[Objective]Fish pose estimation(FPE)provides fish physiological information,facilitating health monitoring in aquaculture.It aids decision-making in areas such as fish behavior recognition.When fish are injured or def...[Objective]Fish pose estimation(FPE)provides fish physiological information,facilitating health monitoring in aquaculture.It aids decision-making in areas such as fish behavior recognition.When fish are injured or deficient,they often display abnormal behaviors and noticeable changes in the positioning of their body parts.Moreover,the unpredictable posture and orientation of fish during swimming,combined with the rapid swimming speed of fish,restrict the current scope of research in FPE.In this research,a FPE model named HPFPE is presented to capture the swimming posture of fish and accurately detect their key points.[Methods]On the one hand,this model incorporated the CBAM module into the HRNet framework.The attention module enhanced accuracy without adding computational complexity,while effectively capturing a broader range of contextual information.On the other hand,the model incorporated dilated convolution to increase the receptive field,allowing it to capture more spatial context.[Results and Discussions]Experiments showed that compared with the baseline method,the average precision(AP)of HPFPE based on different backbones and input sizes on the oplegnathus punctatus datasets had increased by 0.62,1.35,1.76,and 1.28 percent point,respectively,while the average recall(AR)had also increased by 0.85,1.50,1.40,and 1.00,respectively.Additionally,HPFPE outperformed other mainstream methods,including DeepPose,CPM,SCNet,and Lite-HRNet.Furthermore,when compared to other methods using the ornamental fish data,HPFPE achieved the highest AP and AR values of 52.96%,and 59.50%,respectively.[Conclusions]The proposed HPFPE can accurately estimate fish posture and assess their swimming patterns,serving as a valuable reference for applications such as fish behavior recognition.展开更多
The diurnal variation in the sun's altitude alters the red-to-blue light spectrum ratio under identical water layers.This study explores how juvenile steelhead trout(Oncorhynchus mykiss)(initial weight:34.67 g...The diurnal variation in the sun's altitude alters the red-to-blue light spectrum ratio under identical water layers.This study explores how juvenile steelhead trout(Oncorhynchus mykiss)(initial weight:34.67 g±2.69 g)responds to these fluctuating light conditions,focusing on growth,daily activity levels,and energy budget.During 16 weeks,the experiment was conducted to examine six lighting scenarios:12 h white light followed by 12 h darkness(12W);12 h blue light followed by 12 h darkness(12B);12 h red light followed by 12 h darkness(12R);1.5 h blue light,9 h red light,and again 1.5 h blue light followed by 12 h darkness(3B9R);3 h blue light,6h red light,and again 3 h blue light followed by 12 h darkness(6B6R);and 12 h combined blue and red lights followed by 12 h darkness(T12BR).The findings reveal that the 3B9R lighting condition notably enhanced specific growth rate(SGR),feed conversion efficiency(FCE),and growth energy allocation,while diminishing daily activity levels in steelhead trout.Conversely,under the 6B6R condition,there was a significant reduction in SGR and FCE,indicating that growth was inhibited.Under the 12R condition,despite the high activity and respiratory energy loss,the trout exhibited improved SGR and FCE with reduced fecal energy loss.The study suggests that 3B9R and 12R lighting conditions might be beneficial in commercial steelhead trout farming,potentially lowering costs and boosting production.These results offer valuable insights for the application of supplementary lighting technology in salmon aquaculture.展开更多
Methane is an explosive gas in coalmines and needs to be monitored by methane sensors.Conductivetype methane sensors are small,simple and stable,and they are very promising for mining safety or home safety application...Methane is an explosive gas in coalmines and needs to be monitored by methane sensors.Conductivetype methane sensors are small,simple and stable,and they are very promising for mining safety or home safety applications.They can even be employed in mining Internet of things if the power consumption can be lowered down to few milliwatts.Many researches of nanomaterialsbased conductive-type methane sensors have been reported recently.This review intends to present a comprehensive and critical summary on the recent progresses in the nanomaterials-based conductive-type methane sensors field.Many excellent methane-sensitive nanomaterials will be present,such as SnO2,ZnO,TiO2,WO3,carbon nanotubes,graphene,rare earth metal-based perovskite oxides and their hybrids.Particular attention is given to the synthetic methods of the nanomaterials,sensing mechanisms of the nanomaterials and the relationship between the sensing performance and the structures and components of the nanomaterials.Finally,the future trends and perspectives of nanomaterials-based conductive-type methane sensors are proposed.展开更多
