In this proposal, the impact of dynamic farm environment due to varying vegetation density on the Received Signal Strength (RSS), coverage and energy consumption of an IoT assisted Wireless Sensor Network (IoWSN) is analyzed through measurement campaign. Experimental observations on free-space and tree Path Loss Model (PLM) based sensor node deployment strategies in a cropping period have shown network disconnectivity due to incorrect assessment of excess attenuation caused by dynamically varying vegetation height and density during the monitoring period. To address the challenge, an empirically formulated PLM is proposed to estimate the excess attenuation at different crop development stages of medium grass vegetation. Further, using the formulated PLM, a Non-dominated Sorting Genetic Algorithm (NSGA-III) multi-objective optimization is performed to generate initial node deployment with a heterogeneous transmission range. To address the issue of over-coverage, transmitter output power scheduling is performed with predefined upper limit derived from the NSGA-III optimization. The output power is dynamically scheduled during the monitoring period based on changes in the captured RSS to minimize over-coverage. Improvements in coverage, connectivity, and energy efficiency compared to existing approaches are validated through Proof of Concept.