Abstract
Balancing the accuracy and interpretability of predictive models has been a persistent challenge in traditional approaches. In this study, we advance this field by integrating cutting-edge artificial intelligence (AI) techniques with Explainable AI (XAI) methodologies to significantly enhance both the accuracy and interpretability of vineyard leaf disease predictions. We employ state-of-the-art convolutional neural networks (CNNs) and introduce a fine-grained model architecture featuring, adept at discerning subtle disease indicators in vineyard leaves. This innovative approach not only boosts the diagnostic performance of the models but also provides clear visualizations of the decision-making processes. This study utilizes a focused dataset strategy, incorporating one specialized grape disease dataset (Esca) and a subset of the general PlantVillage dataset, specifically selecting categories relevant to Apple and Grape diseases. The obtained results have demonstrated our model’s exceptional capability in accurately identifying and classifying various leaf diseases, showcasing its practical applicability in real-world vineyard management. Furthermore, our approach addresses the vital need for transparency and trust in AI applications within agriculture, particularly in viticulture.
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Acknowledgement
This study is a part of “VIPA-DELF” sub-project and has indirectly received funding from the European Union’s Horizon Europe research and innovation action programme, via the CHAMELEON Open Call #1 issued and executed under the CHAMELEON project (Grant Agreement no. 101060529). The technical work done in this study has benefited from the Experimental Infrastructure for Exploration of Exascale Computing (eX3), which is financially supported by the Research Council of Norway under contract 270053
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Mobeen, N.E., Shaikh, S., Nweke, L.O., Abomhara, M., Yayilgan, S.Y., Fahad, M. (2024). Vineyard Leaf Disease Prediction: Bridging the Gap Between Predictive Accuracy and Interpretability. In: de la Iglesia, D.H., de Paz Santana, J.F., López Rivero, A.J. (eds) New Trends in Disruptive Technologies, Tech Ethics, and Artificial Intelligence. DiTTEt 2024. Advances in Intelligent Systems and Computing, vol 1459. Springer, Cham. https://doi.org/10.1007/978-3-031-66635-3_9
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DOI: https://doi.org/10.1007/978-3-031-66635-3_9
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