Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping

Plant stress phenotyping is essential to select stress-resistant varieties and develop better stress-management strategies. Standardization of visual assessments and deployment of imaging techniques have improved the accuracy and reliability of stress assessment in comparison with unaided visual measurement. The growing capabilities of machine learning (ML) methods in conjunction with image-based phenotyping can extract new insights from curated, annotated, and high-dimensional datasets across varied crops and stresses. We propose an overarching strategy for utilizing ML techniques that methodically enables the application of plant stress phenotyping at multiple scales across different types of stresses, program goals, and environments

Earth benefits from NASA research and technology. Life sciences applications

This document provides a representative sampling of examples of Earth benefits in life-sciences-related applications, primarily in the area of medicine and health care, but also in agricultural productivity, environmental monitoring and safety, and the environment. This brochure is not intended as an exhaustive listing, but as an overview to acquaint the reader with the breadth of areas in which the space life sciences have, in one way or another, contributed a unique perspective to the solution of problems on Earth. Most of the examples cited were derived directly from space life sciences research and technology. Some examples resulted from other space technologies, but have found important life sciences applications on Earth. And, finally, we have included several areas in which Earth benefits are anticipated from biomedical and biological research conducted in support of future human exploration missions

Image Analysis and Machine Learning in Agricultural Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed. Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could help with agricultural data collection. In the first chapter, information related to different types of imaging (e.g., RGB, multi/hyperspectral, and thermal imaging) was explored in detail for its advantages in different agriculture applications. The process of image analysis demonstrated how target features were extracted for analysis including shape, edge, texture, and color. After acquiring features information, machine learning can be used to automatically detect or predict features of interest such as disease severity. In the second chapter, case studies of different agricultural applications were demonstrated including: 1) leaf damage symptoms, 2) stress evaluation, 3) plant growth evaluation, 4) stand/insect counting, and 5) evaluation for produce quality. Case studies showed that the use of image analysis is often more advantageous than visual rating. Advantages of image analysis include increased objectivity, speed, and more reproducibly reliable results. In the third chapter, machine learning was explored using romaine lettuce images from RD4AG to automatically grade for bolting and compactness (two of the important parameters for lettuce quality). Although the accuracy is at 68.4 and 66.6% respectively, a much larger data base and many improvements are needed to increase the model accuracy and reliability. With the advancement in cameras, computers with high computing power, and the development of different algorithms, image analysis and machine learning have the potential to replace part of the labor and improve the current data collection procedure in agricultural research. Advisor: Gary L. Hei

USSR Space Life Sciences Digest, issue 29

This is the twenty-ninth issue of NASA's Space Life Sciences Digest. It is a double issue covering two issues of the Soviet Space Biology and Aerospace Medicine Journal. Issue 29 contains abstracts of 60 journal papers or book chapters published in Russian and of three Soviet monographs. Selected abstracts are illustrated with figures and tables from the original. A review of a book on environmental hygiene and a list of papers presented at a Soviet conference on space biology and medicine are also included. The materials in this issue were identified as relevant to 28 areas of space biology and medicine. The areas are: adaptation, aviation medicine, biological rhythms, body fluids, botany, cardiovascular and respiratory systems, developmental biology, digestive system, endocrinology, equipment and instrumentation, genetics, habitability and environment effects, hematology, human performance, immunology, life support systems, mathematical modeling, metabolism, musculoskeletal system, neurophysiology, nutrition, personnel selection, psychology, radiobiology, reproductive system, space biology and medicine, and the economics of space flight

Engineering analytics through explainable deep learning

Pattern recognition has its origins in engineering while machine learning developed from computer science. Today, artificial intelligence (AI) is a booming field with many practical applications and active research topics that deals with both pattern recognition and machine learning. We now use softwares and applications to automate routine labor, understand speech (using Natural Language Processing) or images (extracting hierarchical features and patterns for object detection and pattern recognition), make diagnoses in medicine, even intricate surgical procedures and support basic scientific research. This thesis deals with exploring the application of a specific branch of AI, or a specific tool, Deep Learning (DL) to real world engineering problems which otherwise had been difficult to solve using existing methods till date. Here we focus on different Deep Learning based methods to deal with two such engineering problems. We also explore the inner workings of such models through an explanation stage for each of the applied DL based strategies that gives us a sense of how such typical black box models work, or as we call it, an explanation stage for the DL model. This explanation framework is an important step as previously, Deep Learning based models were thought to be frameworks which produce good results (classification, object detection, object recognition to name a few), but with no explanations or immediately visible causes as to why it achieves the results it does. This made Deep Learning based models hard to trust amongst the scientific community. In this thesis, we aim to achieve just that by deploying two such explanation frameworks, one for a 2D image study case and another for a 3D image voxel study case, which will be discussed later in the subsequent chapters

Application of remote sensing to selected problems within the state of California

There are no author-identified significant results in this report

Prediction of Early Vigor from Overhead Images of Carinata Plants

Breeding more resilient, higher yielding crops is an essential component of ensuring ongoing food security. Early season vigor is signi cantly correlated with yields and is often used as an early indicator of tness in breeding programs. Early vigor can be a useful indicator of the health and strength of plants with bene ts such as improved light interception, reduced surface evaporation, and increased biological yield. However, vigor is challenging to measure analytically and is often rated using subjective visual scoring. This traditional method of breeder scoring becomes cumbersome as the size of breeding programs increase. In this study, we used hand-held cameras tted on gimbals to capture images which were then used as the source for automated vigor scoring. We have employed a novel image metric, the extent of plant growth from the row centerline, as an indicator of vigor. Along with this feature, additional features were used for training a random forest model and a support vector machine, both of which were able to predict expert vigor ratings with an 88:9% and 88% accuracies respectively, providing the potential for more reliable, higher throughput vigor estimates

An evaluation of the utilization of remote sensing in resource and environmental management of the Chesapeake Bay region

A nine-month study was conducted to assess the effectiveness of the NASA Wallops Chesapeake Bay Ecological Program in remote sensing. The study consisted of a follow-up investigation and information analysis of actual cases in which remote sensing was utilized by management and research personnel in the Chesapeake Bay region. The study concludes that the NASA Wallops Chesapeake Bay Ecological Program is effective, both in terms of costs and performance