wGrapeUNIPD-DL: An open dataset for white grape bunch detection.

National and international Vitis variety catalogues can be used as image datasets for computer vision in viticulture. These databases archive ampelographic features and phenology of several grape varieties and plant structures images (e.g. leaf, bunch, shoots). Although these archives represent a potential database for computer vision in viticulture, plant structure images are acquired singularly and mostly not directly in the vineyard. Localization computer vision models would take advantage of multiple objects in the same image, allowing more efficient training. The present images and labels dataset was designed to overcome such limitations and provide suitable images for multiple cluster identification in white grape varieties. A group of 373 images were acquired from later view in vertical shoot position vineyards in six different Italian locations at different phenological stages. Images were then labelled in YOLO labelling format. The dataset was made available both in terms of images and labels. The real number of bunches counted in the field, and the number of bunches visible in the image (not covered by other vine structures) was recorded for a group of images in this dataset.

Bibliometric Analysis of Trends in Mulberry and Silkworm Research on the Production of Silk and Its By-Products.

Traditionally, sericulture is meant as the agricultural activity of silk production, from mulberry (Morus sp.pl.) cultivation to silkworm (Bombyx mori L.) rearing. The aim of the present work is to analyze the trends and outputs of scientific research on sericulture-related topics during the last two decades, from 2000 to 2020. In this work the authors propose a text-mining analysis of the titles, abstracts and keywords of scientific articles focused on sericulture and available in the SCOPUS database considering the above-mentioned period of time; from this article collection, the 100 most recurrent terms were extracted and studied in detail. The number of publications per year in sericulture-related topics increased from 87 in 2000 to 363 in 2020 (+317%). The 100 most recurrent terms were then aggregated in clusters. The analysis shows how in the last period scientific research, besides the traditional themes of sericulture, also focused on alternative products obtainable from the sericultural practice, as fruits of mulberry trees (increment of +134% of the occurrences in the last five years) and chemical compounds as antioxidants (+233% of occurrences), phenolics (+330% of occurrences) and flavonoids (+274% of occurrences). From these considerations, the authors can state how sericulture is an active and multidisciplinary research field.

Radiative transfer model inversion using high-resolution hyperspectral airborne imagery - Retrieving maize LAI to access biomass and grain yield.

Mapping crop within-field yield variability provide an essential piece of information for precision agriculture applications. Leaf Area Index (LAI) is an important parameter that describes maize growth, vegetation structure, light absorption and subsequently maize biomass and grain yield (GY). The main goal for this study was to estimate maize biomass and GY through LAI retrieved from hyperspectral aerial images using a PROSAIL model inversion and compare its performance with biomass and GY estimations through simple vegetation index approaches. This study was conducted in two separate maize fields of 12 and 20 ha located in north-west Mexico. Both fields were cultivated with the same hybrid. One field was irrigated by a linear pivot and the other by a furrow irrigation system. Ground LAI data were collected at different crop growth stages followed by maize biomass and GY at the harvesting time. Through a weekly/biweekly airborne flight campaign, a total of 19 mosaics were acquired between both fields with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400 to 850 nanometres (nm) at different crop growth stages. The PROSAIL model was calibrated and validated for retrieving maize LAI by simulating maize canopy spectral reflectance based on crop-specific parameters. The model was used to retrieve LAI from both fields and to subsequently estimate maize biomass and GY. Additionally, different vegetation indices were calculated from the aerial images to also estimate maize yield and compare the indices with PROSAIL based estimations. The PROSAIL validation to retrieve LAI from hyperspectral imagery showed a R2 value of 0.5 against ground LAI with RMSE of 0.8 m2/m2. Maize biomass and GY estimation based on NDRE showed the highest accuracies, followed by retrieved LAI, GNDVI and NDVI with R2 value of 0.81, 0.73, 0.73 and 0.65 for biomass, and 0.83, 0.69, 0.73 and 0.62 for GY estimation, respectively. Furthermore, the late vegetative growth stage at V16 was found to be the best stage for maize yield prediction for all studied indices.

A survey of few-shot learning in smart agriculture: developments, applications, and challenges.

