Publication
Exploring explainable AI techniques for plant disease classification in digital agriculture
| dc.contributor.author | Porfírio, Rui Pedro | |
| dc.contributor.author | Madeira, Rui Neves | |
| dc.contributor.author | Santos, Pedro Albuquerque | |
| dc.date.accessioned | 2025-09-22T14:55:16Z | |
| dc.date.available | 2025-09-22T14:55:16Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Integrating smart farming technologies in agriculture is crucial to address the pressing challenges of food security, economic stability, and environmental sustainability. Solutions based on artificial intelligence (AI) for plant disease detection play a critical role in optimizing crop health and yield. However, the complexity and opacity of these AI models can hinder their acceptance and practical application by end-users. To address this issue, our ongoing research explores applying explainable AI (XAI) techniques to enhance the explainability of vision-based plant disease classification models. Our experiments assess the transparency of vision-based models by applying XAI techniques, such as LIME, Grad-CAM, and occlusion-based attribution, to visualize the reasoning behind model predictions. Additionally, we analyze inherently transparent machine learning models, such as k-Nearest Neighbors, through custom visualization graphics and examine how explainability varies with model accuracy. These findings highlight the role of XAI techniques in enhancing the transparency of predictions for crucial vision tasks in agriculture such as plant disease classification. Building on these results, we explore the potential integration of X AI techniques into third-party applications or prototypes, emphasizing how tailored visual and textual explanations can enhance the transparency and interpretability of plant disease classification models. | eng |
| dc.identifier.citation | Porfírio, R., Madeira, R. N. & Santos, P. A. (2025). Exploring explainable AI techniques for plant disease classification in digital agriculture. Procedia Computer Science, 265, 175-182 | |
| dc.identifier.doi | 10.1016/j.procs.2025.07.170 | |
| dc.identifier.issn | 1877-0509 | |
| dc.identifier.uri | http://hdl.handle.net/10400.26/58777 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.relation.hasversion | https://www.sciencedirect.com/science/article/pii/S1877050925022203?via%3Dihub | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Plant Disease Detection | |
| dc.subject | Digital Agriculture | |
| dc.subject | Explainable AI | |
| dc.subject | Machine Learning | |
| dc.subject | Computer Vision | |
| dc.title | Exploring explainable AI techniques for plant disease classification in digital agriculture | eng |
| dc.type | contribution to journal | |
| dspace.entity.type | Publication | |
| oaire.citation.title | Procedia Computer Science | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 |