WECIA Graph: Visualization of Classification Performance Dependency on Grayscale Conversion Setting

Grayscale conversion is a popular operation performed within image pre-processing of many computer vision systems, including systems aimed at generic object categorization. The grayscale conversion is a lossy operation. As such, it can signicantly in uence performance of the systems. For generic object categorization tasks, a weighted means grayscale conversion proved to be appropriate. It allows full use of the grayscale conversion potential due to weighting coefficients introduced by this conversion method. To reach a desired performance of an object categorization system, the weighting coefficients must be optimally setup. We demonstrate that a search for an optimal setting of the system must be carried out in a cooperation with an expert. To simplify the expert involvement in the optimization process, we propose a WEighting Coefficients Impact Assessment (WECIA) graph. The WECIA graph displays dependence of classication performance on setting of the weighting coefficients for one particular setting of remaining adjustable parameters. We point out a fact that an expert analysis of the dependence using the WECIA graph allows identication of settings leading to undesirable performance of an assessed system

Grapes and Wine

Grape and Wine is a collective book composed of 18 chapters that address different issues related to the technological and biotechnological management of vineyards and winemaking. It focuses on recent advances, hot topics and recurrent problems in the wine industry and aims to be helpful for the wine sector. Topics covered include pest control, pesticide management, the use of innovative technologies and biotechnologies such as non-thermal processes, gene editing and use of non-Saccharomyces, the management of instabilities such as protein haze and off-flavors such as light struck or TCAs, the use of big data technologies, and many other key concepts that make this book a powerful reference in grape and wine production. The chapters have been written by experts from universities and research centers of 9 countries, thus representing knowledge, research and know-how of many regions worldwide

Imagerie radar en ondes millimétriques appliquée à la viticulture

Avec l’expansion des exploitations agricoles, le principe d’homogénéité du rendement (céréales, fruits…) devient de moins en moins pertinent. Ce phénomène de variabilité spatiale implique des conséquences économiques et environnementales avec le développement de nouveaux concepts agricoles comme les « site-specific management » (gestion spécifique des parcelles). Les traitements tels que les fertilisants, les intrants et autres pesticides doivent être utilisés de manière différente en les appliquant au bon endroit, à la bonne période et au bon taux. Cette nouvelle façon de penser l’agriculture fait partie de l’agriculture de précision (PA) et se concentre en quatre domaines technologiques : (i) la télédétection, (ii) la navigation et guidage, (iii) la gestion des données et (iv) les technologies à taux variable. Initiée à la fin des années 1990, la viticulture de précision (PV) est une branche particulière de la PA, caractérisée par des problématiques spécifiques à la viticulture. Les travaux effectués durant cette thèse entrent dans le cadre de la télédétection (ou détection proche) appliquée à la PV. Ils se focalisent sur une nouvelle méthode d’estimation de la quantité de grappes (masse ou volume) directement sur les plants de vignes. Pouvoir estimer le rendement des vignes plusieurs semaines avant la récolte offre de nombreux avantages avec des impacts économiques et qualitatifs, avec par exemple : (i) l’amélioration du rapport rendement/qualité en supprimant au plut tôt une partie de la récolte, (ii) l’optimisation des ressources humaines et la logistique à la récolte, (iii) un remboursement le plus équitable par les assurances en cas d’intempéries qui endommageraient les pieds de vignes. La méthode proposée ici repose sur l’imagerie microondes (à 24GHz ou des fréquences plus élevées) générée par un radar FM-CW. Elle implique la mise en place d’un système d’interrogation intra-parcellaire « pied par pied » à distance basé au sol, et en particulier : (i) l’évaluation de la précision des mesures et les limites du système, (ii) le développement d’algorithmes spécifiques pour l’analyse de données tridimensionnelles, (iii) la construction d’estimateurs pour retrouver le volume des grappes, et finalement (iv) l’analyse des données recueillies pendant les campagnes de mesures. Dû au caractère saisonnier des récoltes, les mesures sont en premier lieu effectuées sur des cibles canoniques, des charges variables et des capteurs passifs en laboratoire. Pour mettre en avant la flexibilité de cette interrogation radar, le même système est utilisé en parallèlement dans le cadre du projet régional PRESTIGE, pour compter à distance le nombre de pommes présentes sur les pommiers en verger. Ces travaux ont été financés par l’entreprise Ovalie-Innovation et l’ANRT (Agence Nationale de la Recherche Technologique)

