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Evaluating Contextual Bias in CNN Image Classification: Evidence from Agricultural Benchmark Datasets

arXiv · AI, language, vision and robotics · article · Sep 13, 2026 · UTC

Convolutional neural networks (CNNs) are typically evaluated using held-out classification accuracy, an approach that presupposes predictions are based primarily on the intended object of interest rather than incidental surrounding context. We test this assumption in CNN-based agricultural image classification by comparing model performance on original images with performance on background-dominated patches extracted from the same images across eight publicly available agricultural benchmark datasets and four widely used CNN architectures. Background-dominated patches were classified above dat

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