Investigation related to multispectral imaging systems

A summary of technical progress made during a five year research program directed toward the development of operational information systems based on multispectral sensing and the use of these systems in earth-resource survey applications is presented. Efforts were undertaken during this program to: (1) improve the basic understanding of the many facets of multispectral remote sensing, (2) develop methods for improving the accuracy of information generated by remote sensing systems, (3) improve the efficiency of data processing and information extraction techniques to enhance the cost-effectiveness of remote sensing systems, (4) investigate additional problems having potential remote sensing solutions, and (5) apply the existing and developing technology for specific users and document and transfer that technology to the remote sensing community

High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth, health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, physiological, and biochemical traits of plant leaves on multiple scales. We summarize the characteristics, advantages and limitations of optical sensing and data-processing methods applied in various plant phenotyping scenarios. Finally, we discuss the future prospects of plant leaf phenotyping research. This review aims to help researchers choose appropriate optical sensors and data processing methods to acquire plant leaf phenotypes rapidly, accurately, and cost-effectively

AgRISTARS: Agriculture and resources inventory surveys through aerospace remote sensing

The rationale, objectives, participants, and approach of the AgRISTARS program are described. Progress is reported in activities related to early warning and crop condition assessment; inventory technology development (formerly foreign commodity production forecasting); yield model development; supporting research; soil moisture; renewable resources inventory; domestic crops and land cover; and conservation and pollution. Emphasis is on technological highlights

Feasibility study ASCS remote sensing/compliance determination system

A short-term technical study was performed by the MSC Earth Observations Division to determine the feasibility of the proposed Agricultural Stabilization and Conservation Service Automatic Remote Sensing/Compliance Determination System. For the study, the term automatic was interpreted as applying to an automated remote-sensing system that includes data acquisition, processing, and management

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Genomic tools and molecular breeding approaches for the domestication of field cress (Lepidium campestre L.)

Field cress (Lepidium campestre L.) is a biennial self-pollinated plant with a small genome size. The ever-increasing global population alongside climate change prompts urgent actions to save the ecosystem. Domesticating multi-purpose species such as field cress could be considered as part of the solution to mitigate the challenges posed by climate change and population growth. In addition to the oil producing potential, the domestication of field cress in arable lands has multitude effects – such as protecting environmental contamination and contributing as food and feed uses. In clues of these potentials, identifying the genomic variation underlying important traits using genomic tools is pivotal approach in field cress domestication. The main goal of the research in this thesis was to develop genomic tools for field cress domestication, specifically aiming at constructing the genetic linkage map, identifying the quantitative trait loci (QTL) underpinning domestication traits, and elucidating the common genetic variants associated with the seed yield as well as seed oil, protein, and moisture contents in field cress. An integrated mapping approach were performed to developing the first genetic linkage map for field cress. Relying on the linkage map, the identification of domestication QTL using linkage analysis as well as common variants using genome-wide association study (GWAS) were succeeded. Furthermore, the developed linkage map will be used in guiding to develop the reference genome using whole-genome sequencing (WGS) in field cress. Given further functional genomic efforts, the identified QTL and single variants could facilitate the process of domestication and genomicsassisted breeding in field cress, including the use of evolving approaches such as genome-wide prediction in the field cress

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

Third Annual Earth Resources Program Review. Volume 2: Agriculture, forestry, and sensor studies

Remote sensing and data reduction techniques for Earth Resources Program applied to agriculture and forestry - conferenc