This book compiles a set of original and innovative papers included in the Special Issue on UAVs for vegetation monitoring, which proves the wide scope of UAVs in very diverse vegetation applications, both in agricultural and forestry scenarios, ranging from the characterization of relevant vegetation features to the detection of plant or crop stressors. New methods and techniques are developed and applied to diverse vegetation scenarios to meet the main challenge of sustainability.

This annotated bibliography documents literature addressing the design and implementation of vegetation monitoring. It provides resources managers, ecologists, and scientists access to the great volume of literature addressing many aspects of vegetation monitoring: planning and objective setting, choosing vegetation attributes to measure, sampling design, sampling methods, statistical and graphical analysis, and communication of results. Over half of the 1400 references have been annotated. Keywords pertaining to the type of monitoring or method are included with each bibliographic entry. Keyword index.

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)

Advances in Legume-based Agroecoystem for Sustainable Intensification explores current research and future strategies for ensuring capacity growth and socioeconomic improvement through the utilization of legume crop cultivation and production in the achievement of sustainability development goals (SDGs). Sections cover the role of legumes in addressing issues of food security, improving nitrogen in the environment, environmental sustainability, economic-environmentally optimized systems, the importance and impact of nitrogen, organic production, and biomass potential, legume production, biology, breeding improvement, cropping systems, and the use of legumes for eco-friendly weed management. This book is an important resource for scientists, researchers and advanced students interested in championing the effective utilization of legumes for agronomic and ecological benefit. - Focuses on opportunities for agricultural impact and sustainability - Presents insights into both agricultural sustainability and eco-intensification - Includes the impact of legume production on societal impacts such as health and wealth management

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

Global climate change, reductions in arable land, and food security demands that plant breeding will continue to play an imperative role in feeding 9 billion people sustainably by 2050. In order to face this challenge, modern plant breeding will necessitate the adoption of new technologies and practices to boost production of cultivated plants by capturing or generating more favorable genetic diversity. In crop plants, the majority of agronomically important traits are quantitatively inherited, controlled by multiple genes each with a small effect (quantitative trait loci, QTLs). The most common approach to pre-breeding is to use genetic mapping to identify QTLs for key phenotypic variation followed by introgressing those QTLs into the elite gene pool with marker-assisted selection (MAS), which can enhance the selection criteria of phenotypes comparing to conventional breeding with the selection of genes. As the cost of genotyping continues to decline, the use of genotyping-by-sequencing (GBS) technologies or whole genome re-sequencing, coupled with the release of the genome sequences of plant species have permitted the development of dense arrays of single nucleotide polymorphisms (SNPs) covering the entire genome, which have in turn paved the way to genome-wide association studies (GWAS). Meanwhile, fine mapping guided by genome sequences of many plant species have facilitated the exploration of functional genes; in addition, pan-genomes constructed from various available resources such as the reference sequence and its variants, raw reads and haplotype reference panels provide a new perspective on QTL locations and potential molecular targets for plant breeding. Similarly, new approaches to marker-trait association analyses such as quantitative trait locus sequencing (QTL-seq) and quantitative trait gene sequencing (QTG-seq) that are based on bulked-segregant analysis (BSA) and whole-genome resequencing will help accelerate QTL fine-mapping and identification of the causal genes. In conclusion, the tools and strategies for MAS in modern plant breeding have been expanding in recent years. By embracing a broad array of conventional and new molecular techniques, modern plant breeding has a bright future in delivering new crop cultivars to keep our food, fiber and biobased economy diverse and safe.