With the rise of artificial intelligence, deep learning is gradually applied to the field of agriculture and plant science. However, the excellent performance of deep learning needs to be established on massive numbers of samples. In the field of plant science and biology, it is not easy to obtain a large amount of labeled data. The emergence of few-shot learning solves this problem. It imitates the ability of humans' rapid learning and can learn a new task with only a small number of labeled samples, which greatly reduces the time cost and financial resources. At present, the advanced few-shot learning methods are mainly divided into four categories based on: data augmentation, metric learning, external memory, and parameter optimization, solving the over-fitting problem from different viewpoints. This review comprehensively expounds on few-shot learning in smart agriculture, introduces the definition of few-shot learning, four kinds of learning methods, the publicly available datasets for few-shot learning, various applications in smart agriculture, and the challenges in smart agriculture in future development.

Instability of remote sensing based ecological index (RSEI) and its improvement for time series analysis.

With the rapid development of remote sensing technology, the monitoring of land surface ecological status (LSES) based on remote sensing has made remarkable progress, which has a positive contribution on improving the regional ecological environment and promoting the realization of Sustainable Development Goals (SDGs). Among them, the proposed Remote Sensing-based Ecological Index (RSEI) becomes the most widely used model in the current application of remote sensing-based LSES monitoring due to its complete derived from remote sensing images and no subjective intervention. RSEI is not flawless either, and it still suffers from some uncertainties in its application in multiple scenarios. However, compared to the extensive applied research, work on the instability assessment and improvement of RSEI is particularly scarce and urgently needed. Therefore, in this paper, we analyzed the possible instabilities in the RSEI calculation process and proposed various inversion models to evaluate their accuracy and stability in time-series LSES monitoring. The results indicated that the existing normalized RSEI is relatively stable for the characterization of single-phase LSES, however, there is a high risk in the time-series analysis or cross-regional comparison due to the interference of component extremes. The standard deviation discretized DRSEIs proposed in this paper perform better in both single-phase and long-term dynamics LSES assessments and are more consistent with the real land cover changes. Also, compared with the approach that measures LSES dynamics using time-series regional RSEI mean values, the DRSEIs change detection results can reveal the spatial heterogeneity of regional LSES dynamics more effectively and provide a finer reference for the formulation and implementation of ecological protection policies.

Measuring Comfort Behaviours in Laying Hens Using Deep-Learning Tools.

Image analysis using machine learning (ML) algorithms could provide a measure of animal welfare by measuring comfort behaviours and undesired behaviours. Using a PLF technique based on images, the present study aimed to test a machine learning tool for measuring the number of hens on the ground and identifying the number of dust-bathing hens in an experimental aviary. In addition, two YOLO (You Only Look Once) models were compared. YOLOv4-tiny needed about 4.26 h to train for 6000 epochs, compared to about 23.2 h for the full models of YOLOv4. In validation, the performance of the two models in terms of precision, recall, harmonic mean of precision and recall, and mean average precision (mAP) did not differ, while the value of frame per second was lower in YOLOv4 compared to the tiny version (31.35 vs. 208.5). The mAP stands at about 94% for the classification of hens on the floor, while the classification of dust-bathing hens was poor (28.2% in the YOLOv4-tiny compared to 31.6% in YOLOv4). In conclusion, ML successfully identified laying hens on the floor, whereas other PLF tools must be tested for the classification of dust-bathing hens.

Novel Effects of Leonardite-Based Applications on Sugar Beet.

The present study aimed to explore the effects of foliar application of a leonardite-based product on sugar beet (Beta vulgaris L.) plants grown in the field. The approach concerned the evaluation of the community compositional structure of plant endophytic bacteria through a metabarcoding approach, the expression level of a gene panel related to hormonal metabolism and signaling, and the main sugar beet productivity traits. Results indicated that plants treated with leonardite (dosage of 2,000 ml ha-1, dilution 1:125, 4 mg C l-1) compared with untreated ones had a significant increase (p < 0.05) in (i) the abundance of Oxalicibacterium spp., recognized to be an endophyte bacterial genus with plant growth-promoting activity; (ii) the expression level of LAX2 gene, coding for auxin transport proteins; and (iii) sugar yield. This study represents a step forward to advance our understanding of the changes induced by leonardite-based biostimulant in sugar beet.

Challenges and Tendencies of Automatic Milking Systems (AMS): A 20-Years Systematic Review of Literature and Patents.