Energy consumption in the agricultural sector is significant,reaching 20%of the total energy consumption in China.Agricultural Energy Internet,an important extension of Energy Internet in the agricultural field,signif...Energy consumption in the agricultural sector is significant,reaching 20%of the total energy consumption in China.Agricultural Energy Internet,an important extension of Energy Internet in the agricultural field,significantly contributes to agricultural modernization.Key technologies of Agricultural Energy Internet are vital factors supporting its development.This article systematically reviews the key technologies of Agricultural Energy Internet for two areas:agriculture and fishery.The working mechanisms and power consumption characteristics of some state-of-the-art new-energy agricultural intelligent equipment are described.In addition,the principles and profit methods underlying the agro-industrial complementary operation model are introduced.Moreover,against the Agricultural Energy Internet background,the development trends of some state-of-theart new energy agricultural intelligent equipment,agro-industrial complementary,and carbon–neutral technology are proposed in this paper,providing novel perspectives on the promotion of the development of Agricultural Energy Internet and related technological innovation research.An unmanned farm is the main form of the future agricultural system,which is powered by the Agricultural Energy Internet based on smart agriculture and a smart grid.It will become the inevitable trend of modern agriculture to replace oil agriculture with electric farms.The electricity in farming is mainly generated by renewable energy.Renewable energy power generation has low carbon emissions and is the future direction for the development of agricultural energy systems.In addition,the Internet of Things will be further strengthened to realize automation and intelligence of agricultural energy systems。展开更多
As the new generation of artificial intelligence(AI)continues to evolve,weather big data and statistical machine learning(SML)technologies complement each other and are deeply integrated to significantly improve the p...As the new generation of artificial intelligence(AI)continues to evolve,weather big data and statistical machine learning(SML)technologies complement each other and are deeply integrated to significantly improve the processing and forecasting accuracy of fishery weather.Accurate fishery weather services play a crucial role in fishery production,serving as a great safeguard for economic benefits and personal safety,enabling fishermen to carry out fishery production better,and contributing to the sustainable development of the fishery industry.The objective of this paper is to offer an understanding of the present state of research and development in SML technology for simulating and forecasting fishery weather.Specifically,we analyze the current state of research and technical features of SML in weather and summarize the applications of SML in simulation and forecasting of fishery weather,which mainly include three aspects:fishery weather scenario generation,fishery weather forecasting,and fishery extreme weather warning.We also illustrate the main technical means and principles of SML technology.Finally,we summarize the most advanced SML fields and provide an outlook on their application value in the field of fishery weather.展开更多
Rural energy plays an important role in realizing the goals of“carbon peak”and“carbon neutrality”in China.In this paper,the countryside was regarded as the research object,and the rural energy internet was constru...Rural energy plays an important role in realizing the goals of“carbon peak”and“carbon neutrality”in China.In this paper,the countryside was regarded as the research object,and the rural energy internet was constructed to study the impact of rural energy development on rural carbon emissions.The most advanced energy and informative technologies in the development of rural energy were introduced from three perspectives,including rural living,rural planting and rural breeding.The benefits of rural energy internet in practical application,including energy and carbon benefits,were presented through three application cases.In general,a low-carbon,digital and intelligent rural energy will be developed,and the goals of“carbon peak”and“carbon neutrality”will be achieved by constructing and applying of rural energy internet in China.展开更多
Impact of rural electrification is picking up,which has changed the energy structure and significantly reduced greenhouse gas emissions in rural areas.At present,due to separation management of agricultural and energy...Impact of rural electrification is picking up,which has changed the energy structure and significantly reduced greenhouse gas emissions in rural areas.At present,due to separation management of agricultural and energy systems,agricultural cost is high,and new energy consumption in the local microgrid is small,but carbon emission is high.We propose a novel optimal operation strategy for a rural microgrid considering greenhouse load control,which is greenhouse environment control.We establish a greenhouse load control model,including an artificial lighting model,a heating load model,and a load shifting model.Our characteristic work is to establish a carbon dioxide model of greenhouse consumption,and we build a unique optimal operation model of a rural microgrid by combining control of carbon dioxide and control of the energy system.We simulate a rural microgrid with wind power,photovoltaic,gas-fired boiler,and cogeneration system.Summer and winter scenarios are used for analysis,as energy consumption patterns in greenhouses during these seasons are highly representative.Results show the proposed optimization strategy can effectively cut operating expenses for the rural microgrid,improve rate of new energy consumption in the local microgrid,and reduce carbon dioxide emissions.展开更多
