Marine Science Can Contribute to the Search for Extra-Terrestrial Life.

Life on our planet likely evolved in the ocean, and thus exo-oceans are key habitats to search for extraterrestrial life. We conducted a data-driven bibliographic survey on the astrobiology literature to identify emerging research trends with marine science for future synergies in the exploration for extraterrestrial life in exo-oceans. Based on search queries, we identified 2592 published items since 1963. The current literature falls into three major groups of terms focusing on (1) the search for life on Mars, (2) astrobiology within our Solar System with reference to icy moons and their exo-oceans, and (3) astronomical and biological parameters for planetary habitability. We also identified that the most prominent research keywords form three key-groups focusing on (1) using terrestrial environments as proxies for Martian environments, centred on extremophiles and biosignatures, (2) habitable zones outside of "Goldilocks" orbital ranges, centred on ice planets, and (3) the atmosphere, magnetic field, and geology in relation to planets' habitable conditions, centred on water-based oceans.

Influence of the Drying Process on the Volatile Profile of Different Capsicum Species.

Chili is a globally significant spice used fresh or dried for culinary, condiment, and medicinal purposes. Growing concerns about food safety have increased the demand for high-quality products and non-invasive tools for quality control like origin tracing and safety assurance. Volatile analysis offers a rapid, comprehensive, and safe method for characterizing various food products. Thus, this study aims to assess the impact of the drying process on the aromatic composition of various Capsicum species and to identify key compounds driving the aromatic complexity of each genetic makeup. To accomplish these objectives, the aroma was examined in fruits collected from 19 different pepper accessions (Capsicum sp.) belonging to four species: one ancestral (C. chacoense) and three domesticated pepper species (C. annuum, C. baccatum and C. chinense). Fresh and dried samples were analyzed using a headspace PTR-TOF-MS platform. Our findings reveal significant changes in the composition and concentration of volatile organic compounds (VOCs) from fresh to dried Capsicum. Notably, chili peppers of the species C. chinense consistently exhibited higher emission intensity and a more complex aroma compared to other species (both fresh and dried). Overall, the data clearly demonstrate that the drying process generally leads to a reduction in the intensity and complexity of the aromatic compounds emitted. Specifically, fresh peppers showed higher volatile organic compounds content compared to dried ones, except for the two sweet peppers studied, which exhibited the opposite behavior. Our analysis underscores the variability in the effect of drying on volatile compound composition among different pepper species and even among different cultivars, highlighting key compounds that could facilitate species classification in dried powder. This research serves as a preliminary guide for promoting the utilization of various pepper species and cultivars as powder, enhancing product valorization.

Distinguishing the Effects of Water Volumes versus Stocking Densities on the Skeletal Quality during the Pre-Ongrowing Phase of Gilthead Seabream (Sparus aurata).

Gilthead seabream (Sparus aurata) production is a highly valued aquaculture industry in Europe. The presence of skeletal deformities in farmed gilthead seabream represents a major bottleneck for the industry leading to economic losses, negative impacts on the consumers' perception of aquaculture, and animal welfare issues for the fish. Although past work has primarily focused on the hatchery phase to reduce the incidence of skeletal anomalies, this work targets the successive pre-ongrowing phase in which more severe anomalies affecting the external shape often arise. This work aimed to test the effects of: (i) larger and smaller tank volumes, stocked at the same density; and (ii) higher and lower stocking densities maintained in the same water volume, on the skeleton of gilthead seabream fingerlings reared for ~63 days at a pilot scale. Experimental rearing was conducted with gilthead seabream juveniles (~6.7 ± 2.5 g), which were selected as 'non-deformed' based on external inspection, stocked at three different densities (Low Density (LD): 5 kg/m3; Medium Density (MD): 10 kg/m3; High Density (HD): 20 kg/m3) in both 500 L and 1000 L tanks. Gilthead seabream were sampled for growth performance and radiographed to assess the skeletal elements at the beginning and end of the experimental trial. Results revealed that (i) LD fish were significantly longer than HD fish, although there were no differences in final weights, regardless of the water volume; (ii) an increase in the prevalence of seabream exhibiting cranial and vertebral axis anomalies was found to be associated with increased density. These results suggest that farmers can significantly reduce the presence of some cranial and axis anomalies affecting pre-ongrown gilthead seabream by reducing the stocking density.

Established and Emerging Research Trends in Norway Lobster, Nephrops norvegicus.

