Proceedings
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| Filter results7 paper(s) found. |
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1. Integrating Proximal Hyperspectral and Machine Learning to Predict Nitrogen in Short- and Full-stature Corn Hybrids at Early Growth Stage in Indiana, USANitrogen (N) fertilizer use is a complex challenge, as underapplication can harm yield and overapplication can harm profitability and the environment. N accounts for roughly 58% of total US corn fertilizer use (an annual expense of ~$8 billion), with overapplication estimated at 15% ($1.2 billion for possible savings). Within this setting, early-season yield prediction is a high-value capability for breeding and farmers. If plot-level plant N can be forecasted with high accuracy before the corn... B. Paulus Scheffer, D. . Quinn, P.H. Magalhaes Cisdeli, J. Jin, Z. Qin, I. Ciampitti |
2. Development and Evaluation of a Novel Seeding Metering System for Mechanic Seeder Toward Precision AgricultureRecent progress in precision and digital agriculture has increasingly relied on the integration of computational modeling, sensor-based analysis, and data-driven design to improve agricultural machinery performance. Seed metering systems are central to this progress, as they regulate seed delivery for both uniform crop establishment and variable-rate seeding applications. In conventional agricultural systems, where field conditions are assumed to be relatively homogeneous, uniform seed spacing... E. Jotautienė, D. Karayel, H. Yilmaz, A. Grigas |
3. Hybrid Fuzzy–pid Control for Variable-rate Center Pivot Irrigation: an Automation-driven Approach to Precision Water ManagementPrecision agriculture increasingly relies on advanced automation and intelligent control strategies to address the spatial and temporal variability of crop water requirements while minimizing resource consumption. Center pivot irrigation systems are widely deployed in large-scale farming operations; however, their conventional control architectures are typically based on fixed schedules or linear feedback laws, which are insufficient to handle the nonlinear dynamics, uncertainties, and disturbances... F.R. Jimenez Lopez, A. Jimenez, I.A. Ruge Ruge, D.Y. Garcia Ramirez |
4. Deep Learning-based Anomaly Detection System for Rice Crop Health MonitoringGlobal food security relies heavily on the stable production of rice (Oryza sativa L.), yet cultivation remains vulnerable to various phytosanitary anomalies, including foliar diseases like Pyricularia and Rhynchosporium, scald, and abiotic stressors such as herbicide damage. Traditional agronomic management relies on visual scouting, which is inherently subjective, labor-intensive, and often leads to delayed interventions. This study proposes an automated, high-throughput solution for real-time... F.R. Jimenez Lopez, A. Jimenez, D.Y. Garcia Ramirez |
5. Plot2Phenome: A UAV-Based Deep Learning Framework for Automated Micro-Plot Segmentation and Plot Level PhenotypingAutomated micro-plot segmentation is a foundational requirement for plot-level phenotyping from UAV orthomosaics in field breeding trials. However, reliable delineation of individual plots remains difficult in realistic agronomic settings, particularly under canopy closure that erodes inter-plot gaps and under irregular or degraded plot boundaries caused by lodging, variable emergence, and field operations. These conditions reduce boundary contrast, increase instance adjacency, and introduce shape... Y. Li, C. Jin, X. Zhang |
6. UAV-Based Multispectral Modelling of Biomass and Crude Protein Yield for Green Biorefinery ApplicationsIn animal production systems, protein demand is steadily increasing due to global population growth. This rising demand has highlighted the need to identify alternative and sustainable protein sources. Green biorefinery systems can efficiently extract protein from plant biomass. Previous studies confirmed that perennial grass crops such as Perennial Ryegrass, Festulolium, and Tall Fescue can produce high-quality biomass suitable for protein extraction. An estimation model of biomass yield and... M. Canciani, E. Han, U. Jørgensen, Y. Ikeda, N.P. Hansen, S.K. Jensen, M.R. Weisbjerg, T. Didion |
7. Main Environmental and Variety Drivers of Cotton Seed Quality: Historical Insights from the United States Cotton BeltCotton seed quality traits including oil content, nitrogen (protein), and gossypol significantly influence seed value and end-use applications, yet their predictability based on environmental conditions across varied U.S. growing regions remains poorly understood. This study aimed to: (i) identify critical environmental predictors of seed composition; (ii) build machine learning models to predict seed quality as a function of seasonal weather patterns; and (iii) assess differences... A. Dhaliwal, L. Bastos, K. Sv, A. Bhattarai, A. Jakhar, K. Poudel, D.M. Mccallister, S.Y. Jaconis |