Year: 2026
Type: article
Abstract: Machine translation is fundamental in closing the gap between different languages, especially in the areas of con-cern and expertise such as agriculture.With the increase of digital tool usages in the agricultural practice, such an accurate and contextsensitive translation is increasingly significant.Proper delivery of agricultural information, including farm methods, weather advisories, and crop suggestions is essential among farmers, farm laborers, policymakers and researchers.Nevertheless, typical text-based translation frameworks tend to be less than optimal because of uncertainness and a restricted knowledge of context.To address these shortcomings, the proposed study refers to Multimodal Machine Translation (MMT) to incorporate textual and visual information to enhance accuracy.Gated Fusion Transformer (GFT) model has been customized to the agricultural field so that the problem of ambiguity in contexts and inconsistencies in translation can be eliminated.Training and evaluation were done using the multilingual benchmark dataset known as FLORES-200.Two commonly employed measures of performance were used, i.e.BLEU and METEOR.The system under proposal produced a BLEU of 58.2; METEOR score of 0.71, a high level and contextually relevant translation indicator.Besides benchmarking the GFT model in agricultural terms, this work adds value to the research community by offering a basis on which future development of multimodal translation systems in low-resource settings with domain-specific applications may be done.
Authors: PRIYANKA SURAM, Pramoda Patro
Institution: SR University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: bronze
OpenAlex ID: W7160898528