摘要
作物全生命周期的标准生长模型建立是指导获得最佳作物“处方”(作业精准决策、执行)的必要手段.智能作物生育期识别技术是构建作物标准生长模型的重要技术之一.在呼伦贝尔大河湾地区大面积规模化的作物种植形势下,基于传统人工经验或单一传感器进行作物生育期表型数据采集、识别的方法会导致采集范围局限、识别效率低等问题.针对上述问题,对整体系统提出了一系列的改进.首先,在数据采集阶段,提出了一套完整的“空-地-人”一体化作物表型数据采集体系.另外,在数据分析阶段,根据作物不同表型数据,提出了一种改进的智能作物生育期识别体系.提出的生育期识别系统能够实时精准地提供当前作物生育期阶段,为建立作物全生命周期的标准生长模型奠定了优良的基础.
The establishment of a standard growth model for the whole crop life cycle is necessary to guide the acquisition of the best crop“prescription”(accurate decision-making and execution of operations).The intelligent identification technology of crop fertility stages is one of the important technologies to build the standard crop growth model.Under the situation of large-scale crop cultivation in Hulun Buir Dahewan,the traditional method of collecting and identifying crop fertility phenotype data based on manual experience or a single sensor will lead to problems such as limited collection range and low identification efficiency.In order to address the above problems,a series of optimizations are proposed for the overall system.First,in the data collection stage,a complete“air-ground-human”integrated crop phenotype data collection system is proposed in this study.In addition,in the data analysis stage,an improved intelligent identification system of crop fertility stages is proposed based on different crop phenotype data.The proposed identification system can provide real-time and accurate information about the current crop fertility stages,which serves as an excellent basis for establishing a standard growth model for the whole crop life cycle.
作者
陈海华
胡兆民
张景尧
马跃辉
汪家祺
刘子辰
CHEN Hai-Hua;HU Zhao-Min;ZHANG Jing-Yao;MA Yue-Hui;WANG Jia-Qi;LIU Zi-Chen(Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China;Hulun Buir Agricultural Reclamation Group Co.Ltd.,Hulun Buir 021008,China;Intelligent Computing Research Institute of Shandong Industrial Technology Research Institute,Jinan 250100,China)
出处
《计算机系统应用》
2023年第8期67-74,共8页
Computer Systems & Applications
基金
中国科学院战略性先导科技专项(A类)(XDA28120301)
山东省自然科学基金(ZR2021MF094)
黄三角国家农高区科技专项(2022SZX11)
中央引导地方科技发展资金(ZR2020KF030)。
关键词
作物生育期识别
数据采集
作物标准生长模型
人工智能
图像识别
identification of crop fertility stages
data collection
standard growth model of crops
artificial intelligence
image recognition