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Peach fruit is one of the most economically widespread temperate fruits, whose productivity, and nutritional and sensory qualities are determined by interactions among several environmental and genetic factors, rootstocks, agronomic practices and pedo-climatic conditions. In recent years, climate change has prompted peach breeding programs to use specific rootstocks that are well adapted to unusual soil and climate characteristics, thus improving the plant’s adaptability and fruit quality. The aim of this work was to assess the biochemical and nutraceutical profile of two different peach cultivars, considering their growth on different rootstocks over three crop years. An analysis was carried out evaluating the interactive effect of all factors (i.e., cultivars, crop years and rootstocks) revealing the advantages or disadvantages on growth of the different rootstocks. Soluble solids content, titratable acidity, total polyphenols, total monomeric anthocyanins and antioxidant activity in fruit skin and pulp were analyzed. An analysis of variance was performed to assess the differences between the two cultivars considering the effect of rootstock (one way) and crop years, rootstocks and their interaction (two ways). In addition, two principal component analyses were performed separately on the phytochemical traits of the two cultivars to visualize the distributions of the five peach rootstocks during the three crop years. The results showed that fruit quality parameters are strongly dependent on cultivars, rootstocks and climatic conditions. All these aspects could be useful for the choice of rootstock in relation to agronomic management, making this study a valuable tool for choosing the best rootstock, considering simultaneously more factors affecting peaches’ biochemical and nutraceutical profile.

A multi-factorial parametric characterization of soil health is crucial to monitor the correct provision of soil ecosystem services (ES) and to ensure its preservation over time in the current climate change scenario. In this framework, the implementation of biological indicators is still lagging the established use of physical and chemical indicators for soil quality assessment. Moreover, different biological groups are often analysed separately and not like a unique set of dynamic compartments that necessarily interact with each other, mutually determining soil biodiversity. Different initiatives at EU level have been undertaken to improve soil biodiversity monitoring practices, to finally promote their integration into Member States’ national soil quality monitoring policies. Nevertheless, few initiatives clearly aimed at evaluating the multiple level integration of soil health monitoring indices. at multiple level, and established practices for determining and communicating multi-level soil biological health are still scarce.The aim of this work is to ultimately develop a framework for the synthesis of multiple indices of soil quality, focused on biological indicators, to determine soil health and monitor its provision of ES. This work dwells at the intersection of the EXCALIBUR and EJP-Soil (MINOTAUR) European projects action and consortia. As a proof-of-concept, we selected "nutrient cycling" as ES to be evaluated. We identified the soil chemical and physical indicators that could be used for the monitoring of this ES under real field conditions within different pedoclimatic contexts, and we used those variables to build an "a priori" knowledge set and model of the nutrient cycling features in the considered samples. Next, we selected a set of possible biological indicators at different scales (Ii.e. synthetic indices or specific community components) and for different levels of soil biota (Ii.e. meso-fauna, micro-fauna, bacterial and fungal microbiota). Finally, we investigated the relationship between biological and chemical-physical indicators with the working hypothesis of identifying the most robust relationships. Different unsupervised data analysis methodologies were assessed for their ability in the evaluation ofuncovering the relationship between abiotic and biotic variables, also paying attention to the possibility of clear representation of the data and results for improved communication

Eurasian grapevine is a widely cultivated horticultural plant worldwide, but it is more susceptible to powdery mildew. In recent years, the high cost and negative environmental impact of calendar-applied sulfur fungicides are leading research to find alternative remedies. In this study, the early prediction (three days) of the first appearance of powdery mildew infection, on two different Italian grapevine cultivars, was detected through a partial least squares discriminant analysis (PLSDA). The treatment indications of the “PLSDA” models (treatments according to the predictive model) were compared with those of the “Standard” (treatments according to the established agricultural practice of the area). This allowed the early containment of the disease, preventing its subsequent propagation. The model was built based on weather-climate data and phytopathological information collected on the “Untreated” control cultivar to monitor the natural spread of the disease (three years of training and two of tests). For both the cultivars and the two test years (2021 and 2022), the “PLSDA” models early predicted the first appearance of fungal disease, reducing the treatment number (about four) with respect to “Standard”. In addition, analyses of key fruit quality parameters were conducted to evaluate the effectiveness of treatment reduction.

