# Seeking Independent Reviewers: Wheat Rust AI Cross-Dataset Generalisation > Source: > Published: 2026-11-05 14:04:46+00:00 Hello Hugging Face community! I’m Devanandha VS, an independent AI researcher and MSc Artificial Intelligence graduate from Ulster University, UK. I’m seeking independent researchers, computer-vision practitioners and open-source developers interested in evaluating an agricultural AI research project. **Wheat Rust AI Detection** is an open-source deep-learning project using a MobileNetV2-based classifier to identify three wheat leaf categories: Healthy Brown Rust Yellow Rust The classifier achieved **98.78% accuracy (728/737 correct predictions)** on its internal validation dataset. However, subsequent evaluations using independent datasets revealed substantial generalisation weaknesses. Rather than presenting the initial accuracy as proof of real-world reliability, I investigated and publicly documented the model’s limitations, unsuccessful experiments and Grad-CAM explainability findings. The project is an experimental research demonstration, not a production-ready agricultural diagnostic system. I welcome independent contributions in four areas: **1. Cross-Dataset Generalisation** Evaluate the model on independent wheat leaf datasets and investigate differences in performance across data sources. **2. Reproducibility** Attempt to reproduce the documented experiments, including preprocessing, evaluation methodology and dataset configurations. **3. Explainable AI** Review the Grad-CAM analysis and assess the usefulness and limitations of the generated explanations. **4. Model Reliability** Explore improved evaluation methods, relevant baselines, calibration techniques and strategies for addressing dataset shift. I’ve opened a dedicated GitHub issue for independent evaluation and collaboration: **Independent Reproduction and External Validation:** **Project Repository:** Contributors are welcome to report findings, suggest improvements, submit pull requests or discuss potential methodological limitations. Positive feedback, critical assessments and unsuccessful reproduction attempts are equally welcome. When evaluating agricultural image classifiers across independent datasets, what factors would you investigate first? Differences in image acquisition and environmental conditions? Class definitions and annotation quality? Data preprocessing and dataset composition? Domain shift, class imbalance or model calibration? I’d appreciate hearing your experience and welcome collaboration on a more rigorous evaluation. Thank you for your time and consideration! **Devanandha VS** MSc Artificial Intelligence, Ulster University Independent AI Researcher