Authors - Abhijeet G. Dhepe, Ashish R. Lahase, Shalini V. Sathe, Sagar Chitte, Pooja B. Abhang, Bharati W. Gawali, Sunil S. Nimbhore Abstract - This study introduces a novel crop diseases database design for Automatic Speech Recognition (ASR) systems. It aimed to predict crop diseases, addressing the critical role of speech technology in agriculture. The research bridges the gap between farmers and advanced diagnostic tools by creating a specialized voice corpus focused on agricultural terminology and disease names. The dataset, which includes various phrases related to crop health management, was collected in naturally noisy environments from farmers in the Marathwada region. Recordings captured the authentic speech patterns of both male and female participants, encompassing various dialects and accents. This approach ensures real-world applicability, enhancing the reliability and relevance of ASR systems. By including native and non-native speakers, the study promotes linguistic inclusivity. It aims to empower farmers with accessible, speech-based disease prediction tools, ultimately fostering greater efficiency and resilience in crop management.