The sweet cherry fruit quality traits (soluble solid content, titratable acidity, pH and soluble solid titratable acidity ratio), phytochemical content (total phenolic content and total monomeric a...

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.

This work has the purpose to define current level of Basilicata Region’s forest management planning (PAF) and to collect different data concerning to municipal agro-silvo-pastoral properties, administrated until today, and about the planning level reached. In order to allow a correct comparison about different forest populations, the acquired data have been aggregated into homogeneous categories. According to PAF information in this paper are listed: working circle types, main forest system used type, main and dominant species and age-class distribution. Data analysis were based on a comparative analysis about of spatial impact of managed areas if related to analyzed factors, in order to evaluate the areal distribution of collected information. In both provinces (Potenza and Matera), according to data analysis, the working circles that appear to be more frequent have productive and protective functions, while the most extensive type of management forms is high forest system. The comparison between tree factors like main species, managed area and provinces show that in Matera Province the dominant species is the Turkey oak (Quercus cerris L.); instead beech (Fagus sylvatica L.) is the most widespread one in province of Potenza. Moreover Quercus is the most represented genus on the regional territory, if you add mixed oak woods to the Turkey oak. Age-class distribution is an another factor which make a difference between Potenza and Matera provinces: while in Matera is easier to found young forests, uneven-aged management is typical of Potenza’s area, and in both cases we found high forest system. The connection between main species and forest system show us that simple coppice system is typical of mixed oak woods instead beech or Turkey oak pure stand are previously managed as high forest system. In a large quantity of cases, Basilicata Regions coppices are however aged, as shown by the age-class distribution analysis. Finally this work try to understand the relationship between forest management and forest fires.

Background: The present work aims at obtaining an approximate early production estimate of olive orchards used for extra virgin olive oil production by combining image analysis techniques with light drone images acquisition and photogrammetric reconstruction. Methods: In May 2019, an orthophoto was reconstructed through a flight over an olive grove to predict oil production from segmentation of plant canopy surfaces. The orchard was divided into four plots (three considered as training plots and one considered as a test plot). For each olive tree of the considered plot, the leaf surface was assessed by segmenting the orthophoto and counting the pixels belonging to the canopy. At harvesting, the olive production per plant was measured. The canopy radius of the plant (R) was automatically obtained from the pixel classification and the measured production was plotted as a function of R. Results: After applying a k-means-classification to the four plots, two distinct subsets emerged in association with the year of loading (high-production) and unloading. For each plot of the training set the logarithm of the production curves against R were fitted with a linear function considering only four samples (two samples belonging to the loading region and two samples belonging to the unloading one) and the total production estimate was obtained by integrating the exponent of the fitting-curve over R. The three fitting curves obtained were used to estimate the total production of the test plot. The resulting estimate of the total production deviates from the real one by less than 12% in training and less than 18% in tests. Conclusions: The early estimation of the total production based on R extracted by the orthophotos can allow the design of an anti-fraud protocol on the declared production.

The improvement of harvesting methodologies plays an important role in the optimization of wood production in a context of sustainable forest management. Different harvesting methods can be applied according to forest site-specific condition and the appropriate mechanization level depends on a number of factors. Therefore, efficiency and functionality of wood harvesting operations depend on several factors. The aim of this study is to analyze how the different harvesting processes affect operational costs and labor productivity in typical small-scale Italian harvesting companies. A multiple linear regression model (MLR) and artificial neural network (ANN) have been carried out to predict gross time, productivity and costs estimation in a series of qualitative and quantitative variables. The results have created a correct statistical model able to accurately estimate the technical parameters (work time and productivity) and economic parameters (costs per unit of product and per hectare) useful to the forestry entrepreneur to predict the results of the work in advance, considering only the values detectable of some characteristic elements of the worksite.

