A Machine Vision System for Evaluation of Planter Seed Spatial Distribution

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a Technical Paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 4 (2002): V. Alchanatis, Y. Kashti, and R. Brikman. A Machine Vision System for Evaluation of Planter Seed Spatial Distribution

Variability of potato petiole nitrogen in response to nitrogen fertilizer, implications for variable management

Non-Peer ReviewedRecent increases in the cost of fertilizer nitrogen have prompted producers to assess the potential to vary inputs in space and time to produce the highest marketable yield of potatoes. A study was conducted from 2005 to 2007 near Brandon, Manitoba Canada, to assess the spatial variability of potato yield in upper, middle and lower landforms on a sandy loam soil in response to a range of nitrogen fertilizer rates and split application. Petiole nitrogen, determined late in the growing season, was correlated with potato yield and was used to assess nitrogen sufficiency through the growing season. Petiole nitrogen varied with time during the growing season, from uniform levels in June across all fertilizer treatments, to those which varied with fertilizer treatment in July and August. Furthermore potato petiole nitrogen was higher in lower landforms during July and August, where higher total and marketable yields were recorded. The potential for split application of nitrogen in potatoes based on management zones or sensor readings will have to be carefully assessed to account for temporal and spatial variability

Development of a 2-Dimensional Video-Acoustic Tool for Monitoring Bite Placement

Studies of grazing behaviour conducted at the spatial scale of a feeding station demonstrate that intake rate declines with increasing depletion, a response attributed to an increase in bite overlap (Ginnett et al., 1999; Ungar et al., 2001). In order to understand the rules that govern bite placement, a methodology is required that can map the sequential placement of bites on the sward surface. We developed a video-acoustic tool to achieve this and report the findings of using the tool on small uniform patches of herbage

Data-driven agriculture and sustainable farming: friends or foes?

Sustainability in our food and fiber agriculture systems is inherently knowledge intensive. It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience. Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about synergies between the domains of natural systems that are key to simultaneously achieve sustainability and food security. In the quest for agricultural sustainability, some high-payoff research areas are suggested to resolve critical legal and technical barriers as well as economic and social constraints. These include: the development of holistic decision-making systems, automated animal intake measurement, low-cost environmental sensors, robot obstacle avoidance, integrating remote sensing with crop and pasture models, extension methods for data-driven agriculture, methods for exploiting naturally occurring Genotype x Environment x Management experiments, innovation in business models for data sharing and data regulation reinforcing trust. Public funding for research is needed in several critical areas identified in this paper to enable sustainable agriculture and innovation

Nasal continuous positive airway pressure improves myocardial perfusion reserve and endothelial-dependent vasodilation in patients with obstructive sleep apnea

<p>Abstract</p> <p>Background</p> <p>Obstructive sleep apnea (OSA) has been associated with cardiovascular disease (CVD), but whether OSA is an independent risk factor for CVD is controversial. The purpose of this study is to determine if patients with OSA have subclinical cardiovascular disease that is detectable by multi-modality cardiovascular imaging and whether these abnormalities improve after nasal continuous positive airway pressure (nCPAP).</p> <p>Results</p> <p>Of the 35 consecutive subjects with newly diagnosed moderate to severe OSA recruited from the Stanford Sleep Disorders Clinic, 20 patients were randomized to active vs. sham nCPAP. Active nCPAP was titrated to pressures that would prevent sleep disordered breathing based on inpatient polysomnography. OSA patients had baseline vascular function abnormalities including decreased myocardial perfusion reserve (MPR), brachial flow mediated dilation (FMD) and nitroglycerin-induced coronary vasodilation. Patients randomized to active nCPAP had improvement of MPR (1.5 ± 0.5 vs. 3.0 ± 1.3, p = 0.02) and brachial FMD (2.5% ± 5.7% vs. 9.0% ± 6.5%, p = 0.03) after treatment, but those randomized to sham nCPAP showed no significant improvement. There were no significant changes seen in chamber sizes, systolic and diastolic function, valvular function and coronary vasodilation to nitroglycerin.</p> <p>Conclusions</p> <p>Patients with moderate to severe OSA had decreased MPR and brachial FMD that improved after 3 months of nCPAP. These findings suggest that relief of apnea in OSA may improve microvascular disease and endothelial dysfunction, which may prevent the development of overt cardiovascular disease. Further study in a larger patient population may be warranted.</p

Thermal crop water stress index base line temperatures for sugarbeet in arid western U.S.