Over the last two decades, the dairy industry has adopted the use of Automatic Milking Systems (AMS). AMS have the potential to increase the effectiveness of the milking process and sustain animal welfare. This study assessed the state of the art of research activities on AMS through a systematic review of scientific and industrial research. The papers and patents of the last 20 years (2000-2019) were analysed to assess the research tendencies. The words appearing in title, abstract and keywords of a total of 802 documents were processed with the text mining tool. Four clusters were identified (Components, Technology, Process and Animal). For each cluster, the words frequency analysis enabled us to identify the research tendencies and gaps. The results showed that focuses of the scientific and industrial research areas complementary, with scientific papers mainly dealing with topics related to animal and process, and patents giving priority to technology and components. Both scientific and industrial research converged on some crucial objectives, such as animal welfare, process sustainability and technological development. Despite the increasing interest in animal welfare, this review highlighted that further progress is needed to meet the consumers' demand. Moreover, milk yield is still regarded as more valuable compared to milk quality. Therefore, additional effort is necessary on the latter. At the process level, some gaps have been found related to cleaning operations, necessary to improve milk quality and animal health. The use of farm data and their incorporation on herd decision support systems (DSS) appeared optimal. The results presented in this review may be used as an overall assessment useful to address future research.

A sample of Italian vineyards: Landscape and management parameters dataset.

Agricultural land use plays a critical role in land planning sustainability. Employing a GIS-based decision-making protocol based on spatial and management data represents an appropriate tool for land planning. The Italian vineyards database presented here describes several spatial and management features of 3686 sample vineyards distributed throughout Italy. The dataset is presented as a centroid shapefile with the attribute table. The features were assessed with a GIS-based geospatial analysis. Parameters such as training system and shape of the vineyard block were attributed through visual assessment of Google Earth images. Row spacing, length-width ratio and headland size were determined using QGIS measuring tools. The mean and maximum slope was derived using a 20 m spatial resolution Digital Elevation Model (DEM). This database may help to establish planting criteria of new vineyards which comply with rational and sustainable requirements. Moreover, the dataset could be combined with other agricultural land use data for further analysis of land management. Furthermore, the database could be implemented to support global-scale vineyard management.

Use of multiple indicators to compare sustainability performance of organic vs conventional vineyard management.

The wine sector is paying more attention to sustainable wine production practices, but this topic is highly debated because organic viticulture aims to a reduction of environmental impacts, while conventional viticulture ensures an increase of yield. This work provides an economic and environmental comparison using different indicators whereas no previous studies on viticulture have faced on both aspects of sustainability. Two distinct vineyards within the same case study farm were considered, where conventional and organic viticulture practices were applied for 5 years. For each type of production, we calculated the economic benefit and environmental indicators such as the Water Footprint, Carbon Footprint, and an indicator of environmental performance associated with the vineyard phase ("Vineyard Management" or "Vigneto" indicator part of the Italian VIVA certification framework). This latter considers six sub-indicators investigating pesticides management, fertilizers management, organic matter content, soil compaction, soil erosion, and landscape quality. The multi criteria approach is a novel framework assessing sustainability on vineyard management using environmental indicators from VIVA calculator and the economic aspect. Main results showed that organic management in viticulture can be applied without having economic losses and with the benefit of better preserving the natural capital.

Comparison of Water-focused Life Cycle Assessment and Water Footprint Assessment: The case of an Italian wine.

In recent decades, the debate on how to implement and measure sustainability in food production gained increasing importance and interest for agriculture. In the wine sector, producers are increasingly pursuing sustainable practices, including measures for water preservation from degradation and overuse. But methodologies for assessing and communicating the impacts on water resources need to be understood in detail to guide the selection of the most appropriate management practices, support environmental labelling and promote environmental-friendly products to consumers. This work focuses on the impacts on water resources associated with the production of Italian wine by comparing two methodologies: the Water-focused Life Cycle Assessment and the "Water" indicator included in the Italian "VIVA" certification framework, which is based on the Water Footprint Assessment. The two methodologies address the impact on freshwater consumption and degradation from a life cycle perspective. VIVA is based on a water balance method that reflects a volumetric measure of water consumption, while the LCA-based approach investigates both the freshwater consumption and depletion using different impact indicators. The study goal is to compare the two methodologies to understand how their outcomes can support and improve the management of water-related issues in wine production. One main conclusion is that the WATER indicator within VIVA framework can provide more precise recommendations for the optimal management of water use during the vineyard phase, while LCA approach highlights impact hotspots related to both direct and indirect use of water resources (e.g., it points out the relevant contribution of the bottling stage to different impact indicators). The comparative application of both methodologies can provide useful insights into the water-related impacts of different wine production processes and stages and support a comprehensive assessment of the best management practices, unless the differences in the methodological approaches and goals are well understood by assessors.

On-Barn Pig Weight Estimation Based on Body Measurements by Structure-from-Motion (SfM).

