This paper focuses on maturity evaluation derived by a color camera for a sweet pepper robotic harvester. Different color and morphological features for sweet pepper maturity were evaluated. Side view and bottom view of sweet paper were analyzed and compared for their ability to classify into 4 maturity classes. The goal of this study was to differentiate between the two center classes which are difficult to separate. Statistical analysis of 13 different features in reliance to the maturity classification and the views indicated the best features for classification. The results show that the features that can be used for classification between the two central classes from both bottom and side views are: Hue range, Equal2Real – the ratio between the equivalent equal sized circle perimeter to the shape perimeter and Area2Peri – the ratio between the area to the perimeter.

The major histocompatibility complex region has been implicated in explaining some of the variation observed in adaptability and tick susceptibility of cattle. The bovine leukocyte antigen region of 192 cattle representing indigenous, composite and exotic breeds used in commercial beef production in Namibia and South Africa was investigated using four microsatellite markers. Ticks counted under the tail were taken as an indicator of tick susceptibility. Tick scores of all but one population was low (11 to 20 ticks), with only the South African Bonsmara population having an average score of 31 to 40 ticks per animal. The observed variation based on four microsatellite markers ranged from 5.5 alleles in Namibian Afrikaner to 7.7 alleles in South African Nguni and Bonsmara cattle. Unbiased heterozygosity values ranged from 0.66 (Namibian Afrikaner) to 0.76 (South African Bonsmara). Structure analyses grouped the five populations into three indistinct clusters with limited genetic variation between the populations.

In cattle, the chemical composition of the carcass is usually evaluated from one of three reference methods (rib dissection, specific gravity or a combination of easily obtained measures) or is estimated from proxy traits (USDA yield grade (YG), subcutaneous fat thickness (SFT)). Objectives were to evaluate if the relationships between the chemical composition of the carcass and each of the proxy traits (YG, SFT) differed depending on the reference method. The study was conducted by meta-analysis from published results using 25 publications that reported carcass composition and proxy traits (53%, 31% and 16% of the data were based on rib dissection, specific gravity and combination of easily obtained measures, respectively). Results showed that the amounts of carcass fat or protein that can be predicted from a given proxy trait (YG or SFT) differ significantly with the reference method used to determine carcass fat or protein.

Potassium (K) is a macronutrient which plays a vital role on crop growth and metabolism. After N the requirements for K are greatest for most arable crops and so the availability of K is of critical importance to optimise production. The precision nutrient management of arable crops requires accurate and timely assessment of crop nutrient status. Much research and practice has focused on crop N status, while there has been a lack of focus on other important nutrients such as K. Therefore, in this study we assess the robustness of 12 fluorescence channels and several indices to predict nutrient status (K, Mg and Ca) across two cereal crops with different row management and lime status on an acidic K deficient soil. A multi-factorial experiment was used with the following treatment factors: crop (barley, wheat), K fertilizer rates (0, 25, 50, 100 kg K/ ha), lime (nil, 1 t/ ha) and two management factors (inter-row, windrow). At flowering the crop was sampled for biomass and nutrient content and proximal sensing (using a Multiplex fluorometer) undertaken of the crop canopy. Crop variables showed significant treatment effects. For instance, all crop variables were greater under the windrow treatment than the inter-row, K rate significantly increased grain yield and TGW, but K rate decreased protein and grain Ca and Mg content, also the grain yield was significantly greater under lime compared with the nil treatment. These crop effects enabled the identification of significant crop-fluorescence relationships. For instance, SFR_R (a chlorophyll index) predicted crop biomass (regardless of crop species) and FLAV predicted with the grain protein of windrow-grown barley. These results are promising and suggest crop-fluorescence relationships can be used to inform crop nutrient status which could be used to aid management decisions. Thus, there is good potential for fluorescence sensing to quantify crop K status and the opportunity to improve the timing and precision of K management for application within a precision agriculture system.

LiDAR technology is one option to collect spatial data about canopy geometry in many crops. However, the method of data acquisition includes many errors related to the LiDAR sensor, the GNSS receiver and the data acquisition set up. Therefore, the objective of this study was to evaluate the errors involved in the data acquisition from a mobile terrestrial laser scanner (MTLS). Regular shaped objects were scanned with a developed MTLS in two different tests: i) with the system mounted on a vehicle and ii) with the system mounted on a platform running over a rail. The errors of area estimation varied between 0.001 and 0.071 m2 for the circle, square and triangle objects. The errors on volume estimations were between 0.0003 and 0.0017 m3, for cylinders and truncated cone.

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.

This study aimed to examine the effects of replacing rolled barley (high in starch) with citrus pulp (high in digestible fibre) in a supplement on intake and performance of young growing cattle offered grass silage ad libitum for 101 days. Weaned, early- and late-maturing breed, male suckled beef calves (n=120) were blocked by sire breed, gender and weight and from within block randomly assigned to one of two concentrate supplements based mainly on rolled barley (BAR) or citrus pulp (CIT) and formulated to have similar concentrations of true protein digestible in the small intestine. On day 87, blood samples were taken before and 2 h after feeding, and rumen fluid samples were collected 2 h post-feeding. Supplement type did not affect (P>0.05) grass silage intake, live weight gain, final live weight, ultrasonically assessed body composition or measurements of skeletal size. Rumen pH (6.64 v. 6.79), ammonia (51 v. 81 mg/l) and acetate-to-propionate ratio (2.7 v. 3.2) were lower (P<0.001) for CIT than BAR. In conclusion, citrus pulp can replace barley in concentrate supplements for growing cattle without negatively affecting performance.

