The ability to characterize rapidly and repeatedly exchangeable potassium(Kex)content in the soil is essential for optimizing remediation of radiocaesium contamination in agriculture.In this paper,we show how this can...The ability to characterize rapidly and repeatedly exchangeable potassium(Kex)content in the soil is essential for optimizing remediation of radiocaesium contamination in agriculture.In this paper,we show how this can be now achieved using a Convolutional Neural Network(CNN)model trained on a large Mid-Infrared(MIR)soil spectral library(40,000 samples with Kex determined with 1 M NH4OAc,pH 7),compiled by the National Soil Survey Center of the United States Department of Agriculture.Using Partial Least Squares Regression as a base-line,we found that our implemented CNN leads to a significantly higher prediction performance of Kex when a large amount of data is available(10000),increasing the coefficient of determination from 0.64 to 0.79,and reducing the Mean Absolute Percentage Error from 135%to 31%.Furthermore,in order to provide end-users with required interpretive keys,we implemented the GradientShap algorithm to identify the spectral regions considered important by the model for predicting Kex.Used in the context of the implemented CNN on various Soil Taxonomy Orders,it allowed(i)to relate the important spectral features to domain knowledge and(ii)to demonstrate that including all Soil Taxonomy Orders in CNN-based modeling is beneficial as spectral features learned can be reused across different,sometimes underrepresented orders.展开更多
Agricultural environmental pollution emergencies have become a hot research topic because of the high incidence and influence depth.This paper introduces classification and features of agricultural environmental pollu...Agricultural environmental pollution emergencies have become a hot research topic because of the high incidence and influence depth.This paper introduces classification and features of agricultural environmental pollution emergencies:by pollutant type,it falls into organic pollution emergencies and inorganic pollution emergencies;by the approach of entering agricultural environment,it falls into water resource agricultural environmental pollution emergencies and non-water resource agricultural environmental pollution emergencies.Hazards of agricultural environmental pollution emergencies are analyzed from 4 perspectives:personal security,indirect loss,ecological environment and social stability.In view of the hazards,countermeasures are given to deal with the pollution emergencies as(i)establishing a risk evaluation mechanism for agricultural environment;(ii)enhancing the capacity of handling agricultural environmental pollution emergencies;(iii)introducing new management concepts for environmental emergencies,and cultivating keen emergency management consciousness.展开更多
针对大规模农田的粮食抢收工作,由于抢收约束时间的限制,导致可能无法在规定时间内完成所有粮食的收割任务,此时则会出现粮食损失值。构建以粮食损失值最小化为目标的农机跨区紧急调配模型,提出基于两阶段的农机紧急调配算法(Agricultur...针对大规模农田的粮食抢收工作,由于抢收约束时间的限制,导致可能无法在规定时间内完成所有粮食的收割任务,此时则会出现粮食损失值。构建以粮食损失值最小化为目标的农机跨区紧急调配模型,提出基于两阶段的农机紧急调配算法(Agricultural Machinery Emergency Algorithm based on Two Stages,TSEA)。首先按照基于距离的分区策略对大规模农田进行分区,接着采用改进的遗传算法分别对各农田分区进行农机紧急作业调配。为验证算法的有效性,主要从紧急任务的粮食损失值、算法运行时间等方面将TSEA算法与GA、SA算法进行比较,试验结果表明,TSEA算法得到的紧急调度方案优于其他两种算法。为验证本文分区策略的有效性,分别采用TSEA算法和不分区的紧急调配算法从粮食损失值、算法运行时间等方面进行比较,验证分区策略的有效性。多组试验结果表明该文提出的策略和算法对于解决大规模农田的农机跨区紧急调配问题更有效,可为农机管理部门提供解决方案。展开更多
基金carried out in the context of the IAEA funded Coordi-nated Research Project(CRPD1.50.19)titled“Remediation of Radioac-tive Contaminated Agricultural Land”,under IAEA Technical Contract n°23685.
文摘The ability to characterize rapidly and repeatedly exchangeable potassium(Kex)content in the soil is essential for optimizing remediation of radiocaesium contamination in agriculture.In this paper,we show how this can be now achieved using a Convolutional Neural Network(CNN)model trained on a large Mid-Infrared(MIR)soil spectral library(40,000 samples with Kex determined with 1 M NH4OAc,pH 7),compiled by the National Soil Survey Center of the United States Department of Agriculture.Using Partial Least Squares Regression as a base-line,we found that our implemented CNN leads to a significantly higher prediction performance of Kex when a large amount of data is available(10000),increasing the coefficient of determination from 0.64 to 0.79,and reducing the Mean Absolute Percentage Error from 135%to 31%.Furthermore,in order to provide end-users with required interpretive keys,we implemented the GradientShap algorithm to identify the spectral regions considered important by the model for predicting Kex.Used in the context of the implemented CNN on various Soil Taxonomy Orders,it allowed(i)to relate the important spectral features to domain knowledge and(ii)to demonstrate that including all Soil Taxonomy Orders in CNN-based modeling is beneficial as spectral features learned can be reused across different,sometimes underrepresented orders.
基金Supported by Humanities and Social Science Fund of Henan Provincial Department of Education(2013-QN-027)Doctoral Fund of Henan Polytechnic University(B2012-008)
文摘Agricultural environmental pollution emergencies have become a hot research topic because of the high incidence and influence depth.This paper introduces classification and features of agricultural environmental pollution emergencies:by pollutant type,it falls into organic pollution emergencies and inorganic pollution emergencies;by the approach of entering agricultural environment,it falls into water resource agricultural environmental pollution emergencies and non-water resource agricultural environmental pollution emergencies.Hazards of agricultural environmental pollution emergencies are analyzed from 4 perspectives:personal security,indirect loss,ecological environment and social stability.In view of the hazards,countermeasures are given to deal with the pollution emergencies as(i)establishing a risk evaluation mechanism for agricultural environment;(ii)enhancing the capacity of handling agricultural environmental pollution emergencies;(iii)introducing new management concepts for environmental emergencies,and cultivating keen emergency management consciousness.
文摘针对大规模农田的粮食抢收工作,由于抢收约束时间的限制,导致可能无法在规定时间内完成所有粮食的收割任务,此时则会出现粮食损失值。构建以粮食损失值最小化为目标的农机跨区紧急调配模型,提出基于两阶段的农机紧急调配算法(Agricultural Machinery Emergency Algorithm based on Two Stages,TSEA)。首先按照基于距离的分区策略对大规模农田进行分区,接着采用改进的遗传算法分别对各农田分区进行农机紧急作业调配。为验证算法的有效性,主要从紧急任务的粮食损失值、算法运行时间等方面将TSEA算法与GA、SA算法进行比较,试验结果表明,TSEA算法得到的紧急调度方案优于其他两种算法。为验证本文分区策略的有效性,分别采用TSEA算法和不分区的紧急调配算法从粮食损失值、算法运行时间等方面进行比较,验证分区策略的有效性。多组试验结果表明该文提出的策略和算法对于解决大规模农田的农机跨区紧急调配问题更有效,可为农机管理部门提供解决方案。