Deep tillage is a conservation tillage method that aims to break the plow pan layer. It provides significant benefits, including enhanced root development, improved soil quality, and substantial increases in crop yields. The depth of tillage is a crucial factor in assessing the effectiveness of deep tillage operations. Accurate regulation of tillage depth in deep tillage equipment is vital for ensuring the high-quality and efficient execution of these practices. The distribution of mechanical resistance within the soil can effectively indicate the location of the plow pan layer and serves as the main reference for setting the tillage depth for machinery. This paper examined the current state of research on tillage depth control technology for deep tillage operations. It focused on three main technical areas: soil mechanical resistance detection, tillage depth measurement, and tillage depth regulation. The report discussed the working principles of various technologies and compared the existing methods. Additionally, the paper analyzed the challenges faced in the development of tillage depth control technology in China and offers recommendations for future advancements. It highlighted that leveraging information and digital technologies to determine the distribution of the soil plow pan layer, along with the integration of efficient and intelligent control technologies for precise tillage depth regulation, represented a key direction for the future development of deep tillage operations.

Soil compaction is a pivotal factor influencing crop growth and yield, and its accurate assessment is imperative for precision agricultural management. Soil mechanical resistance is the key indicator of soil compaction, with accurate measurement enabling precise assessment. Dynamic soil mechanical resistance measurement outperforms conventional manual fixed-point sampling in data acquisition efficiency. In this paper, a methodology is proposed for the dynamic acquisition of soil mechanical resistance using a flexible thin-film pressure sensor. This study dynamically captures soil mechanical resistance at three depths (5 cm, 10 cm, and 15 cm) under dynamic machinery operating conditions. A device was designed for the detection of soil mechanical resistance, and a prediction model for soil mechanical resistance was developed based on the Kalman filter algorithm. Tests were conducted under steady-state and variable-load conditions, and the predicted values accurately tracked the reference pressure. Soil tank trials showed that at an operating speed of 0.69–0.72 km/h, the average prediction errors for the three soil layers were 2.03%, 1.48%, and 6.27%, with the coefficient of determination (R²) between predicted and measured values reaching 0.96. The system effectively predicts multi-depth soil resistance, providing novel theoretical and technical approaches for dynamic acquisition.

Arable land quality is of the essence for the sustenance of grain production and food security. The continuous monitoring of the physical and chemical properties of arable land is instrumental in facilitating a comprehensive understanding of the evolution patterns of soil quality. This, in turn, provides fundamental evidence that is crucial for the optimization of cultivation practices, the establishment of appropriate plough layers, and the enhancement of soil quality. The near-surface sensing methodologies facilitate the acquisition of soil data at reduced scales, thus signifying a pivotal research trajectory for the procurement of soil-related information. The present study undertakes an examination of the current state of research on acquiring key parameters of farmland soil and provides an overview of the fundamental ground-level techniques employed for the assessment of farmland soil parameters. These techniques encompass single-parameter fixed-point detection, encompassing Soil Moisture Content (SMC), Soil Electrical Conductivity (EC), and nutrient analysis, multi-parameter fusion detection, and dynamic parameter monitoring. The study systematically reviews field sensing methods for major soil physicochemical parameters (such as SMC, Soil Penetration Resistance (SPR), EC, and nutrients) while analyzing the current application of Artificial Intelligence (AI) in soil parameter detection. The present paper proposes a developmental trajectory that shifts from “single-parameter static” to “multi-parameter dynamic” monitoring. This trajectory is proposed as a building upon the analysis of existing research. This evolution emphasizes intelligent algorithm-driven data enhancement to improve detection accuracy, forming a closed-loop progression of “dynamic detection—precise modeling—decision support”. This framework provides a reference for the advancement of soil sensing monitoring technologies and the scaling of precision agriculture applications.