Artificial Intelligence and Big Data Analytics in Vineyards: A Review

Advances in remote-sensing, sensor and robotic technology, machine learning, and artificial intelligence (AI) – smart algorithms that learn from patterns in complex data or big data - are rapidly transforming agriculture. This presents huge opportunities for sustainable viticulture, but also many challenges. This chapter provides a state-of-the-art review of the benefits and challenges of AI and big data, highlighting work in this domain being conducted around the world. A way forward, that incorporates the expert knowledge of wine-growers (i.e. human-in-the-loop) to augment the decision-making guidance of big data and automated algorithms, is outlined. Future work needs to explore the coupling of expert systems to AI models and algorithms to increase both the usefulness of AI, its benefits, and its ease of implementation across the vitiviniculture value-chain

Within the skin: Grape berries during the mature stages of ripening

A systems biology approach was used to investigate berry skins of three red- (Cabernet Sauvignon, Merlot, Pinot Noir) and two white-skinned (Chardonnay, Semillon) wine grape cultivars. Identical sample aliquots were analyzed for transcripts by a grapevine whole genome oligonucleotide microarray and RNAseq technologies, proteins by nano-liquid chromatography-mass spectroscopy, and metabolites by gas chromatography-mass spectroscopy and liquid chromatography-mass spectroscopy. Principal components analysis of each of five Omic technologies predicted similar variance between cultivars. Comparison of RNAseq and microarray data revealed a strong Pearson’s correlation (0.80), but concordance of protein with transcript data was low with a Pearson’s correlation of 0.27 and 0.24 for the RNAseq and microarray data, respectively. Integration of metabolite with protein and transcript data produced an expected model of phenylpropanoid biosynthesis, distinguishing red from white grapes, yet, provided detail of individual cultivar differences. The integration of multiple high-throughput Omic datasets revealed complex biochemical variation amongst five cultivars of an ancient and economically important crop species. Grape berry ripening occurs in the late stages of development with increases in sugar, changes in color, and decreases in malate concentration. In the final stages of ripening, fruit flavors and volatile aromas increase to signal readiness for seed dispersal. To identify the common transcriptional changes in the late stages of berry development in multiple grape cultivars, the transcriptomic responses of the berry skins of 7 cultivars of grapes that were grown in the same vineyard were determined using RNAseq at four different °Brix levels (20 to 26 °Brix). The abundance of thousands of transcripts changed significantly in the late stages of berry development. Gene set enrichment analysis of functional Gene Ontology terms provided evidence for a complex interplay of many gene ontology categories including those involved in the circadian clock, postembryonic development, photosynthesis, hormone signaling, reactive oxygen species (ROS), DNA methylation and transcriptional regulation. There were 809 transcription factors (TF) differentially expressed with increasing ˚Brix (~4% of all transcripts and ~32% of all TF), belonging to 81 families, including the C3H, MYB, AP2/ERF and bHLH families. Our analyses indicate that the circadian clock and epigenetic modification are major factors regulating transcription in mature berries.Finally, pathogenesis-related proteins that accumulated in skins of three red-skinned and two white-skinned cultivars: Cabernet Sauvignon, Merlot, Pinot Noir, Chardonnay and Semillon, were characterized in silico, using protein and transcript data. Large amounts of identified proteins were classified as pathogenesis-related in berry skins, more so than what was previously observed in shoot tips. Several PR-families had numerous protein members in skins, which maybe a tissue specific occurrence. The transcript abundance was well correlated to the protein abundance in thaumatins of PR-05, but not so in the L-ascorbate peroxidases of PR-09. Haze-forming proteins, while well represented, did not accumulate with more specificity in the white cultivars and were mostly higher in the red cultivar, Pinot Noir. Large accumulations of PR-proteins in skins at harvest provide support for a prolonged and possibly a constitutive defense mechanism that protects a maturing seed within the berry

A survey of semantic web technology for agriculture.

