A Response-by-Retrieval Chatbot for Enhancing Horticulture Extension Services in Tanzania

Horticulture, which encompasses the cultivation of flowers, fruits, herbs, and vegetables, is a key contributor to Tanzania’s export revenue generation. Smallholder farmers are the primary producers of these crops, and they rely heavily on extension services for critical information that shapes both their economic success and long-term sustainability. However, the delivery of such services from the government and other stakeholders faces challenges, including constraints in human capital, geographic barriers, misaligned information needs, as well as issues with the timeliness of information dissemination. To address these challenges, this study developed a Swahili-language chatbot designed to provide timely, context-specific information tailored to the needs of farmers. To ensure credibility and relevance, key private and public stakeholders were consulted, and comprehensive farming guides were collected to build a custom dataset. This dataset consisted of 307 passages and 2,231 question-answer pairs. Four multilingual models, Multilingual Bidirectional Encoder Representations from Transformers (mBERT), Cross-lingual Language Model Pretraining RoBERTa (XLM-R), Multilingual Decoding-Enhanced BERT with Disentangled Attention (mDeBERTa), and Afro Cross-lingual Language Model Pretraining RoBERTa (AfroXLMR), were finetuned on this dataset for a question-answering task. Among them, the mDeBERTa model achieved the strongest performance, with an Exact Match (EM) score of 62.69% and an F1 score of 75.35%. These results demonstrate the potential of adapting advanced language models for specialized, low-resource language tasks in agriculture. The deployment of mDeBERTa in a prototype chatbot highlights a promising pathway to bridge information gaps and enhance the accessibility of extension services for Tanzania’s smallholder farmers.