Aquaculture is presented as a sustainable alternative to the consumption of wild fish, for example, reducing inputs (such as feed), optimizing outputs, and reducing pollution. Extending to the agricultural framework, in this context, different technologies are being used to diminish those environmental hazards, giving rise to the precision agriculture/aquaculture being this a management concept based on observing, measuring, and responding space/temporal variability of productions. The scope of the precision aquaculture is to apply control-engineering principles to the production, to direct farmers to a better monitoring, control, and documentation of biological processes in fish farms. The aim of this review is to show an overview (enriched by a terms mapping analysis) of the precision aquaculture engineering innovations, with some examples of commercial systems, even if most of them are not specifically addressed in the precision aquaculture framework, in terms of: computer vision for animal monitoring, environmental monitoring tools, and sensor network (i.e., wireless sensor network, and long-range), robotics, and finally data interpretation and decision tools (i.e., algorithms, Internet of Things, and Decision Support Systems). Over the Internet of Things, cyber-physical systems communicate and cooperate with each other and with humans in real-time both internally and across organizational services offered and used by participants of the value chain. To increase the production and ameliorate the fish product quality and animal welfare issues, it is becoming even more important to monitor and control the production process.

Nowadays precision agriculture is undertaking a steep growth in terms of both, commercial products and research and development applications. This is rapidly changing the crop management system taking into grater consideration data acquisition and their elaboration, real-time or offline, to correctly consider the spatial and temporal variability of crop and soil factors. This step is due to reduce the energy inputs and the applications of chemicals and fertilizers, as well as Greenhouse gas (GHGs), while increasing the production. Sustainability is a must while trying to fulfil the food and fiber needs of the rapidly growing human population. In this light, the present review, more than reporting an exhaustive picture, aims attempt to show a clear panorama of such a context underlining the principal scientific researches and applications inherent optoelectronic proximal sensing vehicle-mounted technologies in precision agriculture. The review has been structured following the main sensors types and their core applications: grayscale/RGB imaging, Visible-Near infrared (Vis-NIR) and NIR, sensors dedicated to Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge index (NDRE), stereovision, thermography, combined sensors, sensors dedicated to phenotyping and others. Moreover, were considered two separate chapters regarding dedicated analytical approaches use and development and a term map analysis to graphically highlight the main clusters owing each to a specific subject area.

The aim of this work is to realize a term map analysis on technological advancements, in the year and in the world, of scientific researches of food traceability. Quality protection needs efficient instruments to discriminate Protected Denomination of Origin and Protected Geographical Indication varieties in field and to trace them along the agri-food chain. This study attempts to analyze global scientific of food traceability researches (between 1999 and 2018). In this period, 2534 scientific publications by Scopus database were found. Publication trends, research topics and their geographical distribution were analyzed by science mapping (VOSviewer software). Term map evidenced four main groups: red cluster with terms about food product and analytical methods for the characterization of food; green cluster including terms related with consumer (e.g., “food safety” and “food packaging”); blue cluster associating terms with the technology for traceability and yellow cluster with identification of food by genetic marker. It is possible to observe many links (i.e., co-occurrence between terms) in the green and blue clusters and among them. The yellow cluster could be considered as a subcategory of red one. In addition, green cluster refers to consumer and food safety. Yellow and red clusters contain analytical methods to identify food product, while blue cluster refers to advancements technological transferring information about the product to the consumer. These clusters do not present many linkages, and the consumer is in-between these. Finally, this study contributes to a better knowing of food traceability, and to an enhanced scientific research of technological advancements in supply chain.

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.

Aim of study: Genetic diversity of pistachio, can be evaluated by using different descriptors, as adopted in international certification systems. Mainly the descriptors are morphological traits as leaf, which represents an important organ for its sensibility to growth conditions during the expansion phase. This study adopted a rapid and quantitative non-hierarchic clustering classification (k-means), to extract size classes basing on the contemporary combination of different morphological traits (i.e., leaf stalk length, terminal leaf length, terminal leaf width and terminal leaf ratio) of a varietal collection composed by 21 pistachio cultivars.Area of study: Worldwide.Material and methods: The unsupervised non-hierarchic clustering technique was adopted to the entire samples of pistachio leaves from k=2 to k=15 for both four morphological variables (i.e., leaf stalk length, terminal leaf length, terminal leaf width and terminal leaf ratio) and three morphological variables (i.e., terminal leaf length, terminal leaf width and terminal leaf ratio).Main results: A classification model only on the three morphological variables (for results of statistical analysis in which the groups resulted to be more separated and different for all the variables), with k= 5 (five groups), was constructed using a non-linear artificial neural network approach. The percentages of bad prediction in both training and testing resulted equal to 0%. The “terminal leaf length” returned the higher impact (44.89%).Research highlights: The contemporary combination of different morphological leaf traits, allowed to create an automatic classification of size classes of great importance for cultivar identification and comparison.

