I designed and implemented a unified evaluation framework to systematically compare CNNs, contrastive Vision–Language Models, and generative Vision–Language Models for fine-grained crop disease classification under real-world domain shift.

The goal was not just accuracy under controlled conditions, but robustness across lab and field environments.

Many computer vision systems for agriculture perform well on curated lab images but degrade significantly when deployed in uncontrolled field conditions.

This project evaluated multiple architectural paradigms under explicit Lab vs Field separation to understand performance trade-offs and robustness characteristics.

AgriPath-LF16

111,307 images

16 crops

41 diseases

65 crop–disease pairs

Explicit Lab vs Field source separation

Available on HuggingFace:

Full dataset: https://huggingface.co/datasets/hamzamooraj99/AgriPath-LF16

Balanced subset (used in experiments): https://huggingface.co/datasets/hamzamooraj99/AgriPath-LF16-30k

The 30k subset preserves all classes and supports fair evaluation across domain conditions.

Domain-aware dataset (65 crop–disease classes with clear lab and field split)

Unified training and evaluation pipeline across:

LoRA fine-tuning for parameter-efficient adaptation

Deterministic decoding for generative models

Programmatic output parsing with invalid-generation penalties

Macro F1 as primary metric

Separate evaluation for Lab-only, Field-only, and combined regimes

Designed modular training pipelines supporting CNN, contrastive, and generative VLM backbones under a unified interface

Implemented parameter-efficient fine-tuning (LoRA) to reduce GPU memory footprint while maintaining adaptation capacity

Optimised training on RTX 4090 and A100 using mixed precision (bf16)

Built automated evaluation scripts handling multi-split benchmarking and structured output validation

Integrated experiment tracking and artifact versioning using Weights & Biases

Implemented deterministic inference configurations to eliminate stochastic evaluation variance

PyTorch, Transformers, PEFT, Unsloth, PyTorch Lightning, Weights & Biases