Nowadays, the field of microbiology is undergoing a revolutionary change due to the emergence of Artificial Intelligence (AI). AI is being used to analyze massive data in a predictable form, about the behavior of microorganisms, to solve microbial classification-related problems, exploring the interaction between microorganisms and the surrounding environment. It also helps to extract novel microbial metabolites which have been used in various fields like medical, food and agricultural industries. As the pace of innovation in Microbiology is accelerating, the use of AI in these industries will be beneficial. AI will not only show its extraordinary potential in expanding the market of antibiotics, food, and agriculture but also offer an eco-friendly, safer, and profitable solution to the respective industries. It would be challenging to search out specific features and discuss future research on AI in microbiology with a wide perspective. - Uncovering extended functions of AI in Microbiology. - Production and development of novel drug targets through AI. - Challenges for using and selecting appropriate AI tools in health, agriculture and food sector

Plant phenotyping (PP) describes the physiological and biochemical properties of plants affected by both genotypes and environments. It is an emerging research field that is assisting the breeding and cultivation of new crop varieties to be more productive and resilient to challenging environments. Precision agriculture (PA) uses sensing technologies to observe crops and then manage them optimally to ensure that they grow in healthy conditions, have maximum productivity, and have minimal negative effects on the environment. Traditionally, the observation of plant traits heavily relies on human experts which is labor intensive, time-consuming, and subjective. Automatic crop traits measurement in PP and PA are two different fields, but they share the same sensing and data processing technologies in many respects. Recently, driven by computer and sensor technologies, machine vision (MV) and machine learning (ML) have contributed to accurate, high-throughput, and nondestructive plant phenotyping and precision agriculture. However, these technologies are still in their infant stage and there are many challenges and questions related to them that still need to be addressed. The goal of this Research Topic is to provide a platform to share the latest research results on the application of MV and ML for PP and PA. It aims to highlight cutting-edge technologies, bottle-necks, and future research directions for MV and ML in crop breeding, crop cultivation, disease management, weed control, and pest control.

Dynamic monitoring of crop phenotypic traits (e.g., LAI, plant height, biomass, nitrogen, yield et al.) is essential for exploring crop growth patterns, breeding new varieties, and determining optimized strategies for crop management. Traditional methods for determining crop phenotypic traits are mainly based on field sampling, handheld instrument measurement, and mechanized high-throughput platforms, which are time-consuming, and have low efficiency and incomplete spatial coverage. The development of crop science requires more rapid and accurate access to field-based crop phenotypes. Remote sensing provides a novel solution to quantify crop structural and functional traits in a timely, rapid, non-invasive and efficient manner. With the development of burgeoning remote sensing sensors and diversified algorithms, a range of crop phenotypic traits have been determined, including morphological parameters, spectral and textural characteristics, physiological traits, and responses to abiotic/biotic stresses in different environments. In addition, research advances in varying disciplines beyond agricultural sciences, such as engineering, computer science, molecular biology, and bioinformatics, have brought new opportunities for further development of remote sensing-based methods and technologies to gain more quantitative information on crop structure and function in complex environments

Plant diseases and pests cause significant losses to farmers and threaten food security worldwide. Monitoring the growing conditions of crops and detecting plant diseases is critical for sustainable agriculture. Traditionally, crop inspection has been carried out by people with expert knowledge in the field. However, regarding any activity carried out by humans, this activity is prone to errors, leading to possible incorrect decisions. Innovation is, therefore, an essential fact of modern agriculture. In this context, deep learning has played a key role in solving complicated applications with increasing accuracy over time, and recent interest in this type of technology has prompted its potential application to address complex problems in agriculture, such as plant disease and pest recognition. Although substantial progress has been made in the area, several challenges still remain, especially those that limit systems to operate in real-world scenarios.

