当前位置: X-MOL 学术Precision Agric. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems
Precision Agriculture ( IF 5.4 ) Pub Date : 2020-05-06 , DOI: 10.1007/s11119-020-09719-1
Gerald Blasch , Zhenhai Li , James A. Taylor

Easy-to-use tools using modern data analysis techniques are needed to handle spatio-temporal agri-data. This research proposes a novel pattern recognition-based method, Multi-temporal Yield Pattern Analysis (MYPA), to reveal long-term (> 10 years) spatio-temporal variations in multi-temporal yield data. The specific objectives are: i) synthesis of information within multiple yield maps into a single understandable and interpretable layer that is indicative of the variability and stability in yield over a 10 + years period, and ii) evaluation of the hypothesis that the MYPA enhances multi-temporal yield interpretation compared to commonly-used statistical approaches. The MYPA method automatically identifies potential erroneous yield maps; detects yield patterns using principal component analysis; evaluates temporal yield pattern stability using a per-pixel analysis; and generates productivity-stability units based on k-means clustering and zonal statistics. The MYPA method was applied to two commercial cereal fields in Australian dryland systems and two commercial fields in a UK cool-climate system. To evaluate the MYPA, its output was compared to results from a classic, statistical yield analysis on the same data sets. The MYPA explained more of the variance in the yield data and generated larger and more coherent yield zones that are more amenable to site-specific management. Detected yield patterns were associated with varying production conditions, such as soil properties, precipitation patterns and management decisions. The MYPA was demonstrated as a robust approach that can be encoded into an easy-to-use tool to produce information layers from a time-series of yield data to support management.

中文翻译:

作物生产系统产量区的多时相产量格局分析方法

需要使用现代数据分析技术的易于使用的工具来处理时空农业数据。本研究提出了一种新的基于模式识别的方法,即多时间产量模式分析 (MYPA),以揭示多时间产量数据的长期(> 10 年)时空变化。具体目标是:i) 将多个产量图中的信息综合成一个单一的可理解和可解释的层,表明 10 多年期间产量的可变性和稳定性,以及 ii) 评估 MYPA - 与常用统计方法相比的时间产量解释。MYPA 方法自动识别潜在的错误产量图;使用主成分分析检测收益率模式;使用每像素分析评估时间屈服模式的稳定性;并基于 k 均值聚类和区域统计生成生产力稳定性单位。MYPA 方法应用于澳大利亚旱地系统中的两个商业谷物田和英国凉爽气候系统中的两个商业田地。为了评估 MYPA,将其输出与对相同数据集进行的经典统计产量分析的结果进行比较。MYPA 解释了产量数据中的更多差异,并生成了更适合特定地点管理的更大、更连贯的产量区。检测到的产量模式与不同的生产条件有关,例如土壤特性、降水模式和管理决策。
更新日期:2020-05-06
down
wechat
bug