The burrowing crustacean decapod Nephrops norvegicus is a significant species in European Atlantic and Mediterranean fisheries. Research over the decades has mainly focused on behavioral and physiological aspects related to the burrowing lifestyle, since animals can only be captured by trawls when engaged in emergence on the seabed. Here, we performed a global bibliographic survey of all the scientific literature retrieved in SCOPUS since 1965, and terminology maps were produced with the VOSviewer software to reveal established and emerging research areas. We produced three term-map plots: term clustering, term citation, and term year. The term clustering network showed three clusters: fishery performance, assessment, and management; biological cycles in growth, reproduction, and behavior; and finally, physiology and ecotoxicology, including food products. The term citation map showed that intense research is developed on ecotoxicology and fishery management. Finally, the term year map showed that the species was first studied in its morphological and physiological aspects and more recently in relation to fishery and as a food resource. Taken together, the results indicate scarce knowledge on how burrowing behavior and its environmental control can alter stock assessment, because of the poor use of current and advanced monitoring technologies.

Automated Prototype for Bombyx mori Cocoon Sorting Attempts to Improve Silk Quality and Production Efficiency through Multi-Step Approach and Machine Learning Algorithms.

Cocoon sorting is one of the most labor-demanding activities required both at the end of the agricultural production and before the industrial reeling process to obtain an excellent silk quality. In view of the possible relaunch of European sericulture, the automatization of this production step is mandatory both to reduce silk costs and to standardize fiber quality. The described research starts from this criticality in silk production (the manual labor required to divide cocoons into different quality classes) to identify amelioration solutions. To this aim, the automation of this activity was proposed, and a first prototype was designed and built. This machinery is based on the use of three cameras and imaging algorithms identifying the shape and size of the cocoons and outside stains, a custom-made light sensor and an AI model to discard dead cocoons. The current efficiency of the machine is about 80 cocoons per minute. In general, the amelioration obtained through this research involves both the application of traditional sensors/techniques to an unusual product and the design of a dedicated sensor for the identification of dead/alive pupae inside the silk cocoons. A general picture of the overall efficiency of the new cocoon-sorting prototype is also outlined.

Different Traits, Different Evolutionary Pathways: Insights from Salamandrina (Amphibia, Caudata).

Species delimitation is often based on a single or very few genetic or phenetic traits, something which leads to misinterpretations and often does not provide information about evolutionary processes. Here, we investigated the diversity pattern of multiple phenetic traits of the two extant species of Salamandrina, a genus split only after molecular traits had been studied but the two species of which are phenetically very similar. The phenetic traits we studied are size, external body shape and head colour pattern, in a model comparison framework using non-linear mixed models and unsupervised and supervised clustering. Overall, we found high levels of intra-specific variability for body size and shape, depending on population belonging and habitat, while differences between species were generally lower. The habitat the salamanders dwell in also seems important for colour pattern. Basing on our findings, from the methodological point of view, we suggest (i) to take into account the variability at population level when testing for higher level variability, and (ii) a semi-supervised learning approach to high dimensional data. We also showed that different phenotypic traits of the same organism could result from different evolutionary routes. Local adaptation is likely responsible for body size and shape variability, with selective pressures more similar across species than within them. Head colour pattern also depends on habitat, differently from ventral colour pattern (not studied in this paper) which likely evolved under genetic drift.

Olive Fruit Selection through AI Algorithms and RGB Imaging.

(1) Background: Extra virgin olive oil production is strictly influenced by the quality of fruits. The optical selection allows for obtaining high quality oils starting from batches with different qualitative characteristics. This study aims to test a CNN algorithm in order to assess its potential for olive classification into several quality classes for industrial purposes, specifically its potential integration and sorting performance evaluation. (2) Methods: The acquired samples were all subjected to visual analysis by a trained operator for the distinction of the products in five classes related to the state of external veraison and the presence of visible defects. The olive samples were placed at a regular distance and in a fixed position on a conveyor belt that moved at a constant speed of 1 cm/s. The images of the olives were taken every 15 s with a compact industrial RGB camera mounted on the main frame in aluminum to allow overlapping of the images, and to avoid loss of information. (3) Results: The modelling approaches used, all based on AI techniques, showed excellent results for both RGB datasets. (4) Conclusions: The presented approach regarding the qualitative discrimination of olive fruits shows its potential for both sorting machine performance evaluation and for future implementation on machines used for industrial sorting processes.

Advantages in Using Colour Calibration for Orthophoto Reconstruction.