Aim of study: To evaluate the susceptibility of the Rhynchophorus ferrugineus to electric discharges, to eventually use this application in synergy with other methods as part of an integrated control strategy.Area of study: Worldwide.Material and methods: Three different electric voltage intensities were applied to R. ferrugineus to investigate the insect’s susceptibility in both adult and larval stages. The transmission and diffusion of electricity within a portion of the palm tree was tested. In addition, the suitability of containment materials for handling and transportation of plant residues was tested.Main results: The results of adult test showed that the total number of eggs laid per mating couple and the number of larvae born were about 1.5 times higher in the control compared to the electrified samples. The duration of the electric discharge on larvae had a small impact on the mortality rate, while the electric voltage showed a strong impact on mortality (87% for 10 V and 99% for 15 V). The presence of a significant electric current was observed in a viable portion of stem (distance from the source of electricity 10 cm) providing a direct current with 250 V tension. Among the materials tested for the transportation of palm residues, the aluminium net sheet showed the best results in the containment of both larval and adult individuals.Research highlights: Electrocution has proven to be a system which can help in the control of red palm weevil reducing the use of synthetic chemicals.

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.

The evaluation of soil tillage quality parameters, such as cloddiness and surface roughness produced by tillage tools, is based on traditional methods ranging, respectively, from manual or mechanical sieving of ground samples to handheld rulers, non-contact devices or Precision Agriculture technics, such as laser profile meters. The aim of the study was to compare traditional methods of soil roughness and cloddiness assessment (laser profile meter and manual sieving), with light drone RGB 3D imaging techniques for the evaluation of different tillage methods (ploughed, harrowed and grassed). Light drone application was able to replicate the results obtained by the traditional methods, introducing advantages in terms of time, repeatability and analysed surface while reducing the human error during the data collection on the one hand and allowing a labour-intensive field monitoring solution for digital farming on the other. Indeed, the profilometer positioning introduces errors and may lead to false reading due to limited data collection. Future work could be done in order to streamline the data processing operation and so to produce a practical application ready to use and stimulate the adoption of new evaluation indices of soil cloddiness, such as Entropy and the Angular Second Moment (ASM), which seem more suitable than the classic ones to achieved data referred to more extended surfaces.

A stereovision system for the in-field estimation of trees parameters such as height and diameter is proposed. The system includes a specifically developed mobile application for the management and georeferencing of stereo images. Stereo imaging allows the measurement of the distance between two points using triangulation formulas for the extraction of three-dimensional coordinates. The methodology is structured following three phases: training using system calibration through stereovision analysis of known artificial known objects; testing using measurements of standing tree diameters and heights acquired through stereovision system, laser rangefinder (height) and tree calliper (diameter); field application testing using direct height and diameter measurements in natural and urban woods. For this last phase an Android application was developed. The results show that the error between direct measurements and those measured with both stereovision and traditional reference methods (laser rangefinder and tree calliper) were quite low: 6.8 ± 6.6% between direct and laser rangefinder height measurements; 5.8 ± 5.5% between direct and stereovision height measurements; 4.2 ± 3.0% between direct and stereovision diameter measurements. No significant difference was found between the different methods for estimating height and diameter. Around 200 images matched to stereovision acquisitions were acquired and georeferenced using the application.

Asimina triloba (L.) Dunal is the only member of the Annonaceae family growing in temperate zones and in particular it grows wild in the eastern United States, ranging from northern Florida to southern Ontario (Canada) and as far west as eastern Nebraska. In Italy Asimina triloba is frequently grown for ornamental effort in parks and gardens, although the cultivation and subsequent introduction of the fruits on the market could be strategic from an economic point of view to find new high-value crops to replace the old ones that are losing value considering their good source of polyphenols as well as apples, oranges, grapes, and strawberries. In this study, physical and chemical properties quality traits (i.e., color, weight, dry matter, flesh firmness, soluble solid content, anti radical capacity determination, total phenolic content, total flavans content) of six cultivars were investigated during two crop years. In addition, a principal component analysis (PCA) was calculated to evaluate the cultivars similarity and stability during the two crop years. Finally, correlation studies among all traits and CIELab coordinates were also investigated. The results showed significant differences in physical and chemical properties in relation to the cultivar and crop years, highlighting the best and most stable cultivars, as Taylor, from a pomological point of view and NC1, from a nutraceutical point of view. PCA allowed to discriminate across the crop years cultivars that were richer in bioactive compounds and showed better qualitative parameters. These outcomes could increase consumer interest and promote the cultivation of specific Asimina triloba cultivars in Italy.

