High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques

The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of "linking genotype and phenotype," considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders

Early Detection of Broad-Leaved and Grass Weeds in Wide Row Crops Using Artificial Neural Networks and UAV Imagery

Significant advances in weed mapping from unmanned aerial platforms have been achieved in recent years. The detection of weed location has made possible the generation of site specific weed treatments to reduce the use of herbicides according to weed cover maps. However, the characterization of weed infestations should not be limited to the location of weed stands, but should also be able to distinguish the types of weeds to allow the best possible choice of herbicide treatment to be applied. A first step in this direction should be the discrimination between broad-leaved (dicotyledonous) and grass (monocotyledonous) weeds. Considering the advances in weed detection based on images acquired by unmanned aerial vehicles, and the ability of neural networks to solve hard classification problems in remote sensing, these technologies have been merged in this study with the aim of exploring their potential for broadleaf and grass weed detection in wide-row herbaceous crops such as sunflower and cotton. Overall accuracies of around 80% were obtained in both crops, with user accuracy for broad-leaved and grass weeds around 75% and 65%, respectively. These results confirm the potential of the presented combination of technologies for improving the characterization of different weed infestations, which would allow the generation of timely and adequate herbicide treatment maps according to groups of weeds

Effect of added grape seed and skin on chicken thigh patties during chilled storage

The effect of 2% grape seed and 2% grape skin powder added to chicken thigh patties stored at 4°C was assessed by measuring lipid oxidation, total phenolic content, pH, color changes and sensory attributes. The addition of these grape by-products to the patties lowered pH values and significantly reduced in lightness, redness and yellowness compared with the control. However, the addition of grape seed and skin significantly improved the oxidative stability of raw chicken patties due to higher total phenolic content, grape seed being more effective than skin in this regard. The phenolic content of these samples remained stable even after cooking. The acceptability of the chicken meat in general was not affected by the addition of grape by-products. These results show that grape seed and grape skin could potentially be used as natural antioxidants in raw chicken patties and would be accepted by consumers.The authors thank the MINECO, and CSIC for financial support of Projects AGL2012-31355/GAN and the Intramural 2014470E073. Also we are grateful the CAM and ESI Funds for the financial support the project MEDGAN-CM S2013/ABI2913). Thanks to the MIUR and UNIMOL for the PhD fellowship of Maria Nardoia.Peer Reviewe

Effect of polyphenols dietary grape by-products on chicken patties

An experiment was conducted to study the dietary effect that the inclusion (40 g kg) of grape seed (GS), grape skin (SS), grape pomace (GP), and (0.2 g kg) of vitamin E (E) had on the composition and microbiological quality of chicken breast meat and on the physico-chemical parameters (TBARS, pH, color, Kramer shear force), sensorial characteristics, and microbiological quality of chicken breast meat patties during chilled storage (0, 3, 6, and 9 days) at 2 °C. In general, proximate composition and microbial counts of the raw chicken breast meat and the patties were not affected. Lower TBARS values were detected in patties formulated with breast meat obtained from birds fed E, SS, and GP diets. No clear effect was observed on the color or textural characteristics of the different patties. The addition of SS and GP in chicken diets reduced TBARS values showing some improvement in the oxidative stability of breast patties without affecting its technological properties, sensorial attributes, or microbial quality.The authors thank the MINECO and CSIC for financial support of Projects AGL2012-31355/GAN, AGL2014-53207-C2-1-R, and the Intramural 2014470E073. In addition, we are grateful to CAM and ESI Funds for financially supporting project MEDGAN-CM S2013/ABI2913). We would also like to thank MIUR and UNIMOL for the Ph.D. fellowship of Maria Nardoia.Peer Reviewe