Proceedings
Authors
| Filter results5 paper(s) found. |
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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 |
3. Evaluation of Vertical C/N Distribution in Maize Canopy Using Ensemble Learning with Hyperspectral DataMetabolic status of carbon (C) and nitrogen (N) as two essential elements in crop plants has essential influence on the ultimate formation of yield and quality in crop production. Ratio of carbon to nitrogen (C/N), defined as the ratio of LCC (leaf carbon concentration) to LNC (leaf nitrogen concentration), is useful for understanding and quantifying carbon and nitrogen metabolism in crops, and is one metrics for effectively evaluating the balance of carbon and nitrogen, nutrient status and growth... X. Xu, X. Xu, Y. Meng, G. Yang, Y. Song, H. Xue |
4. Evaluation of Lettuce Image Classification with CNNs under Different NPK Nutritional ConditionsThe growing global demand for food has driven the development of technologies aimed at increasing productive efficiency in sustainable agricultural systems, such as hydroponics. In this context, proper monitoring of nutrient solutions is essential, particularly for the early detection of nitrogen (N), phosphorus (P), and potassium (K) deficiencies, which directly affect lettuce growth, yield, and quality. Traditional nutritional diagnostic methods often rely on destructive laboratory analyses,... E.L. Silva, E. Freitas, V.C. Secundino, D.G. Gomes |
5. Detection of Banana Bunches and Peduncles in the Prata Catarina Cultivar Using Faster R-CNN With Transfer LearningBanana is one of the most produced and consumed fruits worldwide, being strategic for precision agriculture, especially in applications aimed at intelligent management and automated harvesting. Its economic and social relevance in tropical countries reinforces the need for technological solutions that increase productive efficiency and reduce losses in the field. In this context, the automatic detection of bunches and stalks in a natural environment represents a relevant challenge due to occlusion,... Y. Costa G. Da Silva, E. Freitas, P.S. Costa, D.V. Beserra, D.G. Gomes |