Environmental sensor readings in two crop disease image datasets identify the session in which each image was taken
Integrating environmental sensor data with leaf imagery is widely reported to boost crop disease classification accuracy. In this work, we reveal that these reported gains are often artifacts of dataset construction: because a single sensor reading is shared across many images collected in a single session (one farm on one date), multimodal networks can predict disease simply by memorizing session identities. Analyzing two widely used Korean datasets, the Crop Disease Diagnosis (CDD) benchmark and an AI Hub pest/disease dataset, we demonstrate that nearly all images share sensor values, with 91.9% of CDD test images having exact sensor duplicates in the training set. Remarkably, an image-...
| Authors | Sungwoo Kang |
|---|---|
| Primary category | cs.CV |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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