UAVs are sensor platforms increasingly used in precision agriculture, especially for crop and environmental monitoring using photogrammetry. In this work, light drone flights were performed on three consecutive days (with different weather conditions) on an experimental agricultural field to evaluate the photogrammetric performances due to colour calibration. Thirty random reconstructions from the three days and six different areas of the field were performed. The results showed that calibrated orthophotos appeared greener and brighter than the uncalibrated ones, better representing the actual colours of the scene. Parameter reporting errors were always lower in the calibrated reconstructions and the other quantitative parameters were always lower in the non-calibrated ones, in particular, significant differences were observed in the percentage of camera stations on the total number of images and the reprojection error. The results obtained showed that it is possible to obtain better orthophotos, by means of a calibration algorithm, to rectify the atmospheric conditions that affect the image obtained. This proposed colour calibration protocol could be useful when integrated into robotic platforms and sensors for the exploration and monitoring of different environments.

Superior EVOO Quality Production: An RGB Sorting Machine for Olive Classification.

Extra virgin olive oil (EVOO) is a commercial product of high quality, thanks to its nutritional and organoleptic characteristics. The olives ripeness and the choice of harvest time according to their color and size, strongly influences the quality of the EVOO. The physical sorting of olives with machines performing rapid and objective optical selection, impossible by hand, can improve the quality of the final product. The aim of this study concerns the classification of olives into two qualitative classes, based on the maturity stage and the presence of external defects, through an industrial RGB optical sorting prototype, evaluating its performance and comparing the results with those obtained visually by trained operators. EVOOs obtained from classified olives were characterized through chemical, physical-chemical analysis and sensory profile. For the first time, the optoelectronic technologies in an industrial system was tested on olives to produce superior quality EVOO. The selection allows late harvest, obtaining oils with good characteristics from fully ripe and unripe fruits together, separating defective olives with appropriate calibration and training. Optoelectronic selection creates the opportunity to blend the obtained oils destined to different applications according to the needs of the consumer or producer, using a vanguard technology at low cost.

Sorting biotic and abiotic stresses on wild rocket by leaf-image hyperspectral data mining with an artificial intelligence model.

BACKGROUND: Wild rocket (Diplotaxis tenuifolia) is prone to soil-borne stresses under intensive cultivation systems devoted to ready-to-eat salad chain, increasing needs for external inputs. Early detection of the abiotic and biotic stresses by using digital reflectance-based probes may allow optimization and enhance performances of the mitigation strategies. METHODS: Hyperspectral image analysis was applied to D. tenuifolia potted plants subjected, in a greenhouse experiment, to five treatments for one week: a control treatment watered to 100% water holding capacity, two biotic stresses: Fusarium wilting and Rhizoctonia rotting, and two abiotic stresses: water deficit and salinity. Leaf hyperspectral fingerprints were submitted to an artificial intelligence pipeline for training and validating image-based classification models able to work in the stress range. Spectral investigation was corroborated by pertaining physiological parameters. RESULTS: Water status was mainly affected by water deficit treatment, followed by fungal diseases, while salinity did not change water relations of wild rocket plants compared to control treatment. Biotic stresses triggered discoloration in plants just in a week after application of the treatments, as evidenced by the colour space coordinates and pigment contents values. Some vegetation indices, calculated on the bases of the reflectance data, targeted on plant vitality and chlorophyll content, healthiness, and carotenoid content, agreed with the patterns of variations observed for the physiological parameters. Artificial neural network helped selection of VIS (492-504, 540-568 and 712-720 nm) and NIR (855, 900-908 and 970 nm) bands, whose read reflectance contributed to discriminate stresses by imaging. CONCLUSIONS: This study provided significative spectral information linked to the assessed stresses, allowing the identification of narrowed spectral regions and single wavelengths due to changes in photosynthetically active pigments and in water status revealing the etiological cause.

Current Methods, Common Practices, and Perspectives in Tracking and Monitoring Bioinoculants in Soil.

Microorganisms promised to lead the bio-based revolution for a more sustainable agriculture. Beneficial microorganisms could be a valid alternative to the use of chemical fertilizers or pesticides. However, the increasing use of microbial inoculants is also raising several questions about their efficacy and their effects on the autochthonous soil microorganisms. There are two major issues on the application of bioinoculants to soil: (i) their detection in soil, and the analysis of their persistence and fate; (ii) the monitoring of the impact of the introduced bioinoculant on native soil microbial communities. This review explores the strategies and methods that can be applied to the detection of microbial inoculants and to soil monitoring. The discussion includes a comprehensive critical assessment of the available tools, based on morpho-phenological, molecular, and microscopic analyses. The prospects for future development of protocols for regulatory or commercial purposes are also discussed, underlining the need for a multi-method (polyphasic) approach to ensure the necessary level of discrimination required to track and monitor bioinoculants in soil.

Research Trends and Future Perspectives in Marine Biomimicking Robotics.