ABC: Adaptive, Biomimetic, Configurable Robots for Smart Farms - From Cereal Phenotyping to Soft Fruit Harvesting

Currently, numerous factors, such as demographics, migration patterns, and economics, are leading to the critical labour shortage in low-skilled and physically demanding parts of agriculture. Thus, robotics can be developed for the agricultural sector to address these shortages. This study aims to develop an adaptive, biomimetic, and configurable modular robotics architecture that can be applied to multiple tasks (e.g., phenotyping, cutting, and picking), various crop varieties (e.g., wheat, strawberry, and tomato) and growing conditions. These robotic solutions cover the entire perception–action–decision-making loop targeting the phenotyping of cereals and harvesting fruits in a natural environment. The primary contributions of this thesis are as follows. a) A high-throughput method for imaging field-grown wheat in three dimensions, along with an accompanying unsupervised measuring method for obtaining individual wheat spike data are presented. The unsupervised method analyses the 3D point cloud of each trial plot, containing hundreds of wheat spikes, and calculates the average size of the wheat spike and total spike volume per plot. Experimental results reveal that the proposed algorithm can effectively identify spikes from wheat crops and individual spikes. b) Unlike cereal, soft fruit is typically harvested by manual selection and picking. To enable robotic harvesting, the initial perception system uses conditional generative adversarial networks to identify ripe fruits using synthetic data. To determine whether the strawberry is surrounded by obstacles, a cluster complexity-based perception system is further developed to classify the harvesting complexity of ripe strawberries. c) Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, the platform’s action system can coordinate the arm to reach/cut the stem using the passive motion paradigm framework, as inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit with a mean error of less than 3 mm, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented. Although this thesis focuses on strawberry harvesting, ongoing research is heading toward adapting the architecture to other crops. The agricultural food industry remains a labour-intensive sector with a low margin, and cost- and time-efficiency business model. The concepts presented herein can serve as a reference for future agricultural robots that are adaptive, biomimetic, and configurable

Application of Analytical Chemistry to Foods and Food Technology

The application of analytical chemistry to the food sector allows the determination of the chemical composition of foods and the properties of their constituents, contributing to the definition of their nutritional and commodity value. Furthermore, it is possible to study the chemical modifications that food constituents undergo as a result of the treatments they undergo (food technology). Food analysis, therefore, allows us not only to determine the quality of a product or its nutritional value, but also to reveal adulterations and identify the presence of xenobiotic substances potentially harmful to human health. Furthermore, some foods, especially those of plant origin, contain numerous substances with beneficial effects on health. While these functional compounds can be obtained from a correct diet, they can also be extracted from food matrices for the formulation of nutraceutical products or added to foods by technological or biotechnological means for the production of functional foods. On the other hand, the enormous growth of the food industry over the last 50 years has broadened the field of application of analytical chemistry to encompass not only food but also food technology, which is fundamental for increasing the production of all types of food