ABSTRACT. Semantic web technologies have become a popular technique to apply meaning to unstructured data. They have been infrequently applied to problems within the agricultural domain when compared to complementary domains. Despite this lack of application, agriculture has a large number of semantic resources that have been developed by large NGOs such as the Food and Agriculture Organization (FAO). This survey is intended to motivate further research in the application of semantic web technologies for agricultural problems, by making available a self contained reference that provides: a comprehensive review of preexisting semantic resources and their construction methods, data interchange standards, as well as a survey of the current applications of semantic web technologies

Safety-Security Co-Engineering Framework

Executive Summary:The advantages of a model-based approach for safety have been clear for many years now. However, security analysis is typically less formal and more ad-hoc; it may involve systematic processes but these are not generally tied into a formal model-based development and analysis process in the same way that safety can be.Task 4.3 of the SESAME project, Safety/Security Co-Engineering, sets out to remedy this by investigating a holistic co-engineering approach that integrates both of these different concerns.In this report we therefore present a combined safety/security co-engineering framework based on the ODE, the metamodel that serves as a basis for the EDDI dependability management concept. The ODE acts as a common ontology for both safety and security, establishing equivalencies between key concepts and allowing joint analyses to take place in which failures can be incorporated into security analysis and attacks into safety analysis. The combined results indicate the causes and consequences of hazardous events regardless of whether they originate from safety or security issues, and the same risk estimation applies to them all. While developed for design-time usage, this framework paves the way for the generation of combined safety/security artefacts at runtime as well. The common approach means that specification of requirements, event monitors, diagnostic engines, and responses/actions can take advantage of both safety and security information stored in the design-time models.To demonstrate the approach, we apply it to a drone-based case study derived from two of the SESAME use cases

Towards the Internet of Smart Trains: A Review on Industrial IoT-Connected Railways

[Abstract] Nowadays, the railway industry is in a position where it is able to exploit the opportunities created by the IIoT (Industrial Internet of Things) and enabling communication technologies under the paradigm of Internet of Trains. This review details the evolution of communication technologies since the deployment of GSM-R, describing the main alternatives and how railway requirements, specifications and recommendations have evolved over time. The advantages of the latest generation of broadband communication systems (e.g., LTE, 5G, IEEE 802.11ad) and the emergence of Wireless Sensor Networks (WSNs) for the railway environment are also explained together with the strategic roadmap to ensure a smooth migration from GSM-R. Furthermore, this survey focuses on providing a holistic approach, identifying scenarios and architectures where railways could leverage better commercial IIoT capabilities. After reviewing the main industrial developments, short and medium-term IIoT-enabled services for smart railways are evaluated. Then, it is analyzed the latest research on predictive maintenance, smart infrastructure, advanced monitoring of assets, video surveillance systems, railway operations, Passenger and Freight Information Systems (PIS/FIS), train control systems, safety assurance, signaling systems, cyber security and energy efficiency. Overall, it can be stated that the aim of this article is to provide a detailed examination of the state-of-the-art of different technologies and services that will revolutionize the railway industry and will allow for confronting today challenges.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; ED431C 2016-045Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; ED341D R2016/012Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; ED431G/01Agencia Estatal de Investigación (España); TEC2013-47141-C4-1-RAgencia Estatal de Investigación (España); TEC2015-69648-REDCAgencia Estatal de Investigación (España); TEC2016-75067-C4-1-

Selection of candidate genes controlling veraison time in grapevine through integration of meta-QTL and transcriptomic data

Background High temperature during grape berry ripening impairs the quality of fruits and wines. Veraison time, which marks ripening onset, is a key factor for determining climatic conditions during berry ripening. Understanding its genetic control is crucial to successfully breed varieties more adapted to a changing climate. Quantitative trait loci (QTL) studies attempting to elucidate the genetic determinism of developmental stages in grapevine have identified wide genomic regions. Broad scale transcriptomic studies, by identifying sets of genes modulated during berry development and ripening, also highlighted a huge number of putative candidates. Results With the final aim of providing an overview about available information on the genetic control of grapevine veraison time, and prioritizing candidates, we applied a meta-QTL analysis for grapevine phenology-related traits and checked for co-localization of transcriptomic candidates. A consensus genetic map including 3130 markers anchored to the grapevine genome assembly was compiled starting from 39 genetic maps. Two thousand ninety-three QTLs from 47 QTL studies were projected onto the consensus map, providing a comprehensive overview about distribution of available QTLs and revealing extensive co-localization especially across phenology related traits. From 141 phenology related QTLs we generated 4 veraison meta-QTLs located on linkage group (LG) 1 and 2, and 13 additional meta-QTLs connected to the veraison time genetic control, among which the most relevant were located on LG 14, 16 and 18. Functional candidates in these intervals were inspected. Lastly, taking advantage of available transcriptomic datasets, expression data along berry development were integrated, in order to pinpoint among positional candidates, those differentially expressed across the veraison transition. Conclusion Integration of meta-QTLs analysis on available phenology related QTLs and data from transcriptomic dataset allowed to strongly reduce the number of candidate genes for the genetic control of the veraison transition, prioritizing a list of 272 genes, among which 78 involved in regulation of gene expression, signal transduction or development

