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001050636 020__ $$a978-3-031-91834-6 (print)
001050636 020__ $$a978-3-031-91835-3 (electronic)
001050636 0247_ $$2doi$$a10.1007/978-3-031-91835-3_3
001050636 0247_ $$2ISSN$$a0302-9743
001050636 0247_ $$2ISSN$$a1611-3349
001050636 037__ $$aFZJ-2026-00388
001050636 1001_ $$0P:(DE-HGF)0$$aDel Bue, Alessio$$b0$$eEditor
001050636 1112_ $$aComputer Vision – ECCV 2024 Workshop$$cMilan$$d2024-09-29 - 2024-10-04$$wItaly
001050636 245__ $$aA Framework for Enhanced Decision Support in Digital Agriculture Using Explainable Machine Learning
001050636 260__ $$aCham$$bSpringer Nature Switzerland$$c2025
001050636 300__ $$a31 - 45
001050636 3367_ $$2ORCID$$aCONFERENCE_PAPER
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001050636 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1768464502_3993
001050636 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$mcontb
001050636 4900_ $$aLecture Notes in Computer Science$$v15625
001050636 520__ $$aModel explainability, which integrates interpretability with domain knowledge, is crucial for assessing the reliability of machine learning frameworks, particularly in enhancing decision support in digital agriculture. Efforts have been made to establish a clear definition of explainability and develop new interpretability techniques. Assessing interpretability is essential to fully harness the potential of explainability. In this paper, we compare Gradient-weighted Class Activation Mapping, an interpretability technique for Convolutional Neural Networks, with Raw Attentions for Vision Transformers. We analyze both methods in an image-based task to classify the harvest-readiness of cauliflower plants. By developing a model-agnostic framework to compare models based on explainability, we pave the way for more reliable digital agriculture systems.
001050636 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
001050636 588__ $$aDataset connected to CrossRef Book Series, Journals: juser.fz-juelich.de
001050636 7001_ $$0P:(DE-HGF)0$$aCanton, Cristian$$b1$$eEditor
001050636 7001_ $$0P:(DE-HGF)0$$aPont-Tuset, Jordi$$b2$$eEditor
001050636 7001_ $$0P:(DE-HGF)0$$aTommasi, Tatiana$$b3$$eEditor
001050636 7001_ $$00009-0001-8371-3414$$aEmam, Ahmed$$b4$$eCorresponding author
001050636 7001_ $$00000-0003-4301-1140$$aFarag, Mohamed$$b5
001050636 7001_ $$00000-0003-1145-1555$$aKierdorf, Jana$$b6
001050636 7001_ $$00000-0002-1941-150X$$aKlingbeil, Lasse$$b7
001050636 7001_ $$0P:(DE-Juel1)129388$$aRascher, Uwe$$b8
001050636 7001_ $$0P:(DE-Juel1)195965$$aRoscher, Ribana$$b9
001050636 773__ $$a10.1007/978-3-031-91835-3_3
001050636 8564_ $$uhttps://dl.acm.org/doi/abs/10.1007/978-3-031-91835-3_3
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