The swift evolution of deep learning has greatly benefited the field of intensive aquaculture.Specifically,deep learning-based shrimp larvae detection has offered important technical assistance for counting shrimp lar...The swift evolution of deep learning has greatly benefited the field of intensive aquaculture.Specifically,deep learning-based shrimp larvae detection has offered important technical assistance for counting shrimp larvae and recognizing abnormal behaviors.Firstly,the transparent bodies and small sizes of shrimp larvae,combined with complex scenarios due to variations in light intensity and water turbidity,make it challenging for current detection methods to achieve high accuracy.Secondly,deep learning-based object detection demands substantial computing power and storage space,which restricts its application on edge devices.This paper proposes an efficient one-stage shrimp larvae detection method,FAMDet,specifically designed for complex scenarios in intensive aquaculture.Firstly,different from the ordinary detection methods,it exploits an efficient FasterNet backbone,constructed with partial convolution,to extract effective multi-scale shrimp larvae features.Meanwhile,we construct an adaptively bi-directional fusion neck to integrate high-level semantic information and low-level detail information of shrimp larvae in a matter that sufficiently merges features and further mitigates noise interference.Finally,a decoupled detection head equipped with MPDIoU is used for precise bounding box regression of shrimp larvae.We collected images of shrimp larvae from multiple scenarios and labeled 108,365 targets for experiments.Compared with the ordinary detection methods(Faster RCNN,SSD,RetinaNet,CenterNet,FCOS,DETR,and YOLOX_s),FAMDet has obtained considerable advantages in accuracy,speed,and complexity.Compared with the outstanding one-stage method YOLOv8s,it has improved accuracy while reducing 57%parameters,37%FLOPs,22%inference latency per image on CPU,and 56%storage overhead.Furthermore,FAMDet has still outperformed multiple lightweight methods(EfficientDet,RT-DETR,GhostNetV2,EfficientFormerV2,EfficientViT,and MobileNetV4).In addition,we conducted experiments on the public dataset(VOC 07+12)to further verify the effectiveness of FAMDet.Consequently,the proposed method can effectively alleviate the limitations faced by resource-constrained devices and achieve superior shrimp larvae detection results.展开更多
Efficient fish feeding is currently one of biggest challenges in aquaculture to enhance the production of fish quality and quantity. In this review, an information fusion approach was used to integrate multisensor and...Efficient fish feeding is currently one of biggest challenges in aquaculture to enhance the production of fish quality and quantity. In this review, an information fusion approach was used to integrate multisensor and computer vision techniques to make fish feeding more efficient and accurate. Information fusion is a well-known technology that has been used in different fields of artificial intelligence, robotics, image processing,computer vision, sensors and wireless sensor networks.Information fusion in aquaculture is a growing field of research that is used to enhance the performance of an"industrialized" ecosystem. This review study surveys different fish feeding systems using multi-sensor data fusion, computer vision technology, and different food intake models. In addition, different fish behavior monitoring techniques are discussed, and the parameters of water, p H, dissolved oxygen, turbidity, temperature etc.,necessary for the fish feeding process, are examined.Moreover, the different waste management and fish disease diagnosis techniques using different technologies,expert systems and modeling are also reviewed.展开更多
In the textile industry,it is always the case that cotton products are constitutive of many types of foreign fibers which affect the overall quality of cotton products.As the foundation of the foreign fiber automated ...In the textile industry,it is always the case that cotton products are constitutive of many types of foreign fibers which affect the overall quality of cotton products.As the foundation of the foreign fiber automated inspection,image process exerts a critical impact on the process of foreign fiber identification.This paper presents a new approach for the fast processing of foreign fiber images.This approach includes five main steps,image block,image predecision,image background extraction,image enhancement and segmentation,and image connection.At first,the captured color images were transformed into gray-scale images;followed by the inversion of gray-scale of the transformed images;then the whole image was divided into several blocks.Thereafter,the subsequent step is to judge which image block contains the target foreign fiber image through image pre-decision.Then we segment the image block via OSTU which possibly contains target images after background eradication and image strengthening.Finally,we connect those relevant segmented image blocks to get an intact and clear foreign fiber target image.The experimental result shows that this method of segmentation has the advantage of accuracy and speed over the other segmentation methods.On the other hand,this method also connects the target image that produce fractures therefore getting an intact and clear foreign fiber target image.展开更多