Sugarbeet is a deep-rooted crop in unrestricted soil profiles that can readily utilize stored soil water to reduce seasonal irrigation requirements. Utilization of soil water below 0.6 m is not commonly considered for irrigation scheduling due to the labor and expense of soil water monitoring at deeper depths and uncertainty in effective rooting depth and soil water holding capacity. Thermal-based crop water stress index (CWSI) irrigation scheduling for sugarbeet has the potential to overcome soil water monitoring limitations and facilitate utilization of stored soil water. The traditional canopy temperature based CWSI for monitoring plant water status has not been widely used for irrigated crops partly because of the need to know well-watered and non-transpiring canopy temperatures under identical environmental conditions. In this study, canopy temperature of irrigated sugarbeet under full irrigation (FIT) and 25%FIT in 2014, 2015, 2017 and 2018 in southcentral Idaho and FIT and 60%FIT in 2018 in northwestern Wyoming USA was monitored from full cover through harvest along with meteorological conditions and soil water content. Data driven models, multiple linear regression (MLR) and neural network (NN), were used to predict well-watered canopy temperature based on 15-min average values for solar radiation, air temperature, relative humidity, and wind speed collected within 2 hours of solar noon (13:00 – 16:00 MDT). The NN model had significantly less (p 0.6 m) soil water monitoring

Early Diagnosis of Vegetation Health From High-Resolution Hyperspectral and Thermal Imagery: Lessons Learned From Empirical Relationships and Radiative Transfer Modelling

[Purpose of Review] We provide a comprehensive review of the empirical and modelling approaches used to quantify the radiation–vegetation interactions related to vegetation temperature, leaf optical properties linked to pigment absorption and chlorophyll fluorescence emission, and of their capability to monitor vegetation health. Part 1 provides an overview of the main physiological indicators (PIs) applied in remote sensing to detect alterations in plant functioning linked to vegetation diseases and decline processes. Part 2 reviews the recent advances in the development of quantitative methods to assess PI through hyperspectral and thermal images.[Recent Findings] In recent years, the availability of high-resolution hyperspectral and thermal images has increased due to the extraordinary progress made in sensor technology, including the miniaturization of advanced cameras designed for unmanned aerial vehicle (UAV) systems and lightweight aircrafts. This technological revolution has contributed to the wider use of hyperspectral imaging sensors by the scientific community and industry; it has led to better modelling and understanding of the sensitivity of different ranges of the electromagnetic spectrum to detect biophysical alterations used as early warning indicators of vegetation health.[Summary] The review deals with the capability of PIs such as vegetation temperature, chlorophyll fluorescence, photosynthetic energy downregulation and photosynthetic pigments detected through remote sensing to monitor the early responses of plants to different stressors. Various methods for the detection of PI alterations have recently been proposed and validated to monitor vegetation health. The greatest challenges for the remote sensing community today are (i) the availability of high spatial, spectral and temporal resolution image data; (ii) the empirical validation of radiation–vegetation interactions; (iii) the upscaling of physiological alterations from the leaf to the canopy, mainly in complex heterogeneous vegetation landscapes; and (iv) the temporal dynamics of the PIs and the interaction between physiological changes.The authors received funding provided by the FluorFLIGHT (GGR801) Marie Curie Fellowship, the QUERCUSAT and ESPECTRAMED projects (Spanish Ministry of Economy and Competitiveness), the Academy of Finland (grants 266152, 317387) and the European Research Council Synergy grant ERC-2013-SyG-610028 IMBALANCE-P.Peer reviewe