The basic role of industrial rice milling is the transformation of paddy rice into white rice with good appearance while selecting the best quality grain for human consumption. In Italy, the commercial value of paddy rice is assessed calculating whole and free of defect kernel yield after processing. The determination is performed by laboratories utilizing a benchtop yield machine that carries out the husking and kernel bleaching. The aim of the study is the development of a pilot conveyor belt (grain coulter), based on image analysis, to increase the reliability of laboratory yield estimation (discrimination of paddy and white grains). The tests regard rice grains belonging to 26 different genotypes of rice grown in Sardinia (Italy). The low-cost prototype based on open source technologies that aim to substitute the current subjective estimation made by eye with an industrial like optically based one. The method is based on the image analysis and extracts three main qualitative attributes: shape, size (i.e., Fourier descriptors and basic morphometry) and appearance (color), and the use of a multivariate classification technique (i.e., partial least squares discriminant analysis). The models discriminated samples of paddy or white rice for each genotype considered (26 models) and for all the genotypes considered together (1 model). For all the 27 models, the mean sensitivities and specificities were very high, ranging from 99 to 100%, while the mean classification errors were very low. The mean percentage of correct classification in the test set was equal to 99.99% for the “unique model” (i.e., paddy VS white rice) and 100% for the 26 single genotype models. The proposed system appears to be useful not only for paddy and white rice discrimination but also as a flexible apparatus for analyzing many other agro-food products. Indeed, the algorithm was used on other food products, such as red hot chili peppers for other discrimination purposes.

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.

Background Food security can benefit from the technology's transparency, relatively low transaction costs and instantaneous applications. A blockchain is a distributed database of records in the form of encrypted blocks, or a public ledger of all transactions or digital events that have been executed and shared among participating parties and can be verified at any time in the future. Generally, the robust and decentralized functionality of the blockchain is used for global financial systems, but it can easily be expanded to contracts and operations such as tracking of the global supply chain. In the precision agriculture context, Information and Communications Technology can be further implemented with a blockchain infrastructure to enable new farm systems and e-agriculture schemes. Results The purpose of this review is to show a panorama of the scientific studies (enriched by a terms mapping analysis) on the use of blockchain in the agri-food sector, from both an entirely computational and an applicative point of view. As evidenced by the network analysis, the reviewed studies mainly focused on software aspects (e.g. the architecture and smart contracts). However, some aspects regarding the different blockchain knots (computers always connected to the blockchain network) having the role to store and distribute an updated copy of each block in a food supply-chain, result of crucial importance. Conclusion These technologies appear very promising and rich of great potential showing a good flexibility for applications in several sectors but still immature and hard to apply due to their complexity. © 2019 Society of Chemical Industry.

Plums are climacteric fruits with edible fleshy mesocarp. They belong to different species of the genus Prunus (Rosaceae) and consist of many autochthonous genotypes characterized by particular organoleptic characteristics and nutraceutical potential. In this study, chemical properties (i.e., soluble solid content, pH, titratable acidity, total phenolic content and antioxidant activity) of seventeen plum genotypes (four largely diffused varieties plus one clone and thirteen autochthonous genotypes) were investigated during six crop years. A discrimination among the genotypes, with respect to ripening time, pomological and nutraceutical traits, was obtained applying hierarchical cluster analysis (HCA). In addition, a principal component analysis (PCA) was calculated to evaluate the genotypes similarity and stability during the six crop years. Finally, correlation studies among all traits and crop year’s meteorological conditions were also investigated. The results showed significant differences in chemical properties in relation to the genotype, crop years and ripening time highlighting higher titratable acidity, total phenolic content and antioxidant activity in autochthonous genotypes. Contrary, the largely diffused cultivars (or commercial varieties) showed the highest soluble solid content and the lowest acidity values. HCA and PCA allowed to discriminate the autochthonous plum genotypes ‘Regina Claudia’, ‘Prungaccino di Velletri’ and ‘Prugna di Dro’ considering their phenols amount and stability across the six crop years. In many cases, their concentrations resulted higher than those found in the commercial varieties ‘D’Ente 707’, ‘French Prune’ and ‘Stanley’, maintaining good soluble solid content and titratable acidity ratio (SSC/TA). Finally, hierarchical cluster analyses for SSC and TA traits plotted the samples in relation to ripening time. These results could promote specific autochthonous genotypes, to establish a database that could permit to improve the utilization of specific genetic resources in breeding programs.