Digital agriculture is an emerging concept of modern farming that refers to managing farms using modern Engineering, Information and Communication Technologies (EICT) aiming at increasing the overall efficiency of agricultural production, improving the quantity and quality of products, and optimizing the human labor required and natural resource consumption in operations. This encyclopedia is designed to collect the summaries of knowledge on as many as subjects or aspects relevant to ECIT for digital agriculture, present such knowledge in entries, and arrange them alphabetically by articles titles. Springer Major Reference Works platform offers Live Update capability. Our reference work takes full advantage of this feature, which allows for continuous improvement or revision of published content electronically. The Editorial Board Dr. Irwin R. Donis-Gonzalez, University of California Davis, Dept. Biological and Agricultural Engineering, Davis, USA (Section: Postharvest Technologies) Prof. Paul Heinemann, Pennsylvania State University, Department Head of Agricultural and Biological Engineering, PA, USA (Section: Technologies for Crop Production) Prof. Manoj Karkee, Washington State University, Center for Precision and Automated Agricultural Systems, Washington, USA (Section: Robotics and Automation Technologies) Prof. Minzan Li, China Agricultural University, Beijing, China (Section: Precision Agricultural Technologies) Prof. Dikai Liu, University of Technology Sydney (UTS),Faculty of Engineering & Information Technologies, Broadway NSW, Australia (Section: AI, Information and Communication Technologies) Prof. Tomas Norton, University of Leuven, Dept. of Biosystems, Heverlee Leuven, Belgium (Section: Technologies for Animal and Aquatic Production) Dr. Manuela Zude-Sasse, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Precision Horticulture, Potsdam, Germany (Section: Engineering and Mechanization Technologies)

Over the past century, mechanization has been an important means for optimizing resource utilization, improving worker health and safety and reducing labor requirements in farming while increasing productivity and quality of 4F (Food, Fuel, Fiber, Feed). Recognizing this contribution, agricultural mechanization was considered as one of the top ten engineering achievements of 20th century by the National Academy of Engineering. Accordingly farming communities have adopted increasing level of automation and robotics to further improve the precision management of crops (including input resources), increase productivity and reduce farm labor beyond what has been possible with conventional mechanization technologies. It is more important than ever to continue to develop and adopt novel automation and robotic solutions into farming so that some of the most complex agricultural tasks, which require huge amount of seasonal labor such as fruit and vegetable harvesting, could be automated while meeting the rapidly increasing need for 4F. In addition, continual innovation in and adoption of agricultural automation and robotic technologies is essential to minimize the use of depleting resources including water, minerals and other chemicals so that sufficient amount of safe and healthy food can be produced for current generation while not compromising the potential for the future generation. This book aims at presenting the fundamental principles of various aspects of automation and robotics as they relate to production agriculture (the branch of agriculture dealing with farming operations from field preparation to seeding, to harvesting and field logistics). The building blocks of agricultural automation and robotics that are discussed in the book include sensing and machine vision, control, guidance, manipulation and end-effector technologies. The fundamentals and operating principles of these technologies are explained with examples from cutting-edge research and development currently going on around the word. This book brings together scientists, engineers, students and professionals working in these and related technologies to present their latest examples of agricultural automation and robotics research, innovation and development while explaining the fundamentals of the technology. The book, therefore, benefits those who wish to develop novel agricultural engineering solutions and/or to adopt them in the future. .

This book provides a comprehensive overview of plant omics and big data in the fields of plant and crop biology. It discusses each omics layer individually, including genomics, transcriptomics, proteomics, and covers model and non-model species. In a section on advanced topics, it considers developments in each specialized domain, including genome editing and enhanced breeding strategies (such as genomic selection and high-throughput phenotyping), with the aim of providing tools to help tackle global food security issues. The importance of online resources in big data biology are highlighted in a section summarizing both wet- and dry-biological portals. This section introduces biological resources, datasets, online bioinformatics tools and approaches that are in the public domain. This book is for students, engineers, researchers and academics in plant biology, genetics, biotechnology and bioinformatics.