Proceedings
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| Filter results6 paper(s) found. |
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1. Development of a Small Tracking Device for Cattle Using IoT TechnologyThe US is the largest producer of beef in the world. Last year alone, it produces nearly 19% of the world’s beef. This translate to about almost $90 billion in economic impact in the country. Aside from being a producer, the US also consumed more than 26 billion pounds of beef which have a retail value of the entire beef industry to more than $74B. For this level of production and consumption, each rancher in the US must produce a herd size of at least 100 or more to sustain the current... J.M. Maja, A.K. Blocker, E.G. Stuckey, S.G. Sell, G. Tuttle, J. Mueller, J. Andrae |
2. Rapid Identification of Mulberry Leaf Pests Based on Near Infrared Hyperspectral ImagingAs one of the most common mulberry pests, Diaphania pyloalis Walker (Lepidoptera: Pyralididae) has occurred and damaged in the main sericulture areas of China. Naked eye observation, the most dominating method identifying the damage of Diaphania pyloalis, is time-wasting and labor consuming. In order to improve the identification and diagnosis efficiency and avoid the massive outbreak of Diaphania pyloalis, near infrared (NIR) hyperspectral imaging technology combined with partial least discriminant... L. Yang, L. Huang, L. Meng, J. Wang, D. Wu, X. Fu, S. Li |
3. Sampling Bumble Bees and Floral Resources Using Deep Learning and UAV ImageryPollinators, essential components of natural and agricultural systems, forage over relatively large spatial scales. This is especially true of large generalist species, like bumble bees. Thus, it can be difficult to estimate the amount and diversity of floral resources available to them. Floral cover and diversity are often estimated over large areas by extrapolation from small scale samples (e.g., a 1-m quadrat) but the accuracy of such estimates can vary depending on the spatial patchiness of... B. Spiesman, I. Grijalva, D. Holthaus, B. Mccornack |
4. Creating a Comprehensive Software Framework for Sensor-driven Precision AgricultureRobots and GPS-guided tractors are the backbone of smart farming and precision agriculture. Many companies and vendors contribute to the market, each offering their own customized solutions for common tasks. These developments are often based on vendor-specific, proprietary components, protocols and software. Many small companies that produce sensors, actuators or software for niche applications could contribute their expertise to the global efforts of creating smart farming solutions, if their... O. Scholz, F. Uhrmann, M. Weule, T. Meyer, A. Gilson, J. Makarov, J. Hansen, T. Henties |
5. Algorithm to Estimate Sorghum Grain Number from Panicles Using Images Collected with a Smartphone at Field-scaleAn estimation of on-farm yield before harvest is important to assist farmers on deciding additional input use, time to harvest, and options for end uses of the harvestable product. However, obtaining a rapid assessment of on-farm yield can be challenging, even more for sorghum (Sorghum bicolor L.) crop due to the complexity for accounting for the grain number at field-scale. One alternative to reduce labor is to develop a rapid assessment method employing computer vision and artificial intelligence... G.N. Nocera santiago, P. Cisdeli magalhães, I. Ciampitti, L. Marziotte |
6. Detection of Sorghum Aphids with Advanced Machine VisionSorghum aphid, Melanaphis sorghi (Theobald), became a significant pest concern due to the significant yield losses caused in the sorghum production region. Different management practices, including monitoring and applying insecticides, have been used to manage this invasive pest in sorghum. The most common management strategy consists of visual assessments of aphids on sorghum leaves to determine an economic threshold level to spray. However, because of their rapid reproduction,... I.A. Grijalva teran, B. Spiesman, N. Clark, B. Mccornack |