Machine vision based system for flower counting in strawberry plants

Background: For strawberry production, accurate yield prediction is very important to help growers increase their profit by efficiently managing their harvesting operation and setting their contracts with buyers. Strawberry plants produce flowers and fruits simultaneously throughout the season. Strawberry flowers are white in color with a yellow pollen at the center, which later becomes a fruit. Strawberry yield can be estimated by counting the number of flowers in a field in advance of harvesting. The objective of this project is to count the number of flowers using image processing techniques, create a map of flower counts using gee-tagging and provide farmers with an estimate of the yield in a given area. Methods: Strawberry flowers could be at different stages of maturation during imaging. We pre-process images using edge-preserving smoothing filter to remove noise without removing fine features. The next stage involves segmentation of flowers from the background. Since flowers are brighter than most other components of plants, simple thresholding with segmentation algorithm will produce candidate pixels. Then flower detection will be conducted using traditional feature engineering along with a classifier such as Histogram of Oriented Gradients, Wavelet Transform, Local Binary Patterns, and the Deep Learning based techniques. Results: Once flowers are detected, the number of flowers is counted to provide farmers with an estimate of yield and variability at different locations in the field. Discussions: One of the biggest challenges with outdoor imaging is the variable lighting conditions. We propose a camera mounted autonomous system to go over rows of strawberry plants to capture images with geo-tags. Cameras are positioned to capture images from different angles to capture occluded flowers. Conclusion: A novel image processing method for accurate strawberry yield prediction is proposed by counting the number of flowers from images for efficient crop management

From Resistance to Receptiveness: Farmer Willingness to Participate in Extension Discussions About Climate Variability and Climate Change

Identifying what Extension professionals believe are the critical elements of a communication strategy that is most likely to encourage agricultural producers to participate in discussions of climate variability and climate change is pivotal to providing timely solutions to issues facing farmers. The current study involved interviews with 50 Extension professionals from four southeastern states (Alabama, Florida, Georgia, and South Carolina) who were engaged in ongoing work related to climate and agriculture. Respondents were asked to assess how best to engage farmers in conversations related to climate variability and climate change. Qualitative analysis showed that Extension professionals recommended avoiding content related to politics, attribution of climate change to human causes, and telling farmers what to do. Respondents recommended emphasizing adaptation strategies, climate variability over climate change, evidence that climate change exists, and the financial benefits for farmers. In addition, Extension professionals proposed several delivery methods they thought would be most effective with farmers, including delivery tailored to the characteristics of the audience, a positive overall tone, and an understanding that engagement should be viewed as a long-term process based on building relationships with farmers. The findings suggest that farmers are a potentially receptive audience on climate issues when properly approached

Profitability, Engaging Delivery, and Trust: How Extension Professionals Can Optimize Farmer Adoption of Climate-related Adaptation Strategies

This study examined Extension professionals’ perspectives on how to optimize the chances that farmers will adopt climate adaptation strategies designed to minimize risks associated with climate variability and climate change. In-depth interviews were conducted with Extension professionals in four southeastern states (Alabama, Florida, Georgia, and South Carolina). Responses were coded and analyzed, resulting in three recommendations. First, focus on profitability and issues of immediate concern to farmers. Second, use engaging delivery methods, especially field trials conducted under realistic conditions. Third, build trust with farmers, primarily by focusing on research-based information. This study has practical implications for how Extension professionals should approach the work of addressing climate issues in agriculture

Climate-Related Risks and Management Issues Facing Agriculture in the Southeast: Interviews with Extension Professionals

To explore Extension professionals\u27 perceptions of the potential impact of climate variability and climate change on agriculture and to identify the top climate-related issues facing farmers, we conducted interviews with agricultural Extension personnel from Alabama, Florida, Georgia, and South Carolina. Of those interviewed, 92% believed climate change will affect agriculture a moderate amount or a great deal. Qualitative analyses revealed that the Extension professionals considered scarcity of water resources, temperature fluctuations, pest and disease pressures, forecast challenges, seasonal variability, and adaptation strategies as among the most important climate-related issues affecting agriculture in the Southeast

Toward Engagement in Climate Training: Findings from Interviews with Agricultural Extension Professionals

With scientific consensus regarding the occurrence of climate variability and climate change it is clear that farmers can benefit from science-based adaptation strategies for managing climate-related risk. To this end, cooperative extension professionals must engage in climate training events that are carefully planned and tailored to their specific needs. This study consisted of 50 interviews with extension professionals from four states (Alabama, Florida, Georgia, and South Carolina) and collected information about the perceptions of climate variability and change as well as the preferred approaches for climate-related training in extension. Results include the need for accessible, climate-related training that prepares extension professionals to: understand both management- and technology-related adaptation strategies, engage in productive conversations with all stakeholders, and participate in the coproduction of knowledge related to climate issues

