Meeting the growing food demand is a significant challenge, exacerbated by weed-induced crop production constraints. Conventional weed management methods, such as herbicides, have inadvertently fostered the emergence of resistant species, underscoring the imperative for precision agriculture approaches like site-specific weed management (SSWM). However, the success of SSWM, particularly when leveraging deep learning for weed identification, is hindered by limited, high-quality training data.
Meeting the growing food demand is a significant challenge, exacerbated by weed-induced crop production constraints. Conventional weed management methods, such as herbicides, have inadvertently fostered the emergence of resistant species, underscoring the imperative for precision agriculture approaches like site-specific weed management (SSWM). However, the success of SSWM, particularly when leveraging deep learning for weed identification, is hindered by limited, high-quality training data.
Generative models, notably Generative Adversarial Networks (GANs), offer a way to generate diverse weed data, improving plant classification and identification efforts. Nonetheless, a significant hurdle lies in generating synthetic images of high fidelity that faithfully represent weed species, highlighting the need for further refinement of these generative techniques tailored for agricultural applications.
