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020 _ _ |a 978-3-031-91834-6 (print)
020 _ _ |a 978-3-031-91835-3 (electronic)
024 7 _ |a 10.1007/978-3-031-91835-3_3
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024 7 _ |a 0302-9743
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024 7 _ |a 1611-3349
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037 _ _ |a FZJ-2026-00388
100 1 _ |a Del Bue, Alessio
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111 2 _ |a Computer Vision – ECCV 2024 Workshop
|c Milan
|d 2024-09-29 - 2024-10-04
|w Italy
245 _ _ |a A Framework for Enhanced Decision Support in Digital Agriculture Using Explainable Machine Learning
260 _ _ |a Cham
|c 2025
|b Springer Nature Switzerland
300 _ _ |a 31 - 45
336 7 _ |a CONFERENCE_PAPER
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336 7 _ |a Conference Paper
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336 7 _ |a INPROCEEDINGS
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336 7 _ |a Contribution to a book
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490 0 _ |a Lecture Notes in Computer Science
|v 15625
520 _ _ |a Model explainability, which integrates interpretability with domain knowledge, is crucial for assessing the reliability of machine learning frameworks, particularly in enhancing decision support in digital agriculture. Efforts have been made to establish a clear definition of explainability and develop new interpretability techniques. Assessing interpretability is essential to fully harness the potential of explainability. In this paper, we compare Gradient-weighted Class Activation Mapping, an interpretability technique for Convolutional Neural Networks, with Raw Attentions for Vision Transformers. We analyze both methods in an image-based task to classify the harvest-readiness of cauliflower plants. By developing a model-agnostic framework to compare models based on explainability, we pave the way for more reliable digital agriculture systems.
536 _ _ |a 2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)
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700 1 _ |a Canton, Cristian
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700 1 _ |a Pont-Tuset, Jordi
|0 P:(DE-HGF)0
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700 1 _ |a Tommasi, Tatiana
|0 P:(DE-HGF)0
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700 1 _ |a Emam, Ahmed
|0 0009-0001-8371-3414
|b 4
|e Corresponding author
700 1 _ |a Farag, Mohamed
|0 0000-0003-4301-1140
|b 5
700 1 _ |a Kierdorf, Jana
|0 0000-0003-1145-1555
|b 6
700 1 _ |a Klingbeil, Lasse
|0 0000-0002-1941-150X
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700 1 _ |a Rascher, Uwe
|0 P:(DE-Juel1)129388
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700 1 _ |a Roscher, Ribana
|0 P:(DE-Juel1)195965
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773 _ _ |a 10.1007/978-3-031-91835-3_3
856 4 _ |u https://dl.acm.org/doi/abs/10.1007/978-3-031-91835-3_3
910 1 _ |a Forschungszentrum Jülich
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910 1 _ |a Forschungszentrum Jülich
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913 1 _ |a DE-HGF
|b Forschungsbereich Erde und Umwelt
|l Erde im Wandel – Unsere Zukunft nachhaltig gestalten
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915 _ _ |a Nationallizenz
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