Methods and experiments for analysing hard-bottom layer changes and monitoring wheel sink depth in paddy fields

This study aims to enhance smart agricultural machinery for more efficient and high-quality rice production by addressing the challenge of sensing wheel rut depth in paddy fields. Monitoring the hard-bottom layer beneath the tillage zone is difficult due to repeated machinery rolling. An unmanned rice direct seeder, which shares the same mobile chassis as the rice transplanter, was equipped with a dual-antenna Global Navigation Satellite System and dual Attitude and Heading Reference System sensors, and was used as a sensing platform. Methods were developed to calibrate sensors, remove outliers, and estimate implement height using numerical fitting and interquartile range detection. Soil compaction and wheel rut depth measurements were used to compare hard-bottom variations in dry and wet fields. A spatial motion model was created to measure wheel rut depth based on relationships between antenna-to-wheel and antenna-to-implement distances. Digital modeling and interpolation techniques were used to generate accurate models of the mud surface and hard-bottom layer for depth estimation across the field. Field trials showed repetitive rolling in dry fields created localized hard soil layers and increased compaction, while minimal changes occurred in wet fields. The wheel rut depth in dry fields was under 1.0 cm after three passes, but in wet fields, it ranged from 1.9 to 2.9 cm after 2 to 7 passes, with decreasing increments. Wheel sink sensing experiments achieved a standard deviation of 0.678 cm. The surface slope of the paddy field measured by the unmanned direct seeder was 0.03°, which is smaller than the slope of the hard bottom layer at 0.07°. Sink depths were greater in low-lying areas, averaging 23.47 cm, with a variance of 1.84 cm and a maximum depth of 38.10 cm. On a 5-hectare rice farm, the rice transplanter measured mean wheel rut depths of 22.15 cm (variance: 2.17 cm) in Area I and 22.60 cm (variance: 2.53 cm) in Area II. The proposed methods enable continuous, precise monitoring of hard-bottom layer changes and wheel rut depths, characterize the effects of repeated rolling, and produce critical terrain maps. These results support adaptive speed control and entrapment prevention strategies for unmanned smart farm machinery.