Proceedings
Authors
| Filter results4 paper(s) found. |
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1. On-farm Evaluation of the Potential Benefits of Variable Rate Seeding for Corn in MinnesotaMany farmers in Minnesota are interested in adopting variable rate seeding technology for corn, however, little has been reported about their potential benefits. The objectives of this study were to 1) determine within-field variability of optimal seeding rates, and 2) evaluate the potential benefits of variable rate seeding in commercial corn fields in Minnesota. Four on-farm variable rate seeding trials were conducted in Minnesota in 2022 and 2023, with seeding rates ranging from 31,000 to 41,000... Y. Miao, A. Kechchour, S. Folle, K. Mizuta |
2. Rgb-based Soil Water Content Prediction Enhanced by Hyperspectral CalibrationWhile hyperspectral imaging (HSI) cameras demonstrate high accuracy for detecting soil water content (SWC)-related spectral variations, their field deployment remains constrained by prohibitive costs and operational complexity. This study investigates utilizing low-cost RGB cameras through HSI-guided calibration for SWC estimation. 210 paired HSI-RGB measurements were acquired across five soil texture classes (0-40% fine particles), fourteen moisture levels (0-39% SWC), and three illumination... J. Park |
3. Development of a Low-power Wireless Communication System Using Lora for Structural Monitoring in Greenhouse FoundationsPlastic greenhouses dominate protected cultivation in South Korea but are vulnerable to extreme weather and foundation instability. To address this issue, a low-power, low-cost monitoring system was developed to estimate foundation attitude and detect anomalies such as uplift. The system integrates an IMU (Inertial Measurement Unit)-based sensor node, LoRa (Long Range) communication, and a gateway in a star topology. Field tests, including pipe uplift and natural conditions, confirmed comparable... J. Park |
4. A Review on Structural Enhancements and Domain-Specific Adaptation of YOLO for Crop-Weed RecognitionThis review systematically summarizes YOLO-based weed detection models, focusing on two key directions: attention mechanisms that improve discrimination between visually similar vegetation and lightweight techniques that ensure real-time performance on limited hardware. A comparative analysis of improved YOLO variants highlights how structural optimizations improve detection, offering insights into efficient model design. ... J. Park |