What question did this study set out to answer?

The research aims to improve transparency and interpretability in machine learning models for precision agriculture.

February 12, 2026Open Access

Improving transparency in machine learning for agriculture: a case study on mushroom classification using CNN and Grad-CAM

Key Points

The research aims to improve transparency and interpretability in machine learning models for precision agriculture.
Reviewed ML and XAI techniques suitable for precision agriculture
Developed a 2D-CNN model for mushroom hyperspectral image classification
Applied Grad-CAM to identify critical pixels influencing model classifications
Achieved average accuracy of 98.04%
Measured precision at 98.96% and recall at 96.30%
F1-score reached 97.51% highlighting model effectiveness

Abstract

Machine learning (ML) is a powerful tool for precision agriculture (PA) in general. However, its widespread adoption is constrained by lacking transparency. To address this gap, this study integrates ML with Explainable AI (XAI) methods to enhance both the predictive performance and interpretability of the respective models. We provide a review of ML and XAI techniques suitable for PA, with a specific focus on Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM). Furthermore, we conducted a case study on mushroom hyperspectral image classification. A 2D-CNN model was developed, achieving average accuracy 98.04%, precision 98.96%, recall 96.30%, and F1-score 97.51%. Grad-CAM was then applied to identify the critical pixels that informed the model’s classifications. The results demonstrate that the integration of CNNs with Grad-CAM not only achieves high predictive accuracy on complex agricultural data but also enhances model interpretability.

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