CAJ | 학술논문

该文阐述了应用光谱和成像技术进行作物养分生理信息快速检测的主要研究进展和发展趋势。介绍了光谱和成像技术的基本原理、常用数据处理方法、建模方法和模型评价指标，重点总结了光谱和成像技术在5种常见农作物（水稻、小麦、油菜、玉米、大豆）的养分生理信息检测中的应用成果和研究进展（主要包括叶绿素类和氮素检测，病虫害、水分、杂草、重金属、农药胁迫诊断及产量预测等方面），分析了光谱和成像技术在作物生长信息检测的发展趋势。结果表明，光谱和成像技术能够快速无损获取作物养分生理信息，并能有效地对作物长势和逆境胁迫响应进行动态监测，对实现农业的精准化、数字化、信息化及智能化管理和作业具有重要意义。
해문천술료응용광보화성상기술진행작물양분생리신식쾌속검측적주요연구진전화발전추세。개소료광보화성상기술적기본원리、상용수거처리방법、건모방법화모형평개지표，중점총결료광보화성상기술재5충상견농작물（수도、소맥、유채、옥미、대두）적양분생리신식검측중적응용성과화연구진전（주요포괄협록소류화담소검측，병충해、수분、잡초、중금속、농약협박진단급산량예측등방면），분석료광보화성상기술재작물생장신식검측적발전추세。결과표명，광보화성상기술능구쾌속무손획취작물양분생리신식，병능유효지대작물장세화역경협박향응진행동태감측，대실현농업적정준화、수자화、신식화급지능화관리화작업구유중요의의。
The research achievements and growing trends of spectral and imaging technology in fast detection of crop nutrient and physiological information were reviewed. Firstly, the principle of spectral and imaging technology, the data processing methods, modeling methods and the indexes of model evaluation were briefly introduced in this paper. Secondly, this paper focused on the research achievements and applications of spectral and imaging technology in fast detection of crop nutrient and physiological information of five kinds of crops (i.e. rice, wheat, oilseed rape, maize, soybean), including chlorophyll content and nitrogen content detection, crop diseases and pests monitoring, stress diagnosis (water, heavy metal, weed, pesticide stress) and yield prediction. In nutrient content and chlorophyll content detection, the data was acquired by ground-based sensing, aircraft-based sensing and satellite-based sensing, and the raw spectra, as well as vegetable indices, were used to build quantitative models. In crop diseases and pests monitoring, spectral and imaging technology were used to discriminate the crop diseases and pests, and diagnose the crop stress level. As for stress diagnosis, several recently-reported researches were briefly reviewed. In yield prediction, this paper was mainly focused on predicting the canopy parameters which were found to be significantly related to crop yield. Although the ability of spectral and imaging technology was proved, there were several problems needed to be solved. 1) The detection of crop nutrient and physiological information with spectral and imaging technology is affected by crop type, crop growing stage, operational conditions, environmental parameters and field management. Therefore, the stability and reliability of the model needs to be improved, which can be overcome by choosing suitable pretreatment methods and chemometrics methods or proposing new vegetable indices which are insensitive to these influencing factors. 2) Multi-scale rapid detection of crop nutrient and physiological information is required in the future, including the multi-scale dataset fusion, the research of different-scale sensing effect. 3) The quantitative models for the level of crop stress diagnosis is hard to carry out, due to the lack of stress assessment indexes. So it is critical to set up the reference principle for the crop stress level. Furthermore, this paper also analyzed the growing trends of the spectral and imaging technology for the fast detection of crop nutrient and physiological information. Firstly, there is a need to develop more stable and reliable methods for variable selection, data mining and model calibration, as well as the calibration technology which is based on the actual physical model between the radiation and the crop tissue. Secondly, the development of portable machinery and the online detection system for crop information acquirement is required in the further study. Likewise, further research is necessary with respect to developing the information detection system of whole crop growing stage with consideration of different crop features. In conclusion, it is proved that spectral and imaging technology can be used to detect the crop physiological information, carry out the online monitoring of the crop growing statue and the response to the adversity and stress, which is important for the realization of precision, digitization, informatization and the intelligentization management of the agriculture.