A survey of high resolution image processing techniques for cereal crop growth monitoring
作者机构：School of Computer ScienceUniversity College DublinDublinIreland School of Agriculture and Food ScienceUniversity College DublinDublinIreland School of Biosystems and Food EngineeringUniversity College DublinDublinIreland Origin Enterprises plcDublinIreland
出 版 物：《Information Processing in Agriculture》 (农业信息处理（英文）)
年 卷 期：2022年第9卷第2期
页 面：300-315页
核心收录：
学科分类：0907[农学-林学] 082804[工学-农业电气化与自动化] 0908[农学-水产] 08[工学] 0710[理学-生物学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 0828[工学-农业工程] 0905[农学-畜牧学] 0707[理学-海洋科学] 080203[工学-机械设计及理论] 0906[农学-兽医学] 0829[工学-林业工程] 0802[工学-机械工程] 0901[农学-作物学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Crop canopy cover Above ground biomass Leaf area index Chlorophyll content Growth stage Cereal image processing
摘 要：This paper presents a survey of image processing techniques proposed in the literature forextracting key cereal crop growth metrics from high spatial resolution, typically proximalimages. The descriptive crop growth metrics considered are: crop canopy cover, aboveground biomass, leaf area index (including green area index), chlorophyll content, andgrowth stage. The paper includes an overview of relevant fundamental image processingtechniques including camera types, colour spaces, colour indexes, and image segmentation. The descriptive crop growth metrics are defined. Reference methods for groundtruth measurement are described. Image processing methods for metric estimation aredescribed in detail. The performance of the methods is reviewed and compared. The surveyreveals limitations in image processing techniques for cereal crop monitoring such as lackof robustness to lighting conditions, camera position, and self-obstruction. Directions forfuture research to improve performance are identified.
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