Development and optimization of a mechanized jackfruit processing unit for enhanced efficiency and commercial viability

Abstract The jackfruit is the largest edible fruit but remains underutilized due to challenges such as sticky latex, labor‐intensive peeling/coring, and lack of mechanization. This study developed and evaluated a jackfruit peeling, coring, and cutting machine to enhance processing efficiency. Performance was modeled using response surface methodology (RSM) and artificial neural network (ANN). Three jackfruit sizes (small, medium, and large) and three machine speeds (90, 120, and 150 RPM) were evaluated for peeling time (26.1–50.3 s), peeling efficiency (71.6%–85.3%), coring time (15.5–29.9 s), coring efficiency (74.7%–96.0%), and bulb wastage (6.2%–17.6%). RSM showed high model adequacy ( R 2 ≥ 0.97) and ANN confirmed prediction reliability ( R 2 = 0.81–0.99; mean square error = 4.4–44.9). Increasing fruit size significantly increased peeling and coring times but decreased efficiencies. Machine speeds caused minor variations. Optimized conditions of 120 RPM fruit holder speed and 150 RPM corer speed gave maximum desirability (0.869). The machine had a payback period of 2 years and benefit–cost ratio of 2.32 versus 2.66 for manual peeling/coring. The mechanized jackfruit processing will promote enhanced utilization of this nutritious fruit. Practical applications The mechanized jackfruit peeling‐coring‐cutting machine developed in this study has significant practical utility. By enabling efficient and rapid processing of jackfruits, the machine can help tap the underutilized potential of this highly nutritious and functionally beneficial fruit. The optimized machine parameters allow jackfruit processing industries to achieve higher throughput with reduced wastage, thereby boosting productivity and profits. Additionally, the mechanization facilitates value‐addition by enabling jackfruit utilization in various processed products like chips, flour, jam, etc. Further, the machine helps create livelihood opportunities in jackfruit value chains, as it reduces drudgery and enhances process efficiency as compared to manual methods. The simple fabrication and operation also enable adoption by farmer‐producer organizations, self‐help groups, and community‐based jackfruit processing enterprises. Overall, the mechanized solution provides an impetus for sustainable utilization of jackfruit, while also addressing issues like food loss, nutrition security, income support, and women empowerment. The practical insights on machine performance modeling using response surface methodology and artificial neural network approaches further facilitate quality improvements in equipment design.