Proceedings
Authors
| Filter results2 paper(s) found. |
|---|
1. Machine Learning Pipeline to Estimate Soybean Rust Severity Using UAV-derived Multispectral IndicesAsian Soybean Rust is one of the most destructive diseases affecting soybean crops worldwide and can result in yield losses of up to 90% when control measures are not implemented in a timely manner. Conventional disease monitoring based on field scouting is time-consuming, labor-intensive, and inherently subjective, often failing to adequately represent the spatial variability of disease across production fields. These limitations highlight the need for automated, objective, and high throughput... S.A. Teixeira, R. Valdivino, R. Tsukahara, M. Ribeiro |
2. Utilization of Proximal Remote Sensing As a Non-destructive Method for Assessing the Quality of Corn SeedsThe physiological quality of corn seeds plays a key role in crop establishment. It directly influences final productivity. Although germination and vigor tests are well established, they have practical limitations. These tests are time-consuming. They require laboratory infrastructure and can involve destructive procedures. These factors limit their use in situations demanding faster, scalable assessments. In this scenario, proximal remote sensing has gained attention as a practical, non-destructive... A.E. Dos Reis Rodrigues, M.A. Da Silva, J.D. Rodrigues Oliveira, G. Ribeiro Silva, A. Lopes De Brito Filho, M.G. Da Silva Brochado, N.G. Krohn, F. Morlin Carneiro |