Transforming scientific research and development in precision agriculture : the case of hyperspectral sensing and imaging : a thesis presented in partial fulfilment of the requirements for the degree of Doctor in Philosophy in Agriculture at Massey University, Manawatū, New Zealand. EMBARGOED until 30 September 2023.

Embargoed until 30 September 2023There has been increasing social and academic debate in recent times surrounding the arrival of agricultural big data. Capturing and responding to real world variability is a defining objective of the rapidly evolving field of precision agriculture (PA). While data have been central to knowledge-making in the field since its inception in the 1980s, research has largely operated in a data-scarce environment, constrained by time-consuming and expensive data collection methods. While there is a rich tradition of studying scientific practice within laboratories in other fields, PA researchers have rarely been the explicit focal point of detailed empirical studies, especially in the laboratory setting. The purpose of this thesis is to contribute to new knowledge of the influence of big data technologies through an ethnographic exploration of a working PA laboratory. The researcher spent over 30 months embedded as a participant observer of a small PA laboratory, where researchers work with nascent data rich remote sensing technologies. To address the research question: “How do the characteristics of technological assemblages affect PA research and development?” the ethnographic case study systematically identifies and responds to the challenges and opportunities faced by the science team as they adapt their scientific processes and resources to refine value from a new data ecosystem. The study describes the ontological characteristics of airborne hyperspectral sensing and imaging data employed by PA researchers. Observations of the researchers at work lead to a previously undescribed shift in the science process, where effort moves from the planning and performance of the data collection stage to the data processing and analysis stage. The thesis develops an argument that changing data characteristics are central to this shift in the scientific method researchers are employing to refine knowledge and value from research projects. Importantly, the study reveals that while researchers are working in a rapidly changing environment, there is little reflection on the implications of these changes on the practice of science-making. The study also identifies a disjunction to how science is done in the field, and what is reported. We discover that the practices that provide disciplinary ways of doing science are not established in this field and moments to learn are siloed because of commercial constraints the commercial structures imposed in this case study of contemporary PA research

Disruptive Technologies in Agricultural Operations: A Systematic Review of AI-driven AgriTech Research

YesThe evolving field of disruptive technologies has recently gained significant interest in various industries, including agriculture. The fourth industrial revolution has reshaped the context of Agricultural Technology (AgriTech) with applications of Artificial Intelligence (AI) and a strong focus on data-driven analytical techniques. Motivated by the advances in AgriTech for agrarian operations, the study presents a state-of-the-art review of the research advances which are, evolving in a fast pace over the last decades (due to the disruptive potential of the technological context). Following a systematic literature approach, we develop a categorisation of the various types of AgriTech, as well as the associated AI-driven techniques which form the continuously shifting definition of AgriTech. The contribution primarily draws on the conceptualisation and awareness about AI-driven AgriTech context relevant to the agricultural operations for smart, efficient, and sustainable farming. The study provides a single normative reference for the definition, context and future directions of the field for further research towards the operational context of AgriTech. Our findings indicate that AgriTech research and the disruptive potential of AI in the agricultural sector are still in infancy in Operations Research. Through the systematic review, we also intend to inform a wide range of agricultural stakeholders (farmers, agripreneurs, scholars and practitioners) and to provide research agenda for a growing field with multiple potentialities for the future of the agricultural operations