Proceedings
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| Filter results11 paper(s) found. |
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1. Tools For Evaluating The Potential Of Automatic Section ControlOne of the newest technologies in precision agriculture is automatic section control on application equipment. This technology has tremendous potential to reduce wasted inputs, especially on irregularly shaped fields. Paybacks are not necessarily as great on rectangular fields. Producers considering adoption of the technology need to decide whether they will receive sufficient payback for their field shapes. They must also decide... T. Stombaugh, R.S. Zandonadi, J.D. Luck, T.P. Mcdonald, T. Mcgraw |
2. Small UAS Integrated Sensing Tools for Abiotic Stress Monitoring in Irrigated Pinto BeansPrecision agriculture is a practical approach to maximize crop yield with optimal use of rapidly depleting natural resources. Availability of specific and high resolution crop data at critical growth stages is a key for real-time data driven decision support for precision agriculture management during the production season. The goal of this study was to evaluate the feasibility of using small unmanned aerial system (UAS) integrated remote sensing tools to monitor the abiotic stress of eight irrigated... L. Khot, J. Zhou, R. Boydston, P.N. Miklas, L. Porter |
3. Soil Microbial Communities Have Distinct Spatial Patterns in Agricultural FieldsSoil microbial communities mediate many important soil processes in agricultural fields, however their spatial distribution at distances relevant to precision agriculture is poorly understood. This study examined the soil physico-chemical properties and topographic features controlling the spatial distribution of soil microbial communities in a commercial potato field in eastern Canada using next generation sequencing. Soil was collected from a transect (1100 m) with 83 sampling points in a landscape... B. Zebarth, C. Goyer, S. Neupane, S. Li, A. Mills, S. Whitney, A. Cambouris, I. Perron |
4. Monitoring Soybean Growth and Yield Due to Topographic Variation Using UAV-Based Remote SensingRemote sensing has been used as an important tool in precision agriculture. With the development of unmanned aerial vehicle (UAV) technology, collection of high-resolution site-specific field data becomes promising. Field topography affects spatial variation in soil organic carbon, nitrogen and water content, which ultimately affect crop performance. To improve crop production and reduce inputs to the field, it is critical to collect site-specific information in a real-time manner and at a large... J. Zhou, K.A. Sudduth, A. Feng |
5. Detect Estrus in Sows Using a Lidar Sensor and Machine LearningAccurate estrus detection of sows is labor intensive and is crucial to achieve high farrowing rate. This study aims to develop a method to detect accurate estrus time by monitoring the change in vulvar swollenness around estrus using a light detection and ranging (LiDAR) camera. The measurement accuracy of the LiDAR camera was evaluated in laboratory conditions before it was used in monitoring sows in a swine research facility. In this study, twelve multiparous individually housed sows were continuously... J. Zhou, Z. Xu |
6. Toward Smart Soybean Variety Selection Using UAV-based Imagery and Machine LearningThe efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield and resilience to stress, achieved in one year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials due to their large population, complex genetic behavior, and high genotype-environment... J. Zhou, J. Zhou |
7. Estimating Soil Carbon Stocks with In-field Visible and Near-infrared SpectroscopyAgricultural lands can be a sink for carbon and play an important role in offsetting carbon emissions. Current methods of measuring carbon sequestration—through repeated temporal soil samples—are costly and laborious. A promising alternative is using visible, near-infrared (VNIR) diffuse reflectance spectroscopy. However, VNIR data are complex, which requires several data processing steps and often yields inconsistent results, especially when using in situ VNIR measurements. Using... C.J. Ransom, C. Vong, K.S. Veum, K.A. Sudduth, N.R. Kitchen, J. Zhou |
8. Hay Yield Estimation Using UAV-based Imagery and a Convolutional Neural NetworkYield monitoring systems are widely used commercially in grain crops to map yields at a scale of a few meters. However, such high-resolution yield monitoring and mapping for hay and forage crops has not been commercialized. Most commercial hay yield monitoring systems only obtain the weight of individual bales, making it difficult to map and understand the spatial variability in hay yield. This study investigated the feasibility of an unmanned aerial vehicle (UAV)-based remote sensing system for... K. Lee, K.A. Sudduth, J. Zhou |
9. Automated Sow Estrus Detection Using Machine Vision TechnologySuccessful artificial insemination for gilts and sows relies on accurate timing that is determined by estrus check. Estrus checks in current farms are manually conducted by skilled breeding technicians using the back pressure test (BPT) method that is labor-intensive and inefficient due to the large animal-to-staff ratio. This study aimed to develop a robotic imaging system powered by artificial intelligence technology to automatically detect estrus status for gilts and sows in a stall-housing... J. Zhou, Z. Xu, T.J. Safranski, C. Bromfield |
10. Assessing Spray Coverage Variability of an Under-canopy Robotic Sprayer System in Sorghum CropAn under-canopy robotic sprayer system was developed for site-specific pest management in row crops. However, the effect of nozzle type and spray coverage variability at different points within the plant canopy was unknown. The objective of this study was to quantify the spray coverage at multiple locations within the sorghum crop canopy to determine the effectiveness of such robotic systems. The experiments were conducted in a sorghum field in Ashland, Kansas, using XR8001 flat fan and TXVS6... P. Pokharel, A. Sharda, M. Gadhwal, B. Aryal |
11. Deep Learning Prediction of Methane Production in Mesophilic and Thermophilic Anaerobic DigestionAnaerobic digestion (AD) converts organic waste into methane-rich biogas but forecasting methane yield is difficult due to nonlinear dynamics. This study compares Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN), and Temporal Fusion Transformer （TFT） models for predicting methane production rate (MPR, L/L/d) under mesophilic (37°C) and thermophilic (55°C) conditions. Lab-scale reactor data with features including hydraulic retention... C. Chou, A. Liu |