摘要
传统的柑橘灌溉方式主要依赖人工经验,一方面有可能导致灌溉时机不准确,另一方面有可能造成灌溉量过高或者过低,对果实的生长都会产生负面影响。柑橘果园水分蒸散量是表征耗水量的重要指标。为了实现对大面积柑橘果园蒸散量(Evapotranspiration,ET)的准确估算,制定更加科学精细化的灌溉策略,基于气象数据集,应用长短期记忆(Long short-term memory,LSTM)、极限学习机(Extreme learning machine,ELM)和广义回归神经网络(General regression neural network,GRNN)方法对蒸散量建立预测模型并验证其准确性。结果表明,LSTM模型的平均绝对误差(Mean absolute error,MAE)和均方根误差(Root mean square error,RMSE)是3种模型中最优的,ELM和GRNN模型的性能接近。为了估算3种模型结果的可信度,在训练时加入了蒙特卡洛不确定性分析方法。结果表明,LSTM模型在不同输入特征数量下具有较高的精度,而ELM模型存在预测值偏高的现象,GRNN模型则偏低。
Citrus is an important fruit and it’s strongly relevant between quality and irrigation.Traditional irrigation strategies relying on human experience caused two problems,i.e.inaccurate irrigation timing and quantity.Both of the two problems have negative influence on citrus.The evapotranspiration of citrus orchard is an important index of water consumption.In order to evaluate citrus orchard evapotranspiration(ET)to make more scientific and precise irrigation strategies,the long short-term memory(LSTM),extreme learning machine(ELM)and general regression neural network(GRNN)methods were applied to model ET and test its performance based on climatic data.The result showed that LSTM performed the best in mean absolute error(MAE)and root mean square error(RMSE)than the other two models.And ELM model performed closely to GRNN.In order to evaluate the certainty of three models,the Monte Carlo analysis method was added to the process of training.The result indicated that LSTM had good accuracy in different input features while ELM tended to overestimate ET and GRNN tended to underestimate ET.It’s practical to applicate the proposed method to make precise irrigation strategies.
作者
谢家兴
高鹏
孙道宗
陈文彬
陈绍楠
王卫星
XIE Jiaxing;GAO Peng;SUN Daozong;CHEN Wenbin;CHEN Shaonan;WANG Weixing(College of Electronic Engineering,South China Agricultural University,Guangzhou 510642,China;Guangdong Engineering Research Center for Monitoring Agricultural Information,Guangzhou 510642,China;Guangdong Modern Agricultural Science and Technology Innovation Center for Intelligent Orchard,Guangzhou 510642,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2020年第S02期351-356,共6页
Transactions of the Chinese Society for Agricultural Machinery
基金
广东省科技专项资金(“大专项+任务清单”)项目(2020020103)
广东省重点领域研发计划项目(2019B020214003)
广东省教育厅特色创新类项目(2019KTSCX013)
国家荔枝龙眼产业技术体系建设专项资金项目(CARS3214)
广东省现代农业产业技术体系创新团队建设专项资金项目(2019KJ108)
广东省大学生科技创新培育专项资金项目(PDJH2019B0080)。
关键词
蒸散量
柑橘园
预测模型
长短期记忆
广义回归神经网络
极限学习机
evapotranspiration
citrus orchard
long short-term memory
general regression neural network
extreme learning machine