ZimCropGuard: multi-crop disease detection under domain shift

Status: research in progress. The results below are preliminary. This page describes an active programme, not a finished product, and any numbers should be read as work in progress rather than final claims.

The idea

ZimCropGuard is a hybrid deep-learning framework for detecting crop disease across multiple Zimbabwean staple crops, from a single phone photo, under the messy conditions farmers actually shoot in. It builds on my completed BSc dissertation, a hybrid CNN-Transformer for tomato leaf disease that is already deployed, and scales the same robustness-first philosophy to a much larger, multi-crop problem.

Scale. 5 Zimbabwean staple crops, 21+ disease classes, and 54,000+ training images.

The central hypothesis

Models trained on clean laboratory imagery, such as PlantVillage, systematically underperform on real African field conditions. The distribution shifts across lighting, camera quality, background clutter, cultivar, and leaf position, and a model that has only ever seen lab data does not survive the move outdoors. The hypothesis under test is that domain-adaptive training measurably closes that gap. Quantifying the shift, and the recovery, is the point of the research.

Architecture

Two complementary backbones read each image in parallel:

• EfficientNetB0 captures fine local texture, the lesion-level detail that distinguishes one disease from another.
• ViT-B/16 captures global structure, the arrangement and context across the whole leaf.

Their features are concatenated and fused through learned dense layers, a learned-fusion ensemble rather than a simple average of two predictions, so the model can weigh local and global evidence per case.

Training strategy

A staged pipeline, designed for a large and imbalanced multi-crop dataset:

1. Freeze and pretrain the backbones on the combined dataset.
2. Head training on the fused classifier while the backbones stay frozen.
3. Full fine-tune end to end at a low learning rate.
4. Domain calibration on a mixed set of laboratory and real field images, with focal loss to handle heavy class imbalance across 21+ diseases.

Recognition

• Best Poster, IndabaX Zimbabwe 2026.
• Selected to represent Zimbabwe at the Deep Learning Indaba 2027.

Deployment vision

The intended delivery is a Streamlit web app plus an offline-capable progressive web app (PWA), so the tool works with intermittent connectivity in the field. Multi-language support is planned for English, Shona, and Ndebele, alongside a self-improving loop that learns from farmer-submitted uploads over time.

Where it stands

The dissertation-stage tomato model proved the robustness approach end to end and is live. ZimCropGuard extends it to five crops with a larger dual-backbone architecture and an explicit domain-shift study. That study is ongoing, and the preliminary results are promising enough to have earned the poster award, with the full evaluation still in progress.