ADA 2025: A Multimodal Dataset for Water-Deficit Stress Detection in Sugar Beets

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-free classifier given only timestamps matches or exceeds sensor-driven predictions across all seven evaluated crops, and matches the published macro-F1 of a state-of-the-art CDD fusion model. These results indicate that performance gains on standard random splits cannot be disentangled from session leakage. We propose that multimodal crop studies must evaluate on session-held-out splits and report performance against sensor-free date-time baselines to ensure genuine generalization.

Early and accurate detection of crop stress is essential to improve agricultural productivity and ensure global food security. However, collecting a large labeled crop stress dataset is a challenging task. To address this challenge, we introduce a novel spatial-temporal stress network (STS-NET), built on a self-supervised 3D-convolutional autoencoder (3D-CAE), designed to utilize Satellite Image Time Series (SITS) data for crop stress detection. STS-NET exploits four vegetation indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Vegetation Index (GNDVI), Red-Edge Chlorophyll Index (RECI) and Normalized Difference Red-Edge Index (NDRE) obtained from high resolution Planetscope imagery to capture spatiotemporal stress patterns. The model is trained on our BSPT (Barnala Spatial-Temporal) dataset and evaluated on a real-world sugarcane dataset collected over a year from a 2.5-acre test plot located in Lakhimpur-Kheri (LK) district in Uttar Pradesh in India. STS-NET achieved a precision of 97. 98% for water stress, 85.08% for nitrogen stress, and 83.47% for combined stress. The results demonstrate the potential of STS-NET in effectively detecting stress in sugarcane crops with minimal reliance on labeled data. Furthermore, STS-NET can serve as a robust feature extractor for simpler models.

Despite rapid advances in artificial intelligence, reliable real-world plant disease detection remains a persistent challenge. Visual and deep learning approaches have shown promising results, but their deployment under field conditions remains limited. A key bottleneck is the reliance on laboratory-generated datasets that lack environmental diversity, realistic backgrounds, and balanced class distributions, resulting in poor generalization. In contrast, datasets collected directly from agricultural environments capture natural variability and better reflect challenges faced by farmers across regions. This review presents a critical analysis of visual and deep learning approaches for plant disease detection, with emphasis on plant disease datasets. It establishes a taxonomy based on acquisition setting, accessibility, plant diversity, disease composition, class structure, and imbalance severity, and examines their implications for model generalization and real-world deployment. A comparative analysis of laboratory and real-field datasets identifies critical gaps that hinder disease detection. The review further analyzes how multi-level dataset imbalance, including intra-class, inter-crop, and cross-dataset imbalance, and limited environmental variability affect model performance and robustness, an area insufficiently examined in existing surveys. Beyond image-based approaches, it highlights the importance of integrating environmental parameters such as temperature, humidity, and leaf wetness with image data to improve prediction under dynamic field conditions. Finally, the review identifies key challenges, research gaps, and future directions concerning dataset construction, environmental variability, structural imbalance, standardization, and multimodal disease monitoring. It provides a foundation for developing next-generation multimodal frameworks for precision agriculture.