Comparing Vision Transformers against leading CNN architectures to accurately classify grass and broadleaf weeds — enabling targeted spraying that cuts herbicide waste and protects the environment.
Blanket chemical spraying across large fields wastes herbicides and labour, pollutes the environment, and degrades food quality. Accurately identifying weeds so they can be sprayed selectively is critical for sustainable agriculture — but CNNs demand heavy compute, are prone to overfitting, and need large, balanced labelled datasets.
Investigate whether Vision Transformers — typically used in NLP — can match or beat established CNNs at classifying grass versus broadleaf weeds, and deliver a modular pipeline for training and testing weed-classification models.