four main classes, i.e. vine, cover crop, C. dactylon and bare soil, with 85% overall accuracy. These weed maps allow developing new strategies for site-specific control of C. dactylon populations in the context of precision viticulture.

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.

A field study was carried out to investigate the seasonal variations in alfalfa (Medicago sativa L.) water use efficiency (WUE) using Eddy Covariance (EC) measured CO2 and H2O fluxes, aiming at optimizing the use of irrigation water under hyper arid conditions. The EC system used for this study was installed on a center pivot-irrigated 50 ha alfalfa field. Results revealed that the net EC estimated CO2 uptake ranged from 65,00 kg ha−1 (in winter) to 21,500 kg ha−1 (in summer). While, H2O flux was 4,147 m3 ha−1 (in winter) and 20,157 m3 ha−1 (in summer). This resulted in an estimated alfalfa WUE of 1.57 and 1.07 kg m−3 for winter and summer seasons, respectively. However, the actual WUE of harvested alfalfa was calculated at 0.70 and 0.71 kg m−3 for winter and summer, respectively. Therefore, attaining an actual crop WUE of 33–55% lower than the EC measurement (i.e. more water losses were due to leaching and deep-percolation processes, as the EC system could only estimate evapotranspiration over agricultural fields) emphasizes the need of precision irrigation practices, which will enable farmers to apply irrigation water and agrochemicals more precisely and site-specifically to match soil and plant status and needs.

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.

Yield and landscape are commonly used to guide management zone delineation. However, production system choice and management can interact with landscape attributes and weather. The objective of this study was to evaluate forage yield and soil properties in three landscape defined (elevation based) management zones, and under two different grazing systems. Changes in soil properties (soil strength, bulk density, moisture, bioavailable nutrients) and forage productivity (biomass), as related to grazing management and management zone, were measured. Bulk density, moisture, and forage biomass were greater at higher elevation. Soil strength decreased as elevation increased, and was greater near-surface after winter grazing ended. The response of landscape delineated management zones varied with extreme weather conditions and treatment. Lower zones were more sensitive to weather extremes than higher elevations, directly affecting biomass accumulation. In conclusion, we observed interactions between the grazing treatments and the management zones.

An experiment was carried out to assess the performance of an automatic irrigation system based on remotely-sensed soil moisture measurements in blueberries grown in sandy soil under plastic tunnels. The system uses the information provided by soil moisture sensors installed at three soil depths (10, 20 and 30 cm) and user-defined irrigation set-points to trigger automatic irrigation events. An Optimized irrigation treatment in which irrigation set-points and duration of irrigation have been determined from soil hydraulic properties analysis and soil water modelling is compared to the irrigation scheduling criteria established by the grower (Control treatment). The results showed that the Optimized treatment applied 30% of the water applied by the Control treatment, led to mild to moderate crop water stress conditions and had no impact on berry yield and quality.

The WEED-PAM® is a chlorophyll fluorescence sensor. It has already been applied in the detection of herbicide resistant Alopecurus mysuroides populations with promising results. Yet more work needs to be done in order to validate the system’s capabilities in different species. In this study, field experiments were conducted at three sites to clarify the capability of this sensor to detect herbicide resistant Apera spica-venti populations. The plants were treated with five different herbicides: three ALS-, one ACCase- and one PS II- inhibitor. Five days after the herbicide treatment, sensor data were gathered. These data were compared with a visual assessment, performed 21 days after the herbicide application. The populations that exhibited a strong resistance to the ALS and PS II inhibitors could be differentiated from the sensitive ones. Yet the Apera spica-venti population with a low resistance level to the ACCase inhibitors cannot be differentiated from the sensitive population.

Peanut (Arachis Hypogea) is one of the few major agronomic crops for which a yield monitor is not commercially available. This paper describes an ongoing project whose long-term goal is to adapt an optical sensor originally developed for cotton yield monitoring for use as a peanut yield monitor (PYM). The immediate objective of the work reported here was to evaluate the PYM under harvest conditions typical in southern Georgia, USA. The PYM consists of two mass-flow sensors, a data acquisition system, and a DGPS receiver. The PYM was evaluated on three fields totaling 29 ha during the 2016 harvest season. Percent error between the scale load and calculated load was 2% or better for the first field tested, but increased greatly for subsequent fields that were tested, most likely caused by damage to the sensor lens from the impact of pebbles.

This work proposes a methodology that uses remote sensing (RS) images to obtain optimum nitrogen crop uptake (Nuptake) maps, for the all pixels in the image included in the field during the entire growing season. The Nuptake was determined from relationship between critical nitrogen concentration (Nc) and biomass where biomass was estimated by a crop growth model based on the water use efficiency. The paper proposes the use of this methodology in commercial wheat farm. The results are discussed with respect to field measurements of crop biomass and N concentration on different dates and in zones with different nitrogen treatments from 8 commercial wheat farms in Albacete, Spain during 2015 and 2016.

In precision agriculture, the selection and use of appropriate sensors determine the type and quality of information that will feed decision-support models. A wide variety of sensors, spectral ranges, data collection and processing approaches are used, sometimes leading to confusion. Whether in transmission or reflectance mode, multispectral or hyperspectral, laboratory or field-based or even satellite-borne, in order to achieve meaningful and accurate measurements it is essential to have a clear understanding of which part of the electromagnetic spectrum the sensors relate to and how the corresponding radiation interacts with the substrate (e.g. soils, crops, livestock products). Sensors in the visible range (390-700 nm) use colour to identify certain properties of the substrate (e.g. chlorophyll and pigments in crops, organic matter contents in soil) and can be used to detect and quantify colour changes that could, in turn, be correlated with changes in those properties. Alternatively, radiation in the near (NIR, 750-2500 nm) and mid infrared (MIR, 2500-25 000 nm) interacts with the molecular bonds that constitute organic and inorganic matter and, therefore, sensors with detectors in these ranges provide different but interrelated information on the chemical composition of the substrate. Shorter wavelength radiation in the form of X-Rays (0.1-10 nm) induces fluorescence in the substrate and XRF sensors provide elemental atomic information that is highly applicable to the study of soils, sediments and fluids. At the James Hutton Institute, we have expertise in the use of all these types of sensors and are developing practical applications based on a thorough understanding of the processes involved. In this paper we provide an overview of the capabilities and applications of the different sensors used in precision agriculture, not only with a theoretical understanding, but also with an awareness of the practicalities involved.

Yield mapping techniques have only recently started to be implemented by the Californian wine grape industry, but the advancement has necessitated new processing methods for large vineyards. The process for mapping large blocks harvested with multiple machines has only recently occurred and implies that their yield monitors have to be calibrated and corrected to the same scale. Here we discuss two methods for processing yield maps at the commercial level. Method 1 depends on many calibrations with delivered fruit weight to a winery. Method 2 normalizes raw files automatically can reduce total processing time by up to 90%.

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.

A view into future requires maybe a new approach of current arable production systems. So far crop production systems have been adapted to the machinery available on the market. New technological progresses in the range of precision and digital farming show the possibilities meeting the requirements of plants even more small-spatial and efficiently. Especially small autonomous machinery may offer opportunities achieving sustainable intensification in crop farming. Different crop productions and cultivation systems have to be devised, analyzed and assessed in this context.

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.

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.