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Yield estimation is an essential preharvest practice among most large-scale farming companies, since it enables the predetermination of essential logistics to be allocated (i.e., transportation means, supplies, labor force, among others). An overestimation may thus incur further costs, whereas an underestimation entails potential crop waste. More interestingly, an accurate yield estimation enables stakeholders to better place themselves in the market. Yet, computer-aided precision farming is set to play a pivotal role in this respect. Kiwifruit represents a major produce in several countries (e.g., Italy, China, New and Zealand). However, up to date, the relevant literature remains short of a complete as well as automatic system for kiwifruit yield estimation. In this paper, we present a fully automatic and noninvasive computer vision system for kiwifruit yield estimation across a given orchard. It consists mainly of an optical sensor mounted on a minitractor that surveys the orchard of interest at a low pace. Afterwards, the acquired images are fed to a pipeline that incorporates image preprocessing, stitching, and fruit counting stages and outputs an estimated fruit count and yield estimation. Experimental results conducted on two large kiwifruit orchards confirm a high plausibility (i.e., errors of 6% and 15%) of the proposed system. The proposed yield estimation solution has been in commercial use for about 2 years. With respect to the traditional manual yield estimation carried out by kiwifruit companies, it was demonstrated to save a significant amount of time and cut down on estimation errors, especially when speaking of large-scale farming.

Perceptual and statistical evidence has highlighted voice characteristics of individuals affected by genetic syndromes that differ from those of normophonic subjects. In this paper, we propose a procedure for systematically collecting such pathological voices and developing AI-based automated tools to support differential diagnosis. Guidelines on the most appropriate recording devices, vocal tasks, and acoustical parameters are provided to simplify, speed up, and make the whole procedure homogeneous and reproducible. The proposed procedure was applied to a group of 56 subjects affected by Costello syndrome (CS), Down syndrome (DS), Noonan syndrome (NS), and Smith–Magenis syndrome (SMS). The entire database was divided into three groups: pediatric subjects (PS; individuals < 12 years of age), female adults (FA), and male adults (MA). In line with the literature results, the Kruskal–Wallis test and post hoc analysis with Dunn–Bonferroni test revealed several significant differences in the acoustical features not only between healthy subjects and patients but also between syndromes within the PS, FA, and MA groups. Machine learning provided a k-nearest-neighbor classifier with 86% accuracy for the PS group, a support vector machine (SVM) model with 77% accuracy for the FA group, and an SVM model with 84% accuracy for the MA group. These preliminary results suggest that the proposed method based on acoustical analysis and AI could be useful for an effective, non-invasive automatic characterization of genetic syndromes. In addition, clinicians could benefit in the case of genetic syndromes that are extremely rare or present multiple variants and facial phenotypes.

Adductor spasmodic dysphonia is a type of adult-onset focal dystonia characterized by involuntary spasms of laryngeal muscles. This paper applied machine learning techniques for the severity assessment of spasmodic dysphonia. To this aim, 7 perceptual indices and 48 acoustical parameters were estimated from the Italian word /a’jwɔle/ emitted by 28 female patients, manually segmented from a standardized sentence and used as features in two classification experiments. Subjects were divided into three severity classes (mild, moderate, severe) on the basis of the G (grade) score of the GRB scale. The first aim was that of finding relationships between perceptual and objective measures with the Local Interpretable Model-Agnostic Explanations method. Then, the development of a diagnostic tool for adductor spasmodic dysphonia severity assessment was investigated. Reliable relationships between G; R (Roughness); B (Breathiness); Spasmodicity; and the acoustical parameters: voiced percentage, F2 median, and F1 median were found. After data scaling, Bayesian hyperparameter optimization, and leave-one-out cross-validation, a k-nearest neighbors model provided 89% accuracy in distinguishing patients among the three severity classes. The proposed methods highlighted the best acoustical parameters that could be used jointly with GRB indices to support the perceptual evaluation of spasmodic dysphonia and provide a tool to help severity assessment of spasmodic dysphonia.

The early detection of neurodevelopmental disorders in newborns is of utmost importance in clinical practice. Recently, to predict the neurodevelopment scores in preterms, Artificial Intelligence (AI) methods have been proposed mainly based on Electroencephalographic (EEG) or heart rate variability (HRV) analysis. In this work, HRV measures of preterm newborns with and without Sepsis are computed and used as input features of AI regression models. The study assesses the reliability of such features in predicting BAYLEY-III scores obtained during the clinical follow-up at 6- and 12-months. Forty-eight preterms (gestational age $27.8{\pm }1.8$ weeks) were involved, 27 of which were diagnosed with Sepsis. HRV analysis was performed on ECG signals recorded at the corrected term age. BAYLEY-III score prediction was implemented, considering HRV features as input predictors of ensemble regression models. Models were validated using the Leave-One-Subject-Out (LOSO) framework. Encouraging results were achieved, with a Mean Absolute Error (MAE) < 5 points for the Sepsis group in the BAYLEY-III cognitive and language scales at 6- and 12-months. Preliminary results suggested that the autonomic nervous system development may be linked to central nervous system maturation. HRV features, and AI regression models could predict alterations that affect the correct neurodevelopment of newborns.

