Digitalization in the agriculture sector is a global plan to boost productivity, profitability, and resilience to climate change. Many studies have introduced digital technology such as drones and Internet of Things (IoT) under the umbrella of precision farming for crop maintenance and management, however, the acceptance of farmers to use crowdsensing technology for bottom-up reporting, specifically in crop pests and disease contexts demands further investigation. Crowdsensing relies on farmers’ participation in run-on location-based technology-enabled participatory devices, which can draw collective knowledge and sensor data from local farms. Therefore, this study assesses the intention to use mobile crowdsensing technology for reporting crop pests and diseases among farmers in Johor, Malaysia based on the technology acceptance model (TAM) constructs. Using the partial least squares structural equation model (PLS-SEM), this study demonstrates that perceived usefulness (PU) did not significantly impacts farmers’ attitudes toward the use (ATU) of technology. Whereas, generational cohorts (age groups) of farmers significantly affect the relationship between PU and Intention to use (ITU) on the use of the crowdsourced technology for crop pests and diseases monitoring (crowdsensing). This study will provide an understanding of how to promote the high acceptance of local farmers as human sensors in pest and disease monitoring via a mobile crowdsensing platform.