Gestione sostenibile del vigneto mediante Data Science e Big Data Management

Negli ultimi anni, la ricerca in ambito viticolo (Vitis vinifera L.) è stata notevolmente influenzata dalla necessità duplice di rispondere alla crescente domanda di prodotto ad elevati standard qualitativi e a mediare criticità derivanti dagli effetti del cambiamento climatico. Alla base della mediazione di tali fattori risulta fondamentale una ricalibrazione della gestione del vigneto, spostandosi da un approccio convenzionale che prevede una sua gestione come unità omogenea, verso uno che tenga in considerazione le sue discontinuità spaziali legate alle peculiarità pedoclimatiche e alle variabili biotiche, le quali, avendo riflessi eterogenei sul ciclo biologico della vite, determinano un uso non sempre razionale delle risorse. Emerge così l’esigenza di un rinnovamento dei sistemi di monitoraggio, che unisca il trasferimento tecnologico alle conoscenze scientifiche pregresse, verso usi mirati e calibrati sull'ambito viticolo, attraverso i quali poter attuare strategie previsionali che permettano la salvaguardia degli equilibri ecologici pur mantenendo inalterato il livello di produttività e qualità. Nello scenario della moderna viticoltura, il flusso di dati estratti dal campo proviene da fonti diverse tra loro. Si tratta di informazioni relative a diversi aspetti, che vanno dalla caratterizzazione della fisiologia delle piante, alla natura del contesto pedoclimatico fino a dati relativi alla gestione colturale: concimazione, irrigazione, potatura. Appare chiaro che, oltre a fornire grandi opportunità di indagine del sistema vigneto, questa abbondanza e diversificazione dei dati pone di fronte l’onere di dover gestire moli di dati spesso non strutturati che, pur avendo un grande valore intrinseco, richiedono di essere analizzate e sintetizzate affinché possano essere utilizzate in maniera proficua per la gestione agronomica del vigneto. Questi, infatti, se slegati dal contesto o se letti individualmente, danno spesso informazioni assai scarse, difficilmente leggibili, poco legate alla realtà applicativa e che in alcuni casi portano ad errori. Lo scopo dell’analisi di tali dati (chiamati non a caso Big Data) è quindi quello di individuare correlazioni, tendenze, pattern che si ripetono secondo schemi più o meno intuitivi, dinamiche di interdipendenza nascoste o comunque non facilmente identificabili, al fine di elaborare modelli simulativi costantemente aggiornati sulla base della biodiversità del panorama viticolo e dei contesti pedoclimatici, che consentano decisioni basate su dati più strettamente connessi alla realtà di campo anziché sulla semplice speculazione empirica o su serie storiche, con relativi vantaggi gestionali. Gli obiettivi della tesio sono stati quelli di: (i) sviluppare metodologie per l'acquisizione e l'analisi di immagini RGB dal contesto vigneto ed estrarre e analizzare i dati ad esse relativi per meglio comprendere le criticità, i vantaggi e le prospettive applicative di tale tecnologia; (ii) sviluppare modelli per la stima dello stato idrico della vite basati sull'analisi spazio-temporale di dati relativi al sistema pianta-suolo-atmosfera, per acquisire utili informazioni sulla gestione dell'irrigazione; (iii) applicare le metodologie e i modelli di simulazione sviluppati su casi studio reali per valutarne le prestazioni, confrontandoli con metodi esistenti, e analizzando la loro accuratezza nel fornire informazioni per la gestione sostenibile del vigneto

Effects of Irrigation Rate and Planting Density on Maize Yield and Water Use Efficiency in the Temperate Climate of Serbia

Scarce water resources severely limit maize (Zea mays L.) cultivation in the temperate regions of northern Serbia. A two-year field experiment was conducted to investigate the effects of irrigation and planting density on yield and water use efficiency in temperate climate under sprinkler irrigation. The experiment included five irrigation treatments (full irrigated treatment – FIT; 80% FIT, 60% FIT, 40% FIT, and rainfed) and three planting densities (PD1: 54,900 plants ha–1 ; PD2: 64,900 plants ha–1; PD3: 75,200 plants ha–1). There was increase in yield with the irrigation (1.05–80.00%) as compared to the rainfed crop. Results showed that decreasing irrigation rates resulted in a decrease in yield, crop water use efficiency (WUE), and irrigation water use efficiency (IWUE). Planting density had significant effects on yield, WUE, and IWUE which differed in both years. Increasing planting density gradually increased yield, WUE, and IWUE. For the pooled data, irrigation rate, planting density and their interaction was significant (P < 0.05). The highest two-year average yield, WUE, and IWUE were found for FIT-PD3 (14,612 kg ha–1), rainfed-PD2 (2.764 kg m–3), and 60% FITPD3 (2.356 kg m–3), respectively. The results revealed that irrigation is necessary for maize cultivation because rainfall is insufficient to meet the crop water needs. In addition, if water becomes a limiting factor, 80% FIT-PD3 with average yield loss of 15% would be the best agronomic practices for growing maize with a sprinkler irrigation system in a temperate climate of Serbia

Proceedings of the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008

This volume contains full papers presented at the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008, held in Braga, Portugal, between September 4th and 6th, 2008.FC