Proceedings
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| Filter results5 paper(s) found. |
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1. Unveiling Research Patterns in Precision Agriculture: A Comprehensive Network Analysis of ICPA ProceedingsThe International Conference on Precision Agriculture (ICPA) is one of the most influential global forums dedicated to advancing technologies, methodologies, and scientific understanding in the domain of precision agriculture. Since its inception, the conference has served as a central platform for disseminating innovations in data-driven crop management, sensor technologies, spatial analysis, automation, and decision-support systems. Now in its 17th edition, the ICPA has accumulated more than... S. Camargo, J. Valiati |
2. Mobile Edge AI for Detection of Grape Clusters and Disease Symptoms in VineyardsPrecision viticulture demands accessible technological solutions that enable rapid disease diagnosis and production monitoring directly in the field. In real-world production contexts, dependence on cloud connectivity, external servers, or specialized hardware limits the adoption of computer vision tools by small and medium-sized farmers. In this context, this work presents a solution based on artificial intelligence embedded in a mobile application for the detection of grape bunches and leaves... E.M. Da Silveira, F.I. Nogueira, S.D. Camargo, A. Freire Campos, J. Valiati, E.F. Leite |
3. Assessing Metering and Spreading Performance of Drones (UAVs) for Application of Dry MaterialsAlong with pesticide applications, the use of UAVs (also commonly referred to as drones) for applying dry materials has increased rapidly in the United States. Currently, various types of product metering and spreading systems are available on commercial drones for applying dry materials. However, limited information is available on their application performance, especially the metering accuracy and the uniformity of distribution across the swath. Therefore, research studies were conducted to... S. Virk, E. Ward, J. Sizemore |
4. From Render to Field: Detecting Asian Soybean Rust Using Models Trained Exclusively on Synthetic ImageryTraining machine learning models for crop disease detection requires large, annotated datasets that are costly and difficult to obtain under variable field conditions. Asian Soybean Rust (ASR) can reduce soybean yield by up to 90% and costs Brazilian producers over US$2 billion per season in fungicide applications and yield losses. Despite this impact, existing machine learning studies on ASR remain scarce with no publicly available RGB... L.B. Fontoura, A. De Freitas, E. Farinati Leite, J. Valiati |
5. Spreading Performance of a UAV for Cover Crop (Cereal Rye) Seeding at Varying Application Rates and Flight SpeedsWith the increased use of UAVs for pesticide applications in agriculture, there is growing interest in their use for applying dry solid materials, especially for seeding cover crops. However, limited information currently exists on the application performance of UAVs for broadcasting cover crop seed and the effects of different operational parameters. Therefore, studies were conducted to assess the spreading performance of a DJI Agras T25 UAV under varying application rates (22.4, 33.6, 44.8,... S. Virk, J. Sizemore |