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Pavido Scalf

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Aug 4, 2024, 8:17:27 PM8/4/24

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Webring together unmatched farming, sensory and industry expertise to help you obtain world-class hops, with consistent true-to-type flavor and aroma characteristics, so you can always brew your best.

In this short video tutorial, you will learn how to efficiently earn curated scripts in The Elder Scrolls Online's Gold Road. Discover the step-by-step process to collect scripts for your characters through daily quests and specific activities.

Welcome back, adventurers of the Gold Road! In this guide, you will find valuable information on how to efficiently farm scripts for The Elder Scrolls Online. Upon unlocking scribing, the tutorial reveals how you can rapidly expand your script collection without having to spend gold. By completing various wings in the game, you can access daily quests that offer curated scripts, enhancing your gameplay. Learn how to maximize your script gathering potential and build a robust collection for your characters.

Our award winning Chef will deliver a fun, festive, and seated multi-course, family style dinner that will nourish your soul and tantalize your senses with bold and invigorating tastes. Innovative and traditional dishes and splendid company will awaken your mind and teach you about the many farm-to-table local and organic farms that Vermont has to offer you.

Stony Pond Farm is an organic dairy and beef farm in the Northwest corner of Vermont. Tyler and Melanie Webb steward close to 400 acres of land in Fairfield, Vermont. We milk 75 cows and have a small beef herd. In addition, we have an beautiful Airbnb house on the farm where we offer guests a chance to unplug, connect with nature and explore the farm.

The perfect fruit dip to serve your family and make for parties and gatherings to share. You only need 3 ingredients to make this peanut butter honey and apple yogurt dip! It is wholesome, fluffy, protein-packed, and so delicious. I was inspired to create this creamy good peanut butter yogurt dip after I attended a virtual farm tour that was hosted by Stoneyfield Organic. In this tour, we were able to virtually meet the farmers, take a peek at what working on the farm actually looks like, and gain a better understanding of their day-to-day farming, and what it means to be organic and have organic cows.

Finally, please use our hashtag #healthyfitnessmeals on INSTAGRAM for a chance to be featured! FOLLOW Healthy Fitness Meals on FACEBOOK INSTAGRAM PINTEREST TWITTER for all of our latest blog posts and recipes.

This is the Hi-Res Vineyard Nutrition Podcast series, devoted to helping the grape and wine industry understand more about how to monitor and manage vineyard health through grapevine nutrition research. I am your host, Dr. Patty Skinkis, Professor and Viticulture Extension Specialist at Oregon State University.

(02:28) Qin Zhang

Yes, we do use this. This is actually a very mature sensing technology for agricultural production, especially to detect the nutrient stress for all different crops, and you know the history of that: you can go back 40 years when precision farming technology was being developed, and that the core technology is using sensors or sensing technology to detect the crop nutrient condition. Then they create a plan how to manage the nutrition. I would say this technology has been used very successfully for agronomic crops. For example, corn, wheat, rice, etc. But when applied to the grapevines there could be some technical issues or challenges to be addressed.

(03:44) Qin Zhang

OK, this is a very good question. Maybe I should talk a little bit about the fundamentals of sensing (the hyperspectral sensing technology). The hypothesis is that the sensor can detect crop and nutrient conditions. We assume the canopy color is a representation of some chlorophyll level of the crop and which is an indication of crop nutrient stress. We have a lot of different ways to measure those levels, but most of those measurement means are destructive, and we need to collect samples for the crop and that takes time to make an analysis. This hyper-spectrum sensor is a wonderful sensing technology that you do not need to destruct. So, we have non-destructive sensing technology. So, how it works is it measures the reflectance of some specific light from the crop canopy, and then, based on the reflectance estimates the crop nutrient condition. So, often we call it an indirect, approximate sensing technology. It has an advantage that is non-destructive, and it can be done remotely, so, often we assume that we have already heard a lot about satellite-based or drone-based remote sensing. So, this is a technology that allows the sensor to be carried by satellite, by drone, or by tractor, or it is hand-held. So, this is the only technology you can practically use in such a way today.

Ask Extension is a way for you to get answers from the Oregon State University Extension Service. We have experts in family and health, community development, food and agriculture, coastal issues, forestry, programs for young people, and gardening.

OSU Extension is part of the division of Extension and Engagement.

OSU recognizes the impact that its land grant history has had on Indigenous communities in Oregon. See our land acknowledgement.

Precision livestock farming has become an important research focus with the rising demand of meat production in the swine industry. Currently, the farming practice is widely conducted by the technology of computer vision (CV), which automates monitoring pig activity solely based on video recordings. Automation is fulfilled by deriving imagery features that can guide CV systems to recognize animals' body contours, positions, and behavioral categories. Nevertheless, the performance of the CV systems is sensitive to the quality of imagery features. When the CV system is deployed in a variable environment, its performance may decrease as the features are not generalized enough under different illumination conditions. Moreover, most CV systems are established by supervised learning, in which intensive effort in labeling ground truths for the training process is required. Hence, a semi-supervised pipeline, VTag, is developed in this study. The pipeline focuses on long-term tracking of pig activity without requesting any pre-labeled video but a few human supervisions to build a CV system. The pipeline can be rapidly deployed as only one top-view RGB camera is needed for the tracking task. Additionally, the pipeline was released as a software tool with a friendly graphical interface available to general users. Among the presented datasets, the average tracking error was 17.99 cm. Besides, with the prediction results, the pig moving distance per unit time can be estimated for activity studies. Finally, as the motion is monitored, a heat map showing spatial hot spots visited by the pigs can be useful guidance for farming management. The presented pipeline saves massive laborious work in preparing training dataset. The rapid deployment of the tracking system paves the way for pig behavior monitoring. Lay Summary Collecting detailed measurements of animals through cameras has become an important focus with the rising demand for meat production in the swine industry. Currently, researchers use computational approaches to train models to recognize pig morphological features and monitor pig behaviors automatically. Though little human effort is needed after model training, current solutions require a large amount of pre-selected images for the training process, and the expensive preparation work is difficult for many farms to implement such practice. Hence, a pipeline, VTag, is presented to address these challenges in our study. With few supervisions, VTag can automatically track positions of multiple pigs from one single top-view RGB camera. No pre-labeled images are required to establish a robust pig tracking system. Additionally, the pipeline was released as a software tool with a friendly graphical user interface, that is easy to learn for general users. Among the presented datasets, the average tracking error is 17.99 cm, which is shorter than one-third of the pig body length in the study. The estimated pig activity from VTag can serve as useful farming guidance. The presented strategy saves massive laborious work in preparing training datasets and setting up monitoring environments. The rapid deployment of the tracking system paves the way for pig behavior monitoring. The presented pipeline, VTag, saves massive laborious work in preparing labeled training datasets and setting up environment for pig tracking tasks. VTag can be deployed rapidly and paves the way for pig behavior monitoring.