Analysing the performance and interpretability of CNN-based architectures for plant nutrient deficiency identification
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Date
Authors
Mkhatshwa, Junior
Kavu, Tatenda
Daramola, Olawande
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
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.
Description
DATA AVAILABITY STATEMENT: The data are publicly available.
Keywords
Machine learning, Deep learning, Convolutional neural network, Plant nutrient deficiency, Explainable artificial intelligence, SDG-02: Zero hunger, SDG-09: Industry, innovation and infrastructure, Artificial intelligence (AI)
Sustainable Development Goals
SDG-02:Zero Hunger
SDG-09: Industry, innovation and infrastructure
SDG-09: Industry, innovation and infrastructure
Citation
Mkhatshwa, 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.