Proceedings
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| Filter results4 paper(s) found. |
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1. Democratizing Prescriptive Agronomy: Quality-Preserving Edge AI for Sugar BeetsThe global sugar beet sector faces a critical production paradox where agronomic interventions designed to maximize root yield often compromise sucrose concentration and processing quality. While precision agriculture aims to navigate this delicate balance, current methodologies have reached a methodological impasse. Existing solutions are bifurcated between descriptive data-intensive machine learning (ML), which struggles to generalize across heterogeneous fields, and physiological Process-Based... A. Tabbassi, S. Henkler, A. Zakhary, K. Rother |
2. Embrapii: What Can We Learn About Precision Agriculture and Environmental Sustainability After Investments of US$100 Million+ in Agricultural Industrial Innovation in Brazil?Innovation policies have increasingly targeted digital and sustainable transformation, yet systematic assessments of large-scale public-private investments in precision agriculture innovation remain scarce, particularly in tropical contexts. This article analyzes the experience of Embrapii (Brazilian Agency for Research and Industrial Innovation) after fostering more than US$100 million in investments in over 600 research, development, and innovation (RD&I) projects applied to agriculture... J. Videira Menezes, E.M. Dias, M.L. Rebello Pinho Dias Scoton, D.H. Oliveira, I. Mendes Gaya Lopes Dos Santos, F. Stallivieri, L. Cunha De Sousa |
3. RAVI: A QGIS plugin for satellite remote sensing applications of Vegetation Indices and SAR data in Precision AgricultureRemote Sensing (RS) plays a fundamental role in Precision Agriculture (PA), particularly through the use of satellite imagery to identify spatial variability within the fields. Compared to traditional methods for detecting field variability, such as soil sampling, yield mapping, and proximal sensors, RS offers advantages in reduced operational costs, lower labor demands, and greater spatial coverage. Analyzing vegetation indices (VIs) over time allows to track crop phenological development, identify... |
4. Semantic Segmentation Comparison of Prata Catarina Banana Bunches Using Convolutional Neural Network ModelsThe identification and classification of banana ripening stages are essential for production assessment in large scale plantations, enabling efficient harvest monitoring and ensuring fruit quality for commercialization. This study presents a comparative evaluation of three deep learning architectures applied to the semantic segmentation of Prata Catarina banana bunches, aiming to support automated monitoring systems and decision-making tools for precision agriculture applications. The evaluated... J. Rodrigues Moreno, E.L. Silva, E. Freitas, D. G. Gomes, Y. Costa G. Da Silva |