Armillaria genus represents one of the most common causes of chronic root rot disease in woody plants. Prompt recognition of diseased plants is crucial to control the pathogen. However, the current disease detection methods are limited at a field scale. Therefore, an alternative approach is needed. In this study, we investigated the potential of hyperspectral techniques to identify fungi-infected vs. healthy plants of Vitis vinifera. We used the hyperspectral imaging sensor Specim-IQ to acquire leaves’ reflectance data of the Teroldego Rotaliano grapevine cultivar. We analyzed three different groups of plants: healthy, asymptomatic, and diseased. Highly significant differences were found in the near-infrared (NIR) spectral region with a decreasing pattern from healthy to diseased plants attributable to the leaf mesophyll changes. Asymptomatic plants emerged from the other groups due to a lower reflectance in the red edge spectrum (around 705 nm), ascribable to an accumulation of secondary metabolites involved in plant defense strategies. Further significant differences were observed in the wavelengths close to 550 nm in diseased vs. asymptomatic plants. We evaluated several machine learning paradigms to differentiate the plant groups. The Naïve Bayes (NB) algorithm, combined with the most discriminant variables among vegetation indices and spectral narrow bands, provided the best results with an overall accuracy of 90% and 75% in healthy vs. diseased and healthy vs. asymptomatic plants, respectively. To our knowledge, this study represents the first report on the possibility of using hyperspectral data for root rot disease diagnosis in woody plants. Although further validation studies are required, it appears that the spectral reflectance technique, possibly implemented on unmanned aerial vehicles (UAVs), could be a promising tool for a cost-effective, non-invasive method of Armillaria disease diagnosis and mapping in-field, contributing to a significant step forward in precision viticulture.

Es objetivo del trabajo describir el leño de Maytenus vitis-idaea Griseb. y M. viscifolia Griseb. (Celastraceae) y determinar rasgos ecoanatómicos. Las muestras fueron recolectadas del bosque natural en la localidad Guampacha, Sierras de Guasayán, Santiago del Estero, Argentina (distrito chaqueño serrano). Cinco individuos de cada especie fueron seleccionados al azar, obteniéndose una rodaja a 0.3 m. En las descripciones del leño se usó la terminología del Comité de Nomenclatura de IAWA. Los rasgos fundamentales del leño de Maytenus se resumen en: poros pequeños, numerosos, vasos pequeños, miembros de vasos cortos y anillos de crecimiento angostos. El leño de las dos especies estudiadas se caracteriza por presentar porosidad difusa a semicircular, anillos de crecimiento tipo 5b; poros numerosos y pequeños, parénquima axial de distribución apotraqueal en bandas discontinuas de una sola hilera de células, con tendencia a reticulado; escaso paratraqueal unilateral y difuso en agregados, radios heterogéneos; presencia de fibrotraqueidas. Las principales diferencias anatómicas radican en el tipo de poros, fundamentalmente solitarios en M. vitis-idaea y la presencia de radios de mayor longitud en M. viscifolia. Ambas maderas presentan rasgos xerofiticos notorios.Wood anatomy of Maytenus vitis-idaea. y M. viscifolia (Celastraceae). Is objective of this study to describe the wood anatomy of Maytenus vitis-idaea Griseb. and M. viscifolia Griseb. (Celastraceae) and to determine ecoanatomy features. The samples were collected from natural forest in Guampacha, Guasayán, Santiago del Estero, Argentina (Chaco Serrano district). Five individuals were randomly selected; being obtained a track to 0.3 m. IAWA Committee on Nomenclature terminology was used in wood descriptions. Xylem features of Maytenus be summarized as: numerous small pores, short vessel members and growth rings narrow. The wood of the two species were characterized by diffuse to semi-circular porosity, growth rings type 5b, numerous small pores, apotracheal axial parenchyma in discontinuous bands, with a tendency to cross-linked, low paratracheal unilateral and diffuse in aggregates, heterogeneous rays, fibrotracheids present. The main anatomical difference is the predominance of solitary pores in M. vitis-idaea and longest rays in M. viscifolia. Both woods present conspicuous xerophytic features.

Schinopsis, género endémico de Sudamérica, incluye ocho especies que habitan en los bosques xerófilos chaqueños de Argentina, Bolivia, Paraguay y Brasil. Son cuatro las especies nativas de Argentina, dos de gran importancia económica. El objetivo del trabajo es comparar anatómicamente el xilema de: S. lorentzii (Griseb.) Engl., S. balansae Engl. y Schinopsis marginata Engl. Se trabajó con 5 árboles por especie. Se usó la terminología del Comité de Nomenclatura de IAWA y Tortorelli en las descripciones. Se utilizaron los índices de vulnerabilidad y mesomorfía propuestos por Carlquist para determinar el tipo de comportamiento. Se analizó comparativamente los rasgos anatómicos relevantes. Se analizaron estadísticamente cinco variables cuantitativas: frecuencia de poros/mm2 (Fv); diámetro tangencial (Vd) y longitud de los elementos vasculares (VI); altura del radio (Rh). El leño homogéneo, presenta diferencias en las variables cuantitativas como frecuencia, diámetro y longitud de vaso. El rasgo anatómico predominante es la presencia de canales secretores de tanino radiales y vasos del duramen ocluidos por tilosis. Los leños de S. lorentzii y S. marginata mostraron gran afinidad y rasgos mesofiticos con tendencia a xerofíticos; mientras que S. balansae presenta signos mesofíticos.