块茎或植物根系的快速无损检测是植物精准化栽培中的一大难题。土壤多相介质环境块茎生长状态信息检测的新型电阻成像关键技术研究成果将为农作物如马铃薯、番薯、芋和名贵中药材如人参、天麻等块茎植物新品种培育、科学合理种植、提高产量等提供强有力的可视化监测手段和分析工具。对于块茎植物的精细种植而言，可通过对种植田地里块茎生长状态信息的抽样监测，甚至还可通过无线传感器网络实现远程监控，做到合理有效地浇水、施肥和病虫害防治等，这对低碳经济、绿色环保与食品安全等具有重大意义。课题主要研究内容：土壤多相介质环境与块茎生长状态信息检测电阻成像多平面阵列传感器构成的测量阻抗模型；块茎生长的土壤多相介质环境电导特性理论分析、测试与建模；激励/测量策略；变频大电流激励、高灵敏高精度信号放大和多平面组合扫略成像数据获取新型电阻成像系统；图像重建算法；图像特征信息与块茎真实形态和生长特征模型。

The fast non-destructive inspection of plant tubers or roots is a big problem in the plant accurate cultivation.The new electrical resistance tomography (ERT) system will be produced in the research of imaging monitoring the growth status of tubers in subsoil,and it will provide powerful visualization monitor method and analysis tool for the new variety breeding, scientific and reasonable planting, and yield improvement of tuberous plants, such as potato, sweet potato, taro, and the precious traditional Chinese medicine such as ginseng and gastrodia elata.For the fine planting of the tuberous plants, by using the sampling inspection of the tuber growth status in the fields, and even by using the remote monitoring and control through the wireless sensor networks, the watering, fertilizing and plant diseases prevention can be reasonable and effective implemented. It does great significance to the low-carbon economy, the green environmental protection and food security.The main research contents include: the measure impedance model constituting with the subsoil multiphase medium environment and the multi-plane array sensors of electrical resistance tomography system for the information detection of tuber growth status; the theoretical analysis, test and modeling of the conductance characteristic of subsoil multiphase medium for tuber growth; strategies of exciting and measuring; the new type of electrical resistance tomography system with multi-planes scanning imaging data acquisition, variable frequency and big current excitation, high sensitivity and high precision signal amplification; three dimensional image reconstruction algorithm; modeling of the image feature information and the real shape and growth characteristic of tuber.