Jal Anveshak: Prediction of fishing zones using fine-tuned LlaMa 2
This addresses the problem of inefficient fishing practices for Indian fishermen in coastal areas, but it appears incremental as it applies an existing method (fine-tuning Llama 2) to new data.
The authors tackled the problem of untapped potential in using AI for Indian fishermen by introducing Jal Anveshak, an application framework that predicts fishing zones using a fine-tuned Llama 2 model on government data, aiming to help fishermen maximize yield and resolve queries in multilingual and multimodal ways.
In recent years, the global and Indian government efforts in monitoring and collecting data related to the fisheries industry have witnessed significant advancements. Despite this wealth of data, there exists an untapped potential for leveraging artificial intelligence based technological systems to benefit Indian fishermen in coastal areas. To fill this void in the Indian technology ecosystem, the authors introduce Jal Anveshak. This is an application framework written in Dart and Flutter that uses a Llama 2 based Large Language Model fine-tuned on pre-processed and augmented government data related to fishing yield and availability. Its main purpose is to help Indian fishermen safely get the maximum yield of fish from coastal areas and to resolve their fishing related queries in multilingual and multimodal ways.