田间土壤自动采样与参数实时检测装置设计与试验  

Design and experiment of an automatic soil sampling and real-time parameter detection device for field soils

作　　者：陈子文 姚宇熙 张海腾 杨明金[1,2] 蒲应俊 李守太 CHEN Ziwen;YAO Yuxi;ZHANG Haiteng;YANG Mingjin;PU Yingjun;LI Shoutai(College of Engineering and Technology,Southwest University,Chongqing 400715,China;Chongqing Key Laboratory of Intelligent Agricultural Machinery Equipment for Hily and Mountainous Regions,Chongqing 400715,China)

机构地区：[1]西南大学工程技术学院,重庆400715 [2]丘陵山区智能农机装备重庆市重点实验室,重庆400715

出　　处：《农业工程学报》2025年第6期20-30,共11页Transactions of the Chinese Society of Agricultural Engineering

基　　金：重庆市农业农村委员会农业新型研发机构补助项目;重庆市技术创新与应用发展专项重点项目(cstc2021jscx-gksbX0013)。

摘　　要：针对田间信息化管理中传统土壤样本采集与土壤参数检测劳动强度大、操作复杂、效率低等问题,该研究设计了一种土壤自动采样与土壤参数实时检测装置,并提出基于BP神经网络(back propagation neural network,BPNN)的土壤坚实度和质量含水率预测方法。首先,基于土样自动采集与参数测量需求,设计双级分步式土样采检机构、卸土机构及分度式土样收集机构,对机构进行分析与校核确定400 mm运动行程和800N最大入土推力,并搭建基于Jetson TX2嵌入式计算机与STM32F3系单片机的双层构架控制系统,结合全球导航定位系统(global navigation satellite system,GNSS),实现土壤自动采样、自主导航、信息记录与传输、取土自保护以及土壤坚实度与质量含水率动态预测的功能。其次,构建了3层BP神经网络预测模型,将易检测的土壤体积含水率、土壤取样电流、取样速度、取样深度4个参数与土壤坚实度及质量含水率建立回归关系,通过275个试验样本对模型进行训练与测试,得到最佳隐藏层节点数为10,土壤坚实度与质量含水率预测结果平均百分比误差分别为7.74%和1.53%。最后,为验证机器综合性能,以机器采样时间、温湿度传感器探针入土深度、土样质量绝对误差、土壤坚实度与质量含水率预测值相对误差作为评价指标,对柑橘园巡检路径中10个采样点进行实地试验,结果表明,该机器单次土壤采样平均耗时为60.5 s,传感器探针平均入土深度为64.7 mm,土样质量平均绝对误差为1.53 g,10个采样点的土壤坚实度与质量含水率预测的相对误差平均值分别为6.37%和5.00%,满足土壤采样和参数检测需求,同时结合地理位置信息给出土壤坚实度与质量含水率田间分布图。该研究结果可为土壤智能采集、参数实时检测及田间土壤信息分布可视化管理提供参考。As the cornerstone of agricultural production,soil provides various nutrients and moisture essential for crop growth,directly influencing crop yield and quality.Field soil information management constitutes a pivotal component in the development of modern agriculture.Currently,manual soil sampling coupled with offline parameter testing remains the primary approach in field management,which is plagued by issues such as high labor costs,intensive workload,and inefficiencies in collection and measurement.To mitigate these challenges and enhance the cost-effectiveness and efficiency of field management,a soil automatic sampling and real-timesoil parameter detection device was developed in this study.Furthermore,a prediction method based on the BPNN(Back Propagation Neural Network)is proposed for estimating soil firmness and mass moisture content.Firstly,based on the requirements for automatic soil sample collection and parameter measurement,a direct-pressure,two-stage,and step-by-step soil sampling and inspection mechanism,an unloading mechanism,and an indexing soil sample collection mechanism were designed.These mechanisms were analyzed and verified,resulting in the determination of a 400 mm movement stroke and a maximum soil penetration thrust of 800 N.A dual-layer control system architecture was also established,utilizing the Jetson TX2 embedded computer and STM32F3 series microcontroller,integrated with GNSS positioning.This system enabled functions such as automatic soil sampling,autonomous navigation,information recording and transmission,soil sampling self-protection,and dynamic prediction of soil firmness and mass moisture content.Secondly,a three-layer BPNN neural network prediction model was constructed to establish a regression relationship between easily measurable parameters including volumetric water content,soil sampling current,sampling speed,and sampling depth,with soil firmness and mass water content.The model was trained and tested using 275 experimental samples,resulting in an optimal number of hi

关 键 词：土壤坚实度 实时检测 自动采样 土壤质量含水率 BP神经网络 

分 类 号：S126[农业科学—农业基础科学]