การยกระดับประสิทธิภาพการให้อาหารหนอนไหมตามช่วงวัยด้วยเทคโนโลยีแชทบอทเพื่อเพิ่มกำลังการผลิตผ้าไหม จังหวัดสุรินทร์

Suphattra Wayalun; Songsak Meesit; Suphacai Kaeochan

doi:10.65205/jcct.2026.e3896

Authors

Suphattra Wayalun Program in Computer Technology, Faculty of Industrial Technology, Surindra Rajabhat University, Surin 32000, Thailand https://orcid.org/0000-0001-9301-5591
Songsak Meesit Program in Computer Technology, Faculty of Industrial Technology, Surindra Rajabhat University, Surin 32000, Thailand
Suphacai Kaeochan Program in Mechanical and Automotive Engineering Technology, Faculty of Industrial Technology, Surindra Rajabhat University, Surin 32000, Thailand

DOI:

https://doi.org/10.65205/jcct.2026.e3896

Keywords:

Retrieval-Augmented Generation, Agricultural Chatbot, Silkworm Feeding Management, Technology Acceptance Model, Surin Province

Abstract

In Surin Province, silkworm farming runs on memory. Feeding recommendations vary by larval stage, yet most rely on memory and informal knowledge passed between generations. This study developed and evaluated MaiKham AI, a domain-specific chatbot built on a Retrieval-Augmented Generation (RAG) architecture, designed to deliver stage-specific feeding guidance through text, voice, and image. The study had two objectives: assessing technical performance of the chatbot, and measuring its effect on sericulture productivity and technology acceptance. A quasi-experimental design was used. Fifty silkworm farmers in Surin Province were purposively selected 25 used the chatbot, 25 continued conventional practice. The RAG system drew on a 52-week silkworm rearing manual loaded into Google AI Studio’s Knowledge Base, with Gemini 1.5 Pro generating responses grounded in that domain knowledge. Expert panels rated the system 4.58 out of 5.00 (S.D. = 0.24). Chatbot users harvested heavier cocoons and higher-grade silk than the control group (p < 0.001), with yield gains of 70–90% across rearing cycles. On the Technology Acceptance Model, farmers scored Perceived Usefulness at 4.38 (S.D. = 0.78) and Perceived Ease of Use at 4.46 (S.D. = 0.87); elderly farmers who had initially resisted the phone adapted more quickly once they could speak their questions aloud. After a single structured training session, 94% used the system without assistance. RAG based chatbots appear capable of filling the knowledge gap in smallholder sericulture, even where farmers are older and digitally inexperienced.

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