Aim of study: For baby-leaf lettuces greenhouse cultivations the absence of weeds is a mandatory quality requirement. One of the most promising and innovative technologies in weed research, is the use of Unmanned Aerial Vehicles (or drones) equipped with acquisition systems. The aim of this study was to provide an estimation of the exact weed amount on baby-sized red lettuce beds using a light drone equipped with an RGB microcamera.Area of study: Trials were performed at specialized organic farm site in Eboli (Salerno, Italy), under polyethylene multi-tunnel greenhouse.Material and methods: The RGB images acquired were processed with specific algorithms distinguishing weeds from crop yields, estimating the weeds covered surface and the severity of weed contamination in terms of biomass. A regression between the percentage of the surface covered by weed (with respect to the image total surface) and the weight of weed (with respect to the total harvested biomass) was calculated.Main results: The regression between the total cover values of the 25 calibration images and the total weight measured report a significant linear correlation. Digital monitoring was able to capture with accuracy the highly variable weed coverage that, among the different grids positioned under real cultivation conditions, was in the range 0-16.4% of the total cultivated one.Research highlights: In a precision weed management context, with the aim of improving management and decreasing the use of pesticides, this study provided an estimation of the exact weed amount on baby-sized red lettuce beds using a light drone.

The processes of printing foodstuffs or imaging on an unconventional surface deserve extra attention in comparison with conventional techniques especially in relation with the substrate interested that will or should say a lot about the concept behind the design. Indeed, while printing on food, designers are required to consider carefully both, the appropriate edible inks and the production processes. Moreover, printing on food packaging requires almost as much care. However, the concept of food printing represents nowadays a new frontier in food processing and industry to realize new food products of complex shapes and colour and with particular mixtures. At the moment, many current research projects and products related to food printing are being developed. In 2011, a European Cooperation in Science and Technology action named "New possibilities for print media and packaging, combining print with digital" was created with the aim to promote an interdisciplinary interaction among European research partner with several different research backgrounds. Among the aims of the project, a crucial aspect regards the combination of food expertise with printing using new technologies in order to print on food or food printing. In light of this scenario, image processing and machine control occupy a very important part of the research: a number of programs were written or modified as part of the research into food printing. This review aims to produce an updated analysis on the current developments regarding the technology for food printing and printing on food. In this sense, the work starts giving an overview of the 2D and three-dimensional (3D) printer technology and moves on the food media, edible substrate used by 3D printers and a print of food chapter, related to the substrates used by the 2D printers.

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 &lt, 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&ndash, 373 nm and 594&ndash, 605 nm. These are related to the absorption of phenolic components, carotenoids, chlorophylls, and anthocyanins. The proposed tool allows the assessment of EVOO samples&rsquo, origin and thus could help to preserve the &ldquo, Made in Italy&rdquo, from fraud and sophistication related to its commerce.

The aim of this study is the application of advanced modeling techniques to identify powdery mildew tolerant cultivars and reduce fungicides and energy consumption. The energy savings resulting from the increased efficiency of the use of fungicides is an innovative aspect investigated within the project AGROENER researching on energy efficiency. In this preliminary study, investigations through phenotyping methods could represent a potential solution, especially if they are used in combination with tools and algorithms able to extract and convert a large amount of data. Twelve different grapevine cultivars were tested. The construction of an artificial model, characterized by absolute optima of response to a pathogen (i.e., low values of disease incidence and severity and first day of the pathogen appearance), allowed us to cover the potential variability of a real dataset. To identify the cultivars that tolerate powdery mildew the most, two Soft Independent Modeling of Class Analogy (SIMCA) models were built. The modeling efficiencies, indicated by sensitivity value, were equal to 100%. These statistical multivariate classifications identified some of these tolerant cultivars, as the best responding to the pathogen.