This research optimizes IoT-based soil moisture sensor distribution using an end-to-end complete precision irrigation. Knowing the WSN architecture and deployment of LoraWanbased network for the monitoring soil parameters essential to ensure the exact number of sensors required per field and at the right place per management zone should have a profound impact on our productivity. Moreover, the ACCM significantly guided to determine the number of sensors per MZ. ACCM closely helps to monitor the variation in MC as sensor numbers are reduced across the field. So, we were able to see that there is no significant change when we used a 40% and a 100% sensor distribution. While a 10% sensor distribution illustrated where each zone is most affected by the position of the sensors, a 40% sensor distribution was found to delineate variation in MC best as it revealed almost a similar variation in MC as delineated by the line graph of normalized mean of MC. In addition, 40% of sensor distribution had the second highest score for k=3. 40% sensor distribution is the best because it revealed most of the variation in MC at the boundaries per MZ with a fewer number of sensors (12 sensors) as clearly visualized on the maps. This study has demonstrated that IoT-based soil MC sensors can monitor MC and ACCM can minimize the associated cost of sensor purchase by optimizing the number of sensors.