Mechatronic and soft robotics are taking inspiration from the animal kingdom to create new high-performance robots. Here, we focused on marine biomimetic research and used innovative bibliographic statistics tools, to highlight established and emerging knowledge domains. A total of 6980 scientific publications retrieved from the Scopus database (1950-2020), evidencing a sharp research increase in 2003-2004. Clustering analysis of countries collaborations showed two major Asian-North America and European clusters. Three significant areas appeared: (i) energy provision, whose advancement mainly relies on microbial fuel cells, (ii) biomaterials for not yet fully operational soft-robotic solutions; and finally (iii), design and control, chiefly oriented to locomotor designs. In this scenario, marine biomimicking robotics still lacks solutions for the long-lasting energy provision, which presently hinders operation autonomy. In the research environment, identifying natural processes by which living organisms obtain energy is thus urgent to sustain energy-demanding tasks while, at the same time, the natural designs must increasingly inform to optimize energy consumption.

Are Peach Cultivars Used in Conventional Long Food Supply Chains Suitable for the High-Quality Short Markets?

Peaches are climacteric and highly perishable fruits, with a short shelf life, and are prone to rapid deterioration after harvest. In this study, the chemical proprieties, aroma profile and a sensory evaluation were conducted to: (1) characterize and compare fruits of 13 different peach and nectarine cultivars, harvested at physiological maturation; and (2) assess the suitability of these cultivars, that are successfully used in long food supply chains (LFSCs), for their use in short food supply chains (SFSCs). Through statistical analysis clear differences were found among the studied cultivars, and in particular between cultivars suited to SFSCs compared to those suited for LFSCs. Results indicate that, despite all cultivars being planted in the same orchards and with the same pre-harvest management and practices, their post-harvest performances were mainly influenced by the cultivar genetic makeup. Therefore, cultivars conventionally used in SFSCs, such as "Guglielmina" and "Regina di Londa", had the best aroma, sweetness and juiciness compared to LSCPs ones. In contrast, the LSCPs varieties showed interesting values for firmness and crunchiness.

An Open Source Low-Cost Device Coupled with an Adaptative Time-Lag Time-Series Linear Forecasting Modeling for Apple Trentino (Italy) Precision Irrigation.

Precision irrigation represents those strategies aiming to feed the plant needs following the soil's spatial and temporal characteristics. Such a differential irrigation requires a different approach and equipment with regard to conventional irrigation to reduce the environmental impact and the resources use while maximizing the production and thus profitability. This study described the development of an open source soil moisture LoRa (long-range) device and analysis of the data collected and updated directly in the field (i.e., weather station and ground sensor). The work produced adaptive supervised predictive models to optimize the management of agricultural precision irrigation practices and for an effective calibration of other agronomic interventions. These approaches are defined as adaptive because they self-learn with the acquisition of new data, updating the on-the-go model over time. The location chosen for the experimental setup is a cultivated area in the municipality of Tenna (Trentino, Alto Adige region, Italy), and the experiment was conducted on two different apple varieties during summer 2019. The adaptative partial least squares time-lag time-series modeling, in operative field conditions, was a posteriori applied in the consortium for 78 days during the dry season, producing total savings of 255 mm of irrigated water and 44,000 kW of electricity, equal to 10.82%.

A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots.

The degree of olive maturation is a very important factor to consider at harvest time, as it influences the organoleptic quality of the final product, for both oil and table use. The Jaén index, evaluated by measuring the average coloring of olive fruits (peel and pulp), is currently considered to be one of the most indicative methods to determine the olive ripening stage, but it is a slow assay and its results are not objective. The aim of this work is to identify the ripeness degree of olive lots through a real-time, repeatable, and objective machine vision method, which uses RGB image analysis based on a k-nearest neighbors classification algorithm. To overcome different lighting scenarios, pictures were subjected to an automatic colorimetric calibration method-an advanced 3D algorithm using known values. To check the performance of the automatic machine vision method, a comparison was made with two visual operator image evaluations. For 10 images, the number of black, green, and purple olives was also visually evaluated by these two operators. The accuracy of the method was 60%. The system could be easily implemented in a specific mobile app developed for the automatic assessment of olive ripeness directly in the field, for advanced georeferenced data analysis.

Application of near-infrared handheld spectrometers to predict semolina quality.

