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.

For most producers, unmanned aerial vehicles (UAV) are a novelty that has been little employed in their agricultural operations. An UAV will not fix every problem on the farm, but there are some practical applications for which UAVs have demonstrated value. Three examples of how UAVs have been used in weed science applications are presented here; the methods are transferable to other agricultural commodities with similar characteristics. The first of these is quantification of the extent and severity of non-target herbicide injury. The second application is calculation of spray thresholds based on weed populations. The third application is development of site-specific herbicide treatment.

This article examines how the concerns of economic efficiency and of redistribution can be integrated into the design of the policies that aim to control the nitrogen flows. Generally speaking, the economists recommend to determine the degree of pollution and to arbitrate between the gains and the transaction costs associated to the implementation of the policy. Regarding instruments, the reduction of the diffuse nitrogen pollution needs a quantitative or economic instrument owing to the difficulty of valuing the environmental efforts of the producers on markets. Unlike for pesticides, the quota is here preferred to the tax, in particular in the case of fertilisers. The polluter-payer principle should be applied to the risks of accidental pollution on livestock farms. Results of a long process under the European Commission pressure, the policies of EU members in Northern Europe are more in line with the economic prescriptions than those of France. Recent evolutions go nevertheless forward, even if penalties for enforcing the practices standards are always missing.

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.

A ground-based hyperspectral imaging system covering the spectral range of 384–1034 nm was used for Sclerotinia Stem Rot (SSR) detection. Two sample sets of oilseed leaves were collected. Four vegetation indices were extracted and evaluated by analysis of variance (ANOVA) combined with linear discriminant analysis (LDA) for the two sample sets. Discriminant models were built using the 4 vegetation indices. The discriminant results of the two sample sets were good with classification accuracies of the calibration set and the prediction set over 85%. The overall results indicated that vegetation indices calculated from ground-based hyperspectral imaging could be used as reliable and accurate indices for SSR detection.

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.

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).

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.