This study presents the implementation of an in-field IoT system for precision irrigation management. The system includes an IoT-based sensor station, a user-friendly website, and a smartphone app for irrigation management. Soil moisture sensors were calibrated using a laboratory experiment, and the calibrated sensors met the statistical criteria for both sand and loamy sand. The system was tested in corn, blueberry, and tomato fields in Michigan, USA. In the corn and blueberry fields, the evaluation of irrigation practices using IoT-based sensor technology was considered. In the tomato field, automation irrigation was demonstrated. Overirrigation was observed in some fields with sandy soil and a drip irrigation system. In the blueberry demonstration field, the total yield per plant and 50-berry weights were found to be higher with the recommended irrigation management than the farmer's existing field. In the tomato demonstration field, there were no statistical differences in the number of marketable tomatoes and their weights between the farmer's existing method and the recommended irrigation strategy. However, 30% less water was applied to the recommended irrigation strategy plot. The results showed that the IoT-based sensor irrigation strategy can save up to 30% on irrigation while maintaining the same yields and quality of the product. The study highlights the potential of IoT-based sensor technology to improve irrigation water use efficiency and reduce the impact of climate change on agriculture. The system was found to be effective in reducing overirrigation, saving water, and improving crop yields. The study also emphasizes the need for further research and demonstration to increase the adoption of IoT-based sensor technology in agriculture. The system is designed to be affordable and easy to use, making it accessible to farmers for improved irrigation management. The study concludes that IoT-based sensor technology has the potential to improve irrigation water use efficiency and reduce the environmental impact of agriculture.This study presents the implementation of an in-field IoT system for precision irrigation management. The system includes an IoT-based sensor station, a user-friendly website, and a smartphone app for irrigation management. Soil moisture sensors were calibrated using a laboratory experiment, and the calibrated sensors met the statistical criteria for both sand and loamy sand. The system was tested in corn, blueberry, and tomato fields in Michigan, USA. In the corn and blueberry fields, the evaluation of irrigation practices using IoT-based sensor technology was considered. In the tomato field, automation irrigation was demonstrated. Overirrigation was observed in some fields with sandy soil and a drip irrigation system. In the blueberry demonstration field, the total yield per plant and 50-berry weights were found to be higher with the recommended irrigation management than the farmer's existing field. In the tomato demonstration field, there were no statistical differences in the number of marketable tomatoes and their weights between the farmer's existing method and the recommended irrigation strategy. However, 30% less water was applied to the recommended irrigation strategy plot. The results showed that the IoT-based sensor irrigation strategy can save up to 30% on irrigation while maintaining the same yields and quality of the product. The study highlights the potential of IoT-based sensor technology to improve irrigation water use efficiency and reduce the impact of climate change on agriculture. The system was found to be effective in reducing overirrigation, saving water, and improving crop yields. The study also emphasizes the need for further research and demonstration to increase the adoption of IoT-based sensor technology in agriculture. The system is designed to be affordable and easy to use, making it accessible to farmers for improved irrigation management. The study concludes that IoT-based sensor technology has the potential to improve irrigation water use efficiency and reduce the environmental impact of agriculture.