Information on the body shape of pigs is a key indicator to monitor their performance and health and to control or predict their market weight. Manual measurements are among the most common ways to obtain an indication of animal growth. However, this approach is laborious and difficult, and it may be stressful for both the pigs and the stockman. The present paper proposes the implementation of a Structure from Motion (SfM) photogrammetry approach as a new tool for on-barn animal reconstruction applications. This is possible also to new software tools allowing automatic estimation of camera parameters during the reconstruction process even without a preliminary calibration phase. An analysis on pig body 3D SfM characterization is here proposed, carried out under different conditions in terms of number of camera poses and animal movements. The work takes advantage of the total reconstructed surface as reference index to quantify the quality of the achieved 3D reconstruction, showing how as much as 80% of the total animal area can be characterized.

A Feasibility Study on the Use of a Structured Light Depth-Camera for Three-Dimensional Body Measurements of Dairy Cows in Free-Stall Barns.

Frequent checks on livestock's body growth can help reducing problems related to cow infertility or other welfare implications, and recognizing health's anomalies. In the last ten years, optical methods have been proposed to extract information on various parameters while avoiding direct contact with animals' body, generally causes stress. This research aims to evaluate a new monitoring system, which is suitable to frequently check calves and cow's growth through a three-dimensional analysis of their bodies' portions. The innovative system is based on multiple acquisitions from a low cost Structured Light Depth-Camera (Microsoft Kinect™ v1). The metrological performance of the instrument is proved through an uncertainty analysis and a proper calibration procedure. The paper reports application of the depth camera for extraction of different body parameters. Expanded uncertainty ranging between 3 and 15 mm is reported in the case of ten repeated measurements. Coefficients of determination R² > 0.84 and deviations lower than 6% from manual measurements where in general detected in the case of head size, hips distance, withers to tail length, chest girth, hips, and withers height. Conversely, lower performances where recognized in the case of animal depth (R² = 0.74) and back slope (R² = 0.12).

Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone.

Agronomic input and management practices have traditionally been applied uniformly on agricultural fields despite the presence of spatial variability of soil properties and landscape position. When spatial variability is ignored, uniform agronomic management can be both economically and environmentally inefficient. The objectives of this study were to: i) identify optimal N fertilizer rates using an integrated spatio-temporal analysis of yield and site-specific N rate response; ii) test the sensitivity of site specific N management to nitrate leaching in response to different N rates; and iii) demonstrate the environmental benefits of variable rate N fertilizer in a Nitrate Vulnerable Zone. This study was carried out on a 13.6 ha field near the Venice Lagoon, northeast Italy over four years (2005-2008). We utilized a validated crop simulation model to evaluate crop response to different N rates at specific zones in the field based on localized soil and landscape properties under rainfed conditions. The simulated rates were: 50 kg N ha(-1) applied at sowing for the entire study area and increasing fractions, ranging from 150 to 350 kg N ha(-1) applied at V6 stage. Based on the analysis of yield maps from previous harvests and soil electrical resistivity data, three management zones were defined. Two N rates were applied in each of these zones, one suggested by our simulation analysis and the other with uniform N fertilization as normally applied by the producer. N leaching was lower and net revenue was higher in the zones where variable rates of N were applied when compared to uniform N fertilization. This demonstrates the efficacy of using crop models to determine variable rates of N fertilization within a field and the application of variable rate N fertilizer to achieve higher profit and reduce nitrate leaching.

Atomic force microscopy analysis shows surface structure changes in carvacrol-treated bacterial cells.

Carvacrol is a major component in some essential oils such as oregano and thyme and its inhibitory effect on the growth of various microorganisms is well documented. However, the active mechanism of carvacrol, as well as that of other essential oil components, has not yet been fully established and has generally not been well investigated. In this study, the antimicrobial activity of carvacrol against some Gram-positive and Gram-negative food-related bacterial strains was preliminarily verified and the effect of carvacrol on their cell envelope was further investigated by atomic force microscopy analysis. The atomic force microscopy images of the cells treated with carvacrol 3.3 mM for 1 h were analyzed by an appropriate software in order to visualize the effect of the treatment and to determine the values of cell surface roughness and some biometric parameters (cell length and width). The results showed that all microorganisms tested were sensitive to carvacrol both in solid and liquid media. Furthermore, images of cells of all strains treated with carvacrol exhibited appreciable modifications, indicating a change in cell surface structure. Finally, both length and diameter of the microorganisms decreased after contact with carvacrol.