Computer intelligence based model for mental health detection among Indian farming communities.

Mental health challenges among Indian farmers are a critical yet under reviewed public health problem, especially in rural areas where access to men's health professionals is limited. Stress from crop failure, fluctuating prices, debt, and poor social support often lead to deterioration in well-being. Traditional survey methods have been used to measure stress but are limited by manual interpretation, subjectivity, and lack of scalability for rural deployment. This study proposes a diagnostic model based on a convolutional neural network (CNN) that analyses the spoken responses of farmers to a structured questionnaire that focuses on stress levels, coping mechanisms, and social support. The audio responses of 350 farmers were collected in local languages, converted into spectrograms, and processed through a CNN architecture, selected for its ability to learn spatial hierarchies without manual feature engineering. The research objectives were (i) to develop a scalable voice-based CNN model for mental health assessment and (ii) to validate its usability in rural contexts. The hypotheses tested were that (H1) CNN would classify mental health status with high precision and (H2) the system would demonstrate strong usability. The results confirmed high predictive accuracy (99.67%) and strong performance in six usability factors: learnability, efficiency, configurability, satisfaction, understandability and effectiveness, indicating the feasibility of the model for integration into rural healthcare outreach.