Proceedings
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| Filter results7 paper(s) found. |
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1. 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 |
2. Evaluating Deep Learning Models for Image-Based Corn Kernel Detection, Counting and Yield PredictionAccurate estimation of kernel number in corn is essential for evaluating yield potential in breeding and agronomic research. However, manual kernel counting is labor-intensive, prone to human error, and impractical for large-scale datasets, while most existing automated devices are limited to simple counting tasks. This study evaluates deep learning-based approaches for automated kernel detection and counting using You Only Look Once models and Faster R-CNN. Specifically, YOLOv8x, YOLOv10x, and... B. Ghimire, L. Lacerda, T. Bourlai, G. Lu |
3. Evaluating APSIM for Precision Optimization of Planting Windows and Nitrogen Management in Maize-Soybean Intercropping Systems in MalawiMaize-soybean intercropping is a key strategy for improving food security and resource-use efficiency in smallholder systems in sub-Saharan Africa. In Malawi, soybean promotion supports sustainable intensification, yet optimizing planting windows, spatial arrangements, and nitrogen (N) management under variable rainfall remains challenging. This study assesed the capability of the Agricultural Production Systems Simulator (APSIM) to simulate maize-soybean performance and identify precision... |
4. Estimating Peanut Losses Using Machine Learning with Soil and Weather DataMechanized peanut harvesting is an important phase of the production system, directly affecting both production costs and crop yield. However, due to interactions among soil conditions, plant characteristics, and machine performance, this operation is carried out under challenging conditions that may result in high levels of loss. These losses are classified as visible when pods remain on the soil surface after digging and as invisible when they are incorporated into the soil profile, making them... A. Lopes De Brito Filho, F. Morlin Carneiro, G. Pereira Costa, B. Dos Santos Silva, P.H. Nogueira Gusmão, R.P. Pereira Da Silva |
5. Orchestration of Missions for Coordination between Autonomous Agents in Agriculture.Due to technological advancement in agriculture, various autonomous agents, such as drones, mobile robots, and intelligent agricultural vehicles, are being used to automate repetitive tasks and increase agricultural production. In addition, these agents, often developed by different manufacturers and endowed with different capabilities, form a highly heterogeneous environment, imposing a central challenge to be solved: the need to manage these agents so that they can act in a coordinated and effective... V. Fontena, N.K. Wagner, C. Teixeira, J. Lopes, L. Moura, C. Guimarães |
6. Mapping Digital Technologies, Cloud Platforms, and Artificial Intelligence in Precision Agriculture: The Software Baseline for a Citrus and Sugarcane Living Lab.The digital transformation of Precision Agriculture (PA) has been driven by the growing availability of Farm Management Information Systems (FMIS), cloud platforms, and Artificial Intelligence (AI) solutions. This study, linked to the Smart B100 Science for Development Center (CCD-SB100), funded by FAPESP and led by the Agronomic Institute of Campinas (IAC), Faac/Unesp (Bauru), in partnership with FATEC Pompeia, aimed to build a multicriteria matrix (technological inventory) of digital PA solutions... M. Mazega, H. Fortinis, H. Fischer, C.K. Luvizotto, C.E. Otoboni, M.C. De Almeida |
7. Spatial Distribution of Coffee Leaf Miner Infestation and Its Impact on Coffee Fruit Maturation, Yield, and Beverage QualityDifferences in the maturation rate of coffee fruits can be associated with plant stress. The incidence of pests, such as the coffee leaf miner (Leucoptera coffeella), compromises the photosynthetically active area, which can reduce yield and beverage quality. Computer vision can assist in damage reduction by identifying the pest's spatial and temporal behavior. This study aimed to verify, spatially and temporally, the impact of damage caused by the coffee leaf miner on fruit... L.V. Lazzarini, A. , G.P. Cândido, V.M. Nunes, S.M. Hurtado, F.H. Leandro, I.D. Gonçalves |