Rongrong Li , Hongwen Li , Shuofei Yang , Caiyun Lu , Zhengyang Wu , Zhinan Wang
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引用次数: 0
Abstract
Accurate measurement of straw mulch mass requires effective separation of residues inside and outside the sampling frame. To achieve this, a novel spin-descent straw cutter was designed, integrating a stabilizing ring and a rotating and descending separation column. The stabilizing ring pressed the straw to reduce movement, while the separation column simultaneously rotated and descended to cut and separate the straw at the frame boundary, representing an integrated structure not previously reported. A discrete element method (DEM) model was established to simulate straw-soil-cutting system interactions, and three blade types (sawtooth, corrugated, and notch-shaped) were compared. Response surface methodology and multi-objective optimization were employed to balance cutting rate, power consumption, and soil disturbance area. Bench tests confirmed the DEM predictions with errors within 12 %. Results demonstrated that the sawtooth blade achieved the most favorable trade-off, with optimal parameters of 287 N downward force, 230 rpm rotation speed, and 10 mm insertion depth. The study highlighted the unique suitability of the spin-descent cutter for automated straw mulching detection and provided a validated simulation–optimization framework to inform future cutter designs and broader applications in conservation tillage.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.