Mkhatshwa, JuniorKavu, TatendaDaramola, Olawande2024-12-092024-12-092024-06Mkhatshwa, J.; Kavu, T.; Daramola, O. Analysing the Performance and Interpretability of CNN-Based Architectures for Plant Nutrient Deficiency Identification. Computation 2024, 12, 113. https://doi.org/10.3390/computation12060113.2079-3197 (online)10.3390/computation12060113http://hdl.handle.net/2263/99814DATA AVAILABITY STATEMENT: The data are publicly available.Early detection of plant nutrient deficiency is crucial for agricultural productivity. This study investigated the performance and interpretability of Convolutional Neural Networks (CNNs) for this task. Using the rice and banana datasets, we compared three CNN architectures (CNN, VGG16, Inception-V3). Inception-V3 achieved the highest accuracy (93% for rice and banana), but simpler models such as VGG-16 might be easier to understand. To address this trade-off, we employed Explainable AI (XAI) techniques (SHAP and Grad-CAM) to gain insights into model decision-making. This study emphasises the importance of both accuracy and interpretability in agricultural AI and demonstrates the value of XAI for building trust in these models.en© 2024 by the authors. Open Access. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).Machine learningDeep learningConvolutional neural networkPlant nutrient deficiencyExplainable artificial intelligenceSDG-02: Zero hungerSDG-09: Industry, innovation and infrastructureArtificial intelligence (AI)Article