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 paper is focusing on how to eliminate the required time for importing all the necessary data into a farm management information system (FMIS). This process was automated by using ISO 11783 and SAE J1939 communication information from the tractor’s CAN-Bus. Using a data logger and a machine to machine (M2M) gateway inside the tractor’s cabin, CAN-Bus data were recorded and transmitted to the cloud-based server of the FMIS. There, a script was responsible for parsing and aggregating the raw machine data into specific agricultural tasks and then importing them into the FMIS. The operator could choose the type of the performed task by a number of switches connected with the digital inputs of the data logger.

Precise applying of PPP (Plant Protection Products) in orchards and vineyards requires new kinds of sprayer technologies and new methods of sensor data evaluation. In this paper a selective electrical driven sprayer, carried by the autonomous robotic platform elWObot, is introduced. A 3D-Simulation environment and the framework ROS (Robot Operating System) helps developing and testing the interaction between the sprayer and the robot. The calculated leaf wall area (LWA) and the distance from the sprayer to the leaves in the spray region, control the flow-rate and the air-assist of eight adjustable sprayers individually. First field trials showed that the adaption of the software from the simulation to the hardware worked as expected.

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 performance of early-maturing breed sired suckler bulls finished at pasture, with or without concentrate supplementation, at 15 or 19 months of age was evaluated. In total, 60 Aberdeen Angus-sired bulls were assigned to a two (slaughter age (SA): 15 (S15) or 19 (S19) months)×two (finishing strategies (FS): grass only or grass+barley-based concentrate) factorial arrangement. There were no (P>0.05) SA×FS interactions. Increasing SA increased carcass weight (265 v. 355 kg), kill-out proportion (542 v. 561 g/kg), conformation (6.7 v. 8.3, 1 to 15) (P<0.001) and fat (5.8 v. 6.8) scores (P<0.01), and resulted in yellower subcutaneous fat (‘b’ value, 6.6 v. 8.3) and darker muscle (‘L’ value, 30.0 v. 28.3) (P<0.01). Supplementation reduced estimated herbage intake by 0.60 and 0.47 kg dry matter (DM)/kg DM of concentrates for S15 and S19, respectively. Supplementation increased carcass weight (+6.7%, P<0.001) and kill-out proportion (+1.8%, P=0.06) but had no effect on carcass fat and conformation scores or fat and muscle colour. In conclusion, carcasses were adequately finished, with or without concentrates for S19, but not for S15. Supplementation had no effect, and age had relatively minor effects, on fat and muscle colour.

Nitrogen fertilization of silage maize in Central Italy is typically carried out with two applications at early stages of crop development: 2nd (V2) and 6th (V6) leaf respectively. In such conditions, the crop has not yet fully covered the soil and proximal or remote sensing of the canopy is hindered by the strong soil background signal. There is thus great interest in rapid and inexpensive approaches to N fertilization prescription. Therefore, an indirect method for inferring information on yield potential and soil variability, through a field-based clustering of multi-temporal satellite data, has been developed using archive Landsat images to identify temporally constant patterns. This method is potentially useful for the creation of prescription maps. The usefulness of the method was evaluated during an N fertilisation field trial in Maccarese (Central Italy), in 2016. At the V2 stage, both uniform and variable rate applications were performed and compared. A pseudo-cross variogram and a standardized ordinary co-kriging methodology was used to highlight spatially variable significant differences among the treatments.

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.

