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Gonzalez Aguilera, C
Ginzberg, I
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Authors
Rodolfo, T.A
Gonzalez Zarate, O.J
Gonzalez Aguilera, C
Tenenboim, Y
Edan, Y
Ginzberg, I
Paz Kagan, T
Marañon Aguilar, E
Kastensmidt, F
Benevenuti, F
Gonzalez Aguilera, C
Topics
Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture
Type
Oral
Poster
Year
2026
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1. An Interpretable Machine Learning Framework for Soil Nutrient Assessment Based on pH and Electrical Conductivity

Understanding how the physical and chemical properties of soil influence nutrient availability is fundamental for advancing precision agriculture, as these properties directly affect the efficiency of macro- and micronutrient absorption by plants. In recent years, the increasing availability of open agricultural datasets has created new opportunities for developing data-driven frameworks capable of supporting large-scale soil assessment and decision-making. However, the effective integration of... T.A. Rodolfo, O.J. Gonzalez Zarate, C. Gonzalez Aguilera

2. Comparative Evaluation of Combined and Task Specific Detectors for Pomegranate Yield and Fruit Loss Detection

Fruit cracking and drop represent major sources of yield loss in pomegranate orchards; however, existing vision-based yield estimation methods focus on counting healthy fruit and do not usually capture losses occurring on-tree and on the orchard floor, thereby constraining their operational relevance. This study evaluates detection strategies for simultaneous yield and loss quantification, with a specific comparison between combined multi class models and task specific single class models. Detection... Y. Tenenboim, Y. Edan, I. Ginzberg, T. Paz Kagan

3. Hardware–Software Co-Design of Quantized CNN Inference for Edge AI in Precision Agriculture

Precision agriculture increasingly relies on real-time automated inspection systems to ensure crop quality and reduce manual labor in grain handling processes. Manual visual inspection, traditionally used for grain quality assessment, is inherently limited by low throughput, subjectivity, and high labor costs. To address these issues, automated vision-based inspection systems have been widely adopted in industrial environments, enabling high-throughput and consistent grain classification. Recent... E. Marañon Aguilar, F. Kastensmidt, F. Benevenuti, C. Gonzalez Aguilera