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. A Decision Support Tool for Developing Aflatoxin Risk Maps in Peanut FieldsAspergillus flavus (A. flavus) is a soil fungus that contaminates preharvest peanuts (Arachis hypogea) with the carcinogenic secondary metabolite aflatoxin. Because aflatoxin can cause serious illness or death at low concentrations (μg kg-1 to mg kg-1), its presence in foods and feeds is strictly regulated by food safety agencies around the world. Based on previous research by the authors, a hypothesis was developed that aflatoxin contamination... G. Vellidis, S. Maktabi, K. Boote, G. Hoogenboom, L. Lacerda, C. Pilcon, S. Shrestha, R. Wiggins |
3. 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 |
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. Non-destructive Detection of Herbicide Damage in Curly Lettuce Using Spectral Data and Machine Learning AlgorithmsCurly lettuce (Lactuca sativa var. crispa) is a prominent horticultural crop due to its high demand for both production and human consumption. It plays a vital role in creating healthier, more balanced diets. However, the application of phytosanitary products, such as herbicides, whether applied by air or land, can lead to chemical drift into adjacent areas, negatively impacting sensitive crops. This drift can cause phytotoxicity and, in severe cases, result in total crop loss, depending on factors... F. Morlin Carneiro, T. Vidigal Maciel, G. Albuquerque Araujo, H. De Oliveira Cavalheiro, B. Matwijou, M.A. Da Silva, A. Lopes De Brito Filho, M.G. Da Silva Brochado, J.D. Rodrigues Oliveira |