Crop-yield is a crucial metric in agriculture,essential for effective sector management and improving the overall production process.This indicator is heavily influenced by numerous environmental factors,particularly ...Crop-yield is a crucial metric in agriculture,essential for effective sector management and improving the overall production process.This indicator is heavily influenced by numerous environmental factors,particularly those related to soil and climate,which present a challenging task due to the complex interactions involved.In this paper,we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction.This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods,specifically representation learning techniques,along with predictions derived from remote sensing imagery.We tested our proposed methodology on a public dataset centered on corn,aiming to predict crop-yield.Our developed smart model achieved promising results in terms of crop-yield prediction,with a root mean squared error(RMSE)of 1.72,outperforming the baseline models.The ontologybased approach achieved an RMSE of 1.73,while the remote sensing-based method yielded an RMSE of 1.77.This confirms the superior performance of our proposed approach over those using single modalities.This in-tegrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence(AI)represents a significant advancement in agricultural applications.It is particularly effective for crop-yield prediction at the field scale,thus facilitating more informed decision-making in advanced agricultural prac-tices.Additionally,it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy.展开更多
基于中国知网和Web of Science数据库,以“菌渣”“产量”和“生长”等关键词组合检索文献,并提取相应数据。采用Meta分析方法,以单施化肥处理为对照,选择响应比的自然对数(ln R)作为效应值,量化食用菌菌渣施用对作物产量的综合效应,并...基于中国知网和Web of Science数据库,以“菌渣”“产量”和“生长”等关键词组合检索文献,并提取相应数据。采用Meta分析方法,以单施化肥处理为对照,选择响应比的自然对数(ln R)作为效应值,量化食用菌菌渣施用对作物产量的综合效应,并探讨相关影响因素。结果表明,菌渣施用能显著改善作物的主要农艺性状并提高产量,按菌渣对不同作物的产量增幅排序,果类作物>其他经济作物>蔬菜作物>粮食作物。菌渣对作物的增产效应受环境条件的显著影响,地理区域方面,北方地区增产效应大于南方地区;土壤条件方面,在养分较低或pH失衡的土壤中增产效应更显著;气候条件方面,当年均降水量≤600 mm、年均气温10~15℃时增产效应最大。菌渣对作物的增产效应受其特性的显著影响,具体表现为草腐型菌渣的增产效应大于木腐型菌渣;低碳氮比(≤35)菌渣的增产效应大于高碳氮比(>35)菌渣;菌渣堆肥后施用的增产效应大于直接施用。当化肥减施比例≤30%或菌渣配施比例≤30%时,菌渣对作物的增产效应达到最大。综上所述,食用菌菌渣施用具有良好的生态和经济效益。同时,菌渣对作物的增产效应受种植区域、气候条件、土壤理化性质、作物品种、菌渣特性、菌渣施用方式及比例的显著影响,需要因地制宜地制定栽培管理策略。展开更多
基金partially funded by the JSPS KAKENHI Grant Number JP22K18004.
文摘Crop-yield is a crucial metric in agriculture,essential for effective sector management and improving the overall production process.This indicator is heavily influenced by numerous environmental factors,particularly those related to soil and climate,which present a challenging task due to the complex interactions involved.In this paper,we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction.This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods,specifically representation learning techniques,along with predictions derived from remote sensing imagery.We tested our proposed methodology on a public dataset centered on corn,aiming to predict crop-yield.Our developed smart model achieved promising results in terms of crop-yield prediction,with a root mean squared error(RMSE)of 1.72,outperforming the baseline models.The ontologybased approach achieved an RMSE of 1.73,while the remote sensing-based method yielded an RMSE of 1.77.This confirms the superior performance of our proposed approach over those using single modalities.This in-tegrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence(AI)represents a significant advancement in agricultural applications.It is particularly effective for crop-yield prediction at the field scale,thus facilitating more informed decision-making in advanced agricultural prac-tices.Additionally,it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy.
文摘基于中国知网和Web of Science数据库,以“菌渣”“产量”和“生长”等关键词组合检索文献,并提取相应数据。采用Meta分析方法,以单施化肥处理为对照,选择响应比的自然对数(ln R)作为效应值,量化食用菌菌渣施用对作物产量的综合效应,并探讨相关影响因素。结果表明,菌渣施用能显著改善作物的主要农艺性状并提高产量,按菌渣对不同作物的产量增幅排序,果类作物>其他经济作物>蔬菜作物>粮食作物。菌渣对作物的增产效应受环境条件的显著影响,地理区域方面,北方地区增产效应大于南方地区;土壤条件方面,在养分较低或pH失衡的土壤中增产效应更显著;气候条件方面,当年均降水量≤600 mm、年均气温10~15℃时增产效应最大。菌渣对作物的增产效应受其特性的显著影响,具体表现为草腐型菌渣的增产效应大于木腐型菌渣;低碳氮比(≤35)菌渣的增产效应大于高碳氮比(>35)菌渣;菌渣堆肥后施用的增产效应大于直接施用。当化肥减施比例≤30%或菌渣配施比例≤30%时,菌渣对作物的增产效应达到最大。综上所述,食用菌菌渣施用具有良好的生态和经济效益。同时,菌渣对作物的增产效应受种植区域、气候条件、土壤理化性质、作物品种、菌渣特性、菌渣施用方式及比例的显著影响,需要因地制宜地制定栽培管理策略。
