  In recent times, there has been an increasing awareness about imminent
environmental challenges, resulting in people showing a stronger dedication to
taking care of the environment and nurturing green life. The current $19.6
billion indoor gardening industry, reflective of this growing sentiment, not
only signifies a monetary value but also speaks of a profound human desire to
reconnect with the natural world. However, several recent surveys cast a
revealing light on the fate of plants within our care, with more than half
succumbing primarily due to the silent menace of improper care. Thus, the need
for accessible expertise capable of assisting and guiding individuals through
the intricacies of plant care has become paramount more than ever. In this
work, we make the very first attempt at building a plant care assistant, which
aims to assist people with plant(-ing) concerns through conversations. We
propose a plant care conversational dataset named Plantational, which contains
around 1K dialogues between users and plant care experts. Our end-to-end
proposed approach is two-fold : (i) We first benchmark the dataset with the
help of various large language models (LLMs) and visual language model (VLM) by
studying the impact of instruction tuning (zero-shot and few-shot prompting)
and fine-tuning techniques on this task; (ii) finally, we build EcoSage, a
multi-modal plant care assisting dialogue generation framework, incorporating
an adapter-based modality infusion using a gated mechanism. We performed an
extensive examination (both automated and manual evaluation) of the performance
exhibited by various LLMs and VLM in the generation of the domain-specific
dialogue responses to underscore the respective strengths and weaknesses of
these diverse models.
