In recent years,Deep Learning(DL),such as the algorithms of Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN)and Generative Adversarial Networks(GAN),has been widely studied and applied in various fiel...In recent years,Deep Learning(DL),such as the algorithms of Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN)and Generative Adversarial Networks(GAN),has been widely studied and applied in various fields including agriculture.Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique.This article provides a concise summary of major DL algorithms,including concepts,limitations,implementation,training processes,and example codes,to help researchers in agriculture to gain a holistic picture of major DL techniques quickly.Research on DL applications in agriculture is summarized and analyzed,and future opportunities are discussed in this paper,which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly,and further to facilitate data analysis,enhance related research in agriculture,and thus promote DL applications effectively.展开更多
Dissolved oxygen(DO),an important water quality indicator in aquaculture,affects the survival rate of aquatic creatures and the yield of aquatic production.Therefore,it is important to predict DO in fishery ponds for ...Dissolved oxygen(DO),an important water quality indicator in aquaculture,affects the survival rate of aquatic creatures and the yield of aquatic production.Therefore,it is important to predict DO in fishery ponds for applying artificial aeration with low energy and cost.Recently,deep learning models,such as recurrent neural network(RNN),long short-term memory(LSTM),and gated recurrent unit(GRU),are often used to predict the trend of time series,but it is unclear which one of them is more suitable for prediction of DO in fishery ponds.In this work,the RNN model,LSTM model,and GRU model were used to build three DO predicting models.The performance of the three models were compared by mean absolute error(MAE),mean square error(MSE),mean absolute percentage error(MAPE),and the coefficient of determination(R2).The performance of RNN is worse result than LSTM and GRU.The four evaluation indicators of GRU are 0.450 mg/L,0.411,0.054,and 0.994,and the four indicators of LSTM are 0.407 mg/L,0.294,0.059,and 0.970,which shows that the performance of GRU is similar to LSTM,but the time cost and number of parameters used for GRU is much lower than LSTM.It is concluded that the GRU has overall better performance and can be applied to practical applications.展开更多
基金This project is partially supported by National Natural Science Foundation of China(No.31771680)Fundamental Research Funds for the Central Universities of China(No:JUSRP51730A)+4 种基金the Modern Agriculture Funds of Jiangsu Province(No.BE2015310)the Modern Agriculture Funds of Jiangsu Province(Vegetable)(No.SXGC[2017]210)the New Agricultural Engineering of Jiangsu Province(No.SXGC[2016]106)the 111 Project(B1208)the Research Funds for New Faculty of Jiangnan University.
文摘In recent years,Deep Learning(DL),such as the algorithms of Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN)and Generative Adversarial Networks(GAN),has been widely studied and applied in various fields including agriculture.Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique.This article provides a concise summary of major DL algorithms,including concepts,limitations,implementation,training processes,and example codes,to help researchers in agriculture to gain a holistic picture of major DL techniques quickly.Research on DL applications in agriculture is summarized and analyzed,and future opportunities are discussed in this paper,which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly,and further to facilitate data analysis,enhance related research in agriculture,and thus promote DL applications effectively.
基金This project is partially supported by National Natural Science Foundation of China(No:31771680,No:51961125102,No:21706096)Fundamental Research Funds for the Central Universities of China(No:JUSRP51730A)+1 种基金the Modern Agriculture Funds of Jiangsu Province(No:BE2018334)the 111 Project(B12018)and the Research Funds for New Faculty of Jiangnan University。
文摘Dissolved oxygen(DO),an important water quality indicator in aquaculture,affects the survival rate of aquatic creatures and the yield of aquatic production.Therefore,it is important to predict DO in fishery ponds for applying artificial aeration with low energy and cost.Recently,deep learning models,such as recurrent neural network(RNN),long short-term memory(LSTM),and gated recurrent unit(GRU),are often used to predict the trend of time series,but it is unclear which one of them is more suitable for prediction of DO in fishery ponds.In this work,the RNN model,LSTM model,and GRU model were used to build three DO predicting models.The performance of the three models were compared by mean absolute error(MAE),mean square error(MSE),mean absolute percentage error(MAPE),and the coefficient of determination(R2).The performance of RNN is worse result than LSTM and GRU.The four evaluation indicators of GRU are 0.450 mg/L,0.411,0.054,and 0.994,and the four indicators of LSTM are 0.407 mg/L,0.294,0.059,and 0.970,which shows that the performance of GRU is similar to LSTM,but the time cost and number of parameters used for GRU is much lower than LSTM.It is concluded that the GRU has overall better performance and can be applied to practical applications.