Ammonia nitrogen is one of the key parameters in determining the aquaculture water quality condition in pond.The high level of ammonia nitrogen is likely to cause deterioration of water quality and mass death of cultu...Ammonia nitrogen is one of the key parameters in determining the aquaculture water quality condition in pond.The high level of ammonia nitrogen is likely to cause deterioration of water quality and mass death of cultured subjects.Therefore,accurate detection of the cultured water ammonia nitrogen content is crucially important for aquaculture management.While,at present,the accuracy of equipment for measuring the ammonia nitrogen content of aquaculture water in real time cannot meet the requirements for aquaculture.In this paper,the soft computing method is firstly proposed to predict the ammonia nitrogen content in aquaculture water in real time.This method includes empirical mode decomposition(EMD),improved particle swarm optimization(IPSO)and extreme learning machine(ELM).To evaluate the performance of the soft computing techniques,three different statistic indicators were used,including root mean square error(RMSE),the mean absolute error(MAE),and the mean absolute percentage error(MAPE)to compare three artificial soft computing methods.Results showed that the EMD-IPSO-ELM soft computing method showed the best performance among other studied methods in the ammonia nitrogen real time prediction.The EMD-IPSO-ELM model provides moderately and roughly accurately real time prediction value of ammonia nitrogen in aquaculture water.展开更多
Maintaining suitable temperature level around tomato in the greenhouse is essential for the high-quality production.However,in summer,the temperature level around the tomato is usually unclear except using a high-prec...Maintaining suitable temperature level around tomato in the greenhouse is essential for the high-quality production.However,in summer,the temperature level around the tomato is usually unclear except using a high-precision temperature imager.To solve this problem,thermal performance of 3D(three-dimensional)tomato model built based on SolidWorks was investigated by the computational fluid dynamics(CFD)simulations.To assess the effect of temperature distribution around the tomato,a simplified 3D tomato numerical model was firstly validated by a set of field measurement data.The light intensity and indoor ventilation were regarded as the mainly environment factors in the Venlo greenhouse,thermal stratification around tomatoes at different time of day was further studied.The numerical results illustrated the different temperature distribution around tomato body under different radiation intensity.It was found that ventilation could obviously adjust the temperature gradient around the tomato,and alleviate high temperature effect particularly in summer.Suitable ventilation could create a suitable thermal environment for the tomato growth.This study clearly demonstrated 3D temperature distribution around tomatoes,which is beneficial to provide the reference for accurate detection of 3D tomato temperature and appropriate thermal environment design.展开更多
Accurate calculation for comprehensive power load of fishery energy internet plays a significant role in reasonable using of energy and reducing environmental pollution.However,as fishery power load is of greatly uniq...Accurate calculation for comprehensive power load of fishery energy internet plays a significant role in reasonable using of energy and reducing environmental pollution.However,as fishery power load is of greatly unique meteorology sensitivity,it continues to be a difficult problem.Therefore,the research of fishery meteorology is an important part of the rational development of fishery resources,the protection of production safety,and the pursuit of high and stable yield.This paper makes a deep study on the power load of the fishery energy internet under the influence of fishery meteorology and takes onshore fish pond as the research object.First of all,the power load is divided into three parts:oxygen enrichment power load,feeding power load,and water replenishment and drainage power load.The impact mechanism of fishery meteorology(including temperature,surface wind speed,precipitation,relative humidity,etc.)on it is described,and then the overall power load is obtained through modeling and integration.Finally,taking the Yuguang Complementary Project in Zhouquan Town,Tongxiang,Zhejiang Province,China as an example,using the meteorological data of its typical spring day and using the MATLAB tool to solve,the hourly comparison of the three types of power loads,the comprehensive power load demand,the full-day electricity charge forecast and the total annual power consumption are calculated.The annual power consumption per hectare and per kilogram of output calculated by simulation are basically consistent with the order of magnitude of the survey data,which proves the validity of the model proposed.The model