Risk has always existed in agriculture. Every day, farmers take risks and make decisions that have an influence on their farming operations. The weather can change, there can be a crop failure, crop production price levels can drop, contracted workforce might not be available when it's most needed, machinery and tools can fail when it's most crucial and national policy can change in an instant. These are just a few of the numerous factors that can affect farmer decisions. Each of these risks has an effect on how profitable their farm is. To ensure that the finished product meets industry and consumer standards, precise dependability indicators must be established early in the development cycle. One such metric is the product's long-term failure rate, which is sometimes expressed as mean time before failure (MTBF). The MBTF for extremely reliable industrial systems is far greater than the period used to demonstrate this metric in a lab setting under real-world field usage conditions. Increasing the test failure rate is generally desirable and practical, but it can be somewhat useful on occasion. ALT involves stress testing a product under conditions that are more demanding than typical field usage conditions in order to accelerate the failure-discovery process. In this research, we have explored a new step stress competitive life model using a type-I progressive hybrid censoring technique. The Rayleigh distribution is assumed to be followed by the items' failure lifespan. The acceleration factor and distributional parameters are determined via the maximum likelihood estimation technique. The interval estimates are also obtained for the same circumstance. The validity of the model has been examined using simulated data.