This study aimed to determine estimates of production efficiency among primiparous suckler cows of diverse genetic merit (GM) for the national Irish maternal index. Data from 82 heifers of diverse GM across two different replacement strategies (suckler (S) or dairy (D) sourced) were available. Milk yield, grass dry matter intake, cow BW and body condition score (BCS) were recorded. The maternal index had no significant effect on any parameters investigated, whereas S cows were 86 kg heavier in BW, had a 0.33 greater BCS and 0.6 UFL greater NEM requirement compared with D beef crossbred (F1) cows. The F1 produced 2.2 kg/day more milk and had greater lactation energy requirements (0.8 UFL) than S. The F1 produced 0.48 kg milk per 100 kg BW and 0.15 kg more milk per unit intake. An interaction between GM and cow origin (CO) showed that F1 low merit cows consumed an additional 1 kg DM than F1 high, and that S high merit cows produced 1.5 kg less milk than their S low counterparts resulting in a 0.5 UFL greater energy requirement for milk production for S low. The F1 high merit cows produced 0.12 kg more milk per unit intake than F1 low cows, whereas S low cows produced 0.12 kg less milk per unit intake than S high. The F1 low cows consumed 0.17 kg more DM per unit BW than F1 high. Thus, genetic selection for maternal traits has not resulted in differences in production efficiency traits, however CO differences were observed.

Exposure of livestock to high heat loads negatively affects animal’s productivity. Genetic selection for heat tolerance using quantitative approaches has been developed by making use of the milk recording information merged with the meteorological information in the test date. The main conclusions of the studies following this approach have been that there is genetic variability in the response to heat stress (HS), that a genotype by environment interaction between thermal comfort and HS conditions exists and that there is a genetic antagonism between HS tolerance and high milk production. This approach has the advantage of adding no extra costs to the existing milk recording schemes, but it has some disadvantages. Current milk recording information does not seem to fully capture the productive response to high heat loads. Moreover, the antagonism between heat tolerance and high milk production may offset the benefits of selection for this trait if heat-tolerant animals are less productive. The use of new tools from phenomics, genomics and transcriptomics can help in achieving an accurate selection of heat-tolerant animals without damaging progress in milk production. New phenotypes for this selection are measure of body temperature and respiration rates together with measures of indicators of heat tolerance in milk, through the use of mid IR spectroscopy. The use of commercial DNA chips to perform genome wide association studies or comparison of whole genome DNA sequence of animals of heat adapted and temperate climate breeds could also provide useful genomic information. Finally, RNA sequencing together with the recent discovery of the possibility to use the milk transcriptome instead of tissue biopsies could help to discover differentially expressed genes under HS and thermoneutrality. The existence of powerful tools to achieve better phenotypes to identify tolerant v. susceptible animals and to gain insights about the genetic mechanisms underlying HS response to be able to disentangle the genetic puzzle of heat tolerance may make possible the selection of heat-tolerant and still productive animals.

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.

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.

A range of precision farming technologies are used commercially for variable rate applications of nitrogen (N) for cereals, yet these usually adjust N rates from a pre-set value, rather than predicting economically optimal N requirements on an absolute basis. This paper reports chessboard experiments set up to examine variation in N requirements, and to develop and test systems for its prediction, and to assess its predictability. Results showed very substantial variability in fertiliser N requirements within fields, typically >150 kg ha−1, and large variation in optimal yields, typically >2 t ha−1. Despite this, calculated increases in yield and gross margin with N requirements perfectly matched across fields were surprisingly modest (compared to the uniform average rate). Implications are discussed, including the causes of the large remaining variation in grain yield, after N limitations were removed.

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.

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.

The suitability of milk for specific dairy products might be improved by changing milk fat or milk protein composition. In the RobustMilk project, we showed that milk fat composition is determined by genetic factors. In addition, recent studies indicate that milk protein composition is strongly affected by genetic factors. This suggests that there are opportunities to change milk composition by means of selective breeding. Traditional selection is based on large-scale phenotyping and not all analytical methods are suited for this purpose. The RobustMilk project team has shown that several fatty acids can be predicted on the basis of IR spectra. Accuracy of predicting individual milk proteins based on IR spectra is low. In addition to phenotypic records, selection might be based on genotypic information. DGAT1 and SCD1 genotypes are strongly associated with fat composition. β-Lactoglobulin, β-casein and κ-casein protein variants are strongly associated with protein composition. We conclude that tools are now available for changing detailed milk fat or milk protein composition by means of selective breeding.

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.