BACKGROUND: Durum wheat semolina is the best raw material for pasta production and its protein content and gluten strength are essential for cooking quality. The need to develop rapid methods to speed up quality control makes near-infrared spectroscopy (NIR) a useful method that is widely accepted in the cereal sector. In this study, two non-destructive and rapid technologies, a low-cost sensor providing a short wavelength NIR range (swNIR: 700-1100 nm) and a handheld NIR spectrometer (NIR: 1600-2400 nm), were employed to evaluate semolina quality. The spectra data were correlated with chemical (protein content) and rheological parameters (i.e., Gluten Index, Alveograph®, Sedimentation test, GlutoPeak®). A partial least squares (PLS) model was used to compare the efficacy of swNIR and NIR. RESULTS: The protein content was the reference parameter that correlated best with the spectra data and provided the best regression model (r model = 0.9788 for NIR and 0.9561 for swNIR). GlutoPeak indices also correlated well with spectral data, particularly with swNIR spectra. A provisional multivariate model was applied to classify semolina samples in quality classes by using their spectra. Better modeling efficiency was obtained for swNIR. CONCLUSION: The results highlighted the advantages of a pocket-sized low cost sensor (swNIR), which is easier to use directly at the sample source than laboratory instruments or more expensive portable devices. © 2020 Society of Chemical Industry.

Light Drones for Basic In-Field Phenotyping and Precision Farming Applications: RGB Tools Based on Image Analysis.

Plant phenotyping has garnered major attention in recent years, leading to developing new strategies to measure and assess plant traits of interest. For data acquisition of large fields, devices and sensors are required that deliver detailed and reproducible temporal and spatial information on the cultivated crop. This work proposes the potential use of low-cost light drones for in-field phenotyping applications on cereal crops. The proposed method allows to obtain precise measurements of color and height of the plants for the individual plots. The method is based on a color calibration algorithm (TPS-3D interpolating function) and a 3D ortho image reconstruction. The method has been applied on an experimental field with durum and soft wheat parcels obtaining information on real color (with an error lower than 12/256) and height for each single plot.

An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions.

Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species-the bubblegum coral Paragorgia arborea-in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory. Images and concomitant oceanographic data were taken hourly from February to June 2018. We considered coral activity in terms of bloated, semi-bloated and non-bloated surfaces, as proxy for polyp filtering, retraction and transient activity, respectively. A test accuracy of 90.47% was obtained. Chronobiology-oriented statistics and advanced Artificial Neural Network (ANN) multivariate regression modeling proved that a daily coral filtering rhythm occurs within one major dusk phase, being independent from tides. Polyp activity, in particular extrusion, increased from March to June, and was able to cope with an increase in chlorophyll concentration, indicating the existence of seasonality. Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images. These are helpful to obtain multidisciplinary information from cabled observatory infrastructures.

An artificial class modelling approach to identify the most largely diffused cultivars of sweet cherry (Prunus avium L.) in Italy.

The nutritional and commercial value of the sweet cherry provides it a great economic importance in Italy. The aim of this study was to characterize 35 sweet cherry cultivars and one of sour cherry, by analyzing values of different pomological and nutraceutical traits, identifying cultivars with antioxidant activity and total anthocyanins content closest to those present in literature for Ferrovia (largely diffused in Italy). To this goal, a multivariate metric index through the Soft Independent Modeling of Class Analogy analyzing an artificial dataset and testing a real one, two hierarchical clustering and a principal component analysis, were performed. The multivariate analyses result simultaneously investigated all the variables highlighting cvs. Sylvia, Graffione nero Col di Mosso, Ferrovia, Mora della Punta, Bianchetta Nuchis and Sandra to be more similar to literature data of Ferrovia. This matrix index was a useful tool, to select the most commercial promising varieties.

An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer.

Extra virgin olive oil (EVOO) represents a crucial ingredient of the Mediterranean diet. Being a first-choice product, consumers should be guaranteed its quality and geographical origin, justifying the high purchasing cost. For this reason, it is important to have new reliable tools able to classify products according to their geographical origin. The aim of this work was to demonstrate the efficiency of an open source visible and near infra-red (VIS-NIR) spectrophotometer, relying on a specific app, in assessing olive oil geographical origin. Thus, 67 Italian and 25 foreign EVOO samples were analyzed and their spectral data were processed through an artificial intelligence algorithm. The multivariate analysis of variance (MANOVA) results reported significant differences (p < 0.001) between the Italian and foreign EVOO VIS-NIR matrices. The artificial neural network (ANN) model with an external test showed a correct classification percentage equal to 94.6%. Both the MANOVA and ANN tested methods showed the most important spectral wavelengths ranges for origin determination to be 308-373 nm and 594-605 nm. These are related to the absorption of phenolic components, carotenoids, chlorophylls, and anthocyanins. The proposed tool allows the assessment of EVOO samples' origin and thus could help to preserve the "Made in Italy" from fraud and sophistication related to its commerce.