An accurate 3D model of an outdoor scene can be used in many different scenarios of precision agriculture, for instance to analyse the silhouette of a tree crown canopy for precision spraying, to count fruit for fruit yield prediction or to simply navigate a vehicle between the plant rows. Instead of using stereovision, limited by the problems of different light intensities, or by using expensive multi-channel 3D range finder (LIDAR scanner), limited by the number of channels, this work investigates the possibility of using two single channel LIDAR scanners mounted on a small robot to allow a real-time 3D object reconstruction of the robot environment. The approach used readings captured by two LIDAR scanners, SICK LMS111 and SICK TiM310, where the first one was scanning horizontally and the second one vertically. In order to correctly map the 3D points of the readings from the vertical sensor into a 3D space, a custom SLAM algorithm based on image registration techniques was used to calculate the new positions of the robot. The approach was tested in an indoor and outdoor environment, proving its accuracy with an error rate of 0.02 m±0.02 m for vertical and −0.01 m±0.13 m for the horizontal plane.

An increasing number of farm machines nowadays implement precision agriculture technologies. Most of these operate through proximal sensing using optical sensors (i.e. NIR or Vis-NIR). Imaging techniques in this context have received minor consideration due to the complex analysis of the data but on the other side offer great flexibility. This study reports a preliminary pilot imaging multi-sensor retrofit system to be applied independently on a wide range of agricultural machines and able to test different monitoring or control image-based applications for precision agriculture. The process, based on RGB image, was tested for in-field discrimination of weeds in lettuce and broccoli crops. It works by discriminating and extracting single plants from the soil and weeds. However, to be truly implementable, the experimental code should be optimized in order to shorten the time needed for acquisition and processing.

Activity of brown and beige adipocytes could contribute to breed differences in fat deposition. Therefore, we compared the abundance of markers for adipocyte types in steers from three cattle breeds differing in fat deposition (Japanese Black, Holstein, Charolais). Markers for white (leptin (LEP)), beige (transmembrane protein 26 (TMEM26), uncoupling protein 1 (UCP1)), and brown adipocytes (Zic family member 1 (ZIC1), UCP1) were analysed by quantitative reverse transcription PCR in subcutaneous fat (SCF), intramuscular fat (IMF), intermuscular fat (IRMF), perirenal fat (PF) and visceral fat (VF). LEP messenger RNA (mRNA) was less abundant in VF compared with other depots (P<0.05). TMEM26 was weakly but evenly expressed in all depots in all animals, whereas UCP1 mRNA showed higher individual variation in some depots. ZIC1 was not detectable in VF and PF but abundant in SCF, IMF and even more abundant in IRMF (P<0.05). No significant breed differences were detected. Using antibodies against UCP1, TMEM26 and ZIC1, we demonstrated that fat depots of 26-month-old cattle still comprise different adipocyte types. However, our results did not support the hypothesis that higher energy expenditure associated with higher abundance or activity of beige or brown adipocytes contributed to differences in fat deposition.

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.

The aim of this study was to identify genomic regions that associated with beef tenderness in Nellore cattle. Phenotypes were obtained according to the standard USDA Quality Grade (1999). Data from 909 genotyped Nellore bulls were used in the Genome-Wide Association Study (GWAS) undertaken using a single-step approach including also a pedigree file composed of 6276 animals. The analyses were performed using the Blupf90 software, estimating the effect of genomic windows of 10 consecutive markers. The GWAS results identified 18 genomic regions located on 14 different chromosomes (1, 4, 6, 7, 8, 10, 18, 19, 20, 21, 22, 25, 26 and 29), which explained more than 1% of the total additive genetic variance; several candidate genes were located in these regions including SLC2A9, FRAS1, ANXA3, FAM219A, DNAI, AVEN, SHISA7, UBE2S, CDC42EP5, CNTN3, C16orf96, UBALD1, MGRN1 and SNORA1 With the single-step GWAS, it was possible to identify regions and genes related to meat tenderness in Nellore beef cattle.

The most popular beef breed in South Africa is the Bonsmara, a locally developed composite breed adapted to sub-tropical conditions. The establishment of a genomic reference population is currently ongoing for the application of genomic selection. To date, 583 Bonsmara cattle (388 bulls and 195 cows) have been genotyped with the GeneSeek® Genomic Profiler Bovine HD™ Chip (GGP-HD) 80 K chip, and the population structure of the reference population was studied. The average minor allele frequency for the Bonsmara was 0.280 across 56 248 autosomal single-nucleotide polymorphisms (SNPs), whereas the observed and expected heterozygosity values were 0.361 and 0.365, respectively. After pruning the data set for SNPs in linkage disequilibrium, 19 119 SNPs were retained, averaging 659 SNPs per autosomal chromosome. This generated an average SNP density of 1 SNP per 90 kb. Structure analysis revealed a non-homogenous population with a high level of genetic admixture, which may negatively influence genomic breeding value prediction accuracy. Genotyping of a further 990 Bonsmara cattle are pending, using the GeneSeek® GGP-HD 150 K chip. As more animals will be added to the reference population, the profile of the reference population are expected to change in such a way to ensure improved genomic estimated breeding value accuracies.

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.

Intramuscular connective tissue plays an important role in meat tenderness. Our objective was to compare the collagen/hydroxyproline content and X-ray computed tomographic (CT) connective tissue proportion of longissimus thoracis (LT) muscle in Hungarian Simmental bulls and cows. Cows had lower carcass weight (247 kg v. 295 kg), EU conformation score (3.5 v. 5.5) and fatness score (4.2 v. 5.9) than bulls (P<0.01). Bulls had higher LT area, but intramuscular fat content was similar for bulls and cows. On the other hand, bulls had lower CT intramuscular connective tissue proportion in LT compared with cows (0.41±0.21% v. 0.66±0.28%, P<0.01). The same tendency could be observed for the collagen content (0.50±0.21% v. 0.67±0.11%, P<0.01). Correlation between the CT connective tissue proportion of LT and collagen content was r=0.8. There was a weak positive correlation between slaughter age and CT connective tissue as well as collagen content of LT (r=0.3 to 0.4). In conclusion, intramuscular connective tissue proportion in LT increased with slaughter age and older cows had higher collagen and connective tissue proportion than bulls. Mixed CT scans can be used for the analysis of intramuscular connective tissue content.

The goal of this work was to cluster maize plants perception points under six different growth stages in noisy 3D point clouds with known positions. The 3D point clouds were assembled with a 2D laser scanner mounted at the front of a mobile robot, fusing the data with the precise robot position, gained by a total station and an Inertial Measurement Unit. For clustering the single plants in the resulting point cloud, a graph-cut based algorithm was used. The algorithm results were compared with the corresponding measured values of plant height and stem position. An accuracy for the estimated height of 1.55 cm and the stem position of 2.05 cm was achieved.

Marbling is an economically important factor in many beef carcass grading schemes. This study aimed to evaluate marbling traits of longissimus thoracis muscle (LT) with different methods: USDA marbling score, intramuscular fat (IMF) content by image analysis of X-ray computed tomography (CT) scans and by conventional analytical method. Bulls (n=46) were slaughtered at an average weight of 536 kg and an average age of 646 days. Mean values of EUROP fat scores varied from 2− to 2+, whereas mean values of EUROP conformation grade from O0 to R0. Average growth rate of bulls was 955 g/day. Average chemical fat content of LT varied from 2.2% to 3.1% across conformation classes, and from 2.3% to 5.0% across fat classes. Intramuscular fat content in the 12th rib and in LT on CT-scans closely correlated with chemical fat content (r=0.81 and 0.85, respectively). The highest frequency of USDA marbling score was ‘small’ (55.2%), followed by ‘slight’ (25.5%), ‘modest’ (17%) and ‘moderate’ (2.1%). Computed tomography scans of LT can be used for the evaluation of marbling in Hungarian Simmental cattle. Bulls with higher growth rate had lower marbling level in LT (r=−0.4 to −0.5). EUROP conformation and fat scores have no relationship with marbling traits in this specific cattle population.

This paper presents an inspection system integrated into an on-ground autonomous platform with range of approximately 80 km. The vehicle is prepared to autonomously cover a field following a predefined route plan. Two types of cameras were integrated in the platform. An RGB-D sensor and a reflex camera were placed in a fixture and connected to a high-performance computer. The heterogeneous information acquired from the RGB-D was later integrated to automatically generate 3D maps of the crops by using custom software developed in the authors’ previous work. The inspection system performance was tested in actual vineyards by conducting several samplings in 2016. Results show that the proposed technology is viable and can provide complementary information to other inspection alternatives.

Due to the lack of reliable yield monitor for sugarcane, production factors which impact and limit stalk yield within fields are not well-known. Thus, this study aims to evaluate whether canopy sensor technology is able to identify sugarcane biomass variability and whether obtaining other agronomic variable data can assist on biomass quantification. For that, forty targeted plots were allocated within two sugarcane-producing fields and data consisted on manual biometric evaluation, aboveground biomass measurement and canopy reflectance. As an ongoing experiment, only two evaluations were addressed (~0.3 and 0.5 m stalk height). On the earliest stage, canopy sensor readings were correlated to sugarcane biomass and their sensitivity to biomass variability was high. Further, data collected on the first evaluation was efficient in predicting biomass amount after 30 days. On the second, canopy sensor readings effectiveness to predict biomass was reduced. These findings suggest that crop canopy reflectance sensing is a useful approach to investigate sugarcane biomass spatial-variability within fields on early stages.

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.