Collective magnetism at multiferroic vortex domain walls

Topological defects have been playgrounds for many emergent phenomena in complex matter such as superfluids, liquid crystals, and early universe. Recently, vortex-like topological defects with six interlocked structural antiphase and ferroelectric domains merging into a vortex core were revealed in multiferroic hexagonal manganites. Numerous vortices are found to form an intriguing self-organized network. Thus, it is imperative to find out the magnetic nature of these vortices. Using cryogenic magnetic force microscopy, we discovered unprecedented alternating net moments at domain walls around vortices that can correlate over the entire vortex network in hexagonal ErMnO3 The collective nature of domain wall magnetism originates from the uncompensated Er3+ moments and the correlated organization of the vortex network. Furthermore, our proposed model indicates a fascinating phenomenon of field-controllable spin chirality. Our results demonstrate a new route to achieving magnetoelectric coupling at domain walls in single-phase multiferroics, which may be harnessed for nanoscale multifunctional devices.Comment: 18 pages, 10 figure

Asian soybean rust: modeling the impact on soybean grain yield in the Triângulo Mineiro / Alto Paranaíba Region, Minas Gerais, Brazil.

Understanding the impact of Asian soybean rust on soybean yield is of great importance in the crop simulation model for this crop become it is possible to predict yield using different sowing dates and growth conditions. The goal of this study were to evaluate the performance of two soybean cultivars in Triângulo Mineiro/Alto Paranaíba, MG, Brazil and the effects of soybean rust on the yield of these cultivars using the CSM-CROPGRO Soybean model. Two soybean cultivars NK 7074 (early) and UFUS-Impacta (medium late), which differ in their development cycles, were growing in Uberaba city during the 2009/2010 growing season. The validation for cultivar UFUS-Impacta was conducted comparing the measured and simulated yield data considering three different sowing dates in the Uberlândia city during the 2002/2003 growing season. Daily meteorological data obtained from six meteorological stations of the National Institute of Meteorology (INMET ). To determine the performance of the soybean cultivars and the effect of soybean rust on yield, three different scenarios were used: no occurrence of rust (NOR) and occurrence of rust with inoculum concentrations of U5.000 and U10.000 urediniospores/mL. For all environments studied, the early cultivar had the best performance than the medium late cultivar. Soybean rust had the most effect on yield for the U10.000 scenario than for the U5.000 scenario. The best soybean performance occurred for Araxá and Uberaba cities. The South-Southeast area of the Triângulo Mineiro/Alto Paranaíba region was the most sensitive to the effect of rust on yield compared to the North region

Cosmic Strings and Superstrings

Cosmic strings are predicted by many field-theory models, and may have been formed at a symmetry-breaking transition early in the history of the universe, such as that associated with grand unification. They could have important cosmological effects. Scenarios suggested by fundamental string theory or M-theory, in particular the popular idea of brane inflation, also strongly suggest the appearance of similar structures. Here we review the reasons for postulating the existence of cosmic strings or superstrings, the various possible ways in which they might be detected observationally, and the special features that might discriminate between ordinary cosmic strings and superstrings.Comment: Minor errors corrected and some references added, 34 pages, 6 figure

Design and construction of a carbon fiber gondola for the SPIDER balloon-borne telescope

We introduce the light-weight carbon fiber and aluminum gondola designed for the SPIDER balloon-borne telescope. SPIDER is designed to measure the polarization of the Cosmic Microwave Background radiation with unprecedented sensitivity and control of systematics in search of the imprint of inflation: a period of exponential expansion in the early Universe. The requirements of this balloon-borne instrument put tight constrains on the mass budget of the payload. The SPIDER gondola is designed to house the experiment and guarantee its operational and structural integrity during its balloon-borne flight, while using less than 10% of the total mass of the payload. We present a construction method for the gondola based on carbon fiber reinforced polymer tubes with aluminum inserts and aluminum multi-tube joints. We describe the validation of the model through Finite Element Analysis and mechanical tests.Comment: 16 pages, 11 figures. Presented at SPIE Ground-based and Airborne Telescopes V, June 23, 2014. To be published in Proceedings of SPIE Volume 914

A timeband framework for modelling real-time systems

Complex real-time systems must integrate physical processes with digital control, human operation and organisational structures. New scientific foundations are required for specifying, designing and implementing these systems. One key challenge is to cope with the wide range of time scales and dynamics inherent in such systems. To exploit the unique properties of time, with the aim of producing more dependable computer-based systems, it is desirable to explicitly identify distinct time bands in which the system is situated. Such a framework enables the temporal properties and associated dynamic behaviour of existing systems to be described and the requirements for new or modified systems to be specified. A system model based on a finite set of distinct time bands is motivated and developed in this paper