Proceedings
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| Filter results5 paper(s) found. |
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1. Model for Remote Estimation of Nitrogen Contents of Corn Leaf Using Hyper-Spectral Reflectance under Semi-Arid Condition.Accuracy and precision of nitrogen estimation can be improved by hyperspectral remote sensing that leads... M. Tahir |
2. Enhancing Farmers' Indigenous Knowledge Management in Cassava Varietal Trial Using Agro Ecosystem Analysis, Farmers' Drama Group and Animations in Eastern part of Nigeria.Researchers continue to come up with new varieties but farmer perspectives and preferences are very important factors for new varieties to spread in farmers’ communities. Researcher priorities alone are not enough. A variety may be ‘scientifically perfect... C.C. Asiabaka, M.O. Adesope, C.C. Ifeanyi- obi, R.N. Nwakwasi, F. Nnadi, E.C. Matthews- njoku, J. Chikaire |
3. Safety and Certification Considerations for Expanding the Use of UAS in Precision AgricultureThe agricultural community is actively engaged in adopting new technologies such as unmanned aircraft systems (UAS) to help assess the condition of crops and develop appropriate treatment plans. In the United States, agricultural use of UAS has largely been limited to small UAS, generally weighing less than 55 lb and operating within the line of sight of a remote pilot. A variety of small UAS are being used to monitor and map crops, while only a few are being used to apply agricultural... H. Verstynen, K. Hayhurst, J. Maddalon, N. Neogi |
4. Optimizing Soil Nutrient Management: Agricultural Policy/environmental Extender (APEX) Model Simulation for Field Scale Phosphorous Loss Reduction in VirginiaManaging soil nutrients is crucial for enhancing crop productivity and meeting consumptions demands while minimizing environmental impacts. Sustainable agriculture relies on well-planned soil nutrient management strategies. Phosphorous (P) stands out among the 16 essential soil nutrients, particularly in Virginia, where natural P levels are typically low. Adequate amount of P is necessary for the early root formation and plant growth. However, excess amount of P in the soil leads to increase the... S. Kumari, J. Rathore, S. Mitra, M. Gardezi, O. Walsh |
5. Predicting Soybean Yield Using Remote Sensing and a Machine Learning ModelSoybean (Glycine max L.), a nutrient-rich legume crop, is an important resource for both livestock feed and human dietary needs. Accurate preharvest yield prediction of soybeans can help optimize harvesting strategies, enhance profitability, and improve sustainability. Soybean yield estimation is inherently complex because yield is influenced by many factors including growth patterns, varying crop physiological traits, soil properties, within-field variability, and weather conditions. The objective... M. Gardezi, O. Walsh, D. Joshi, S. Kumari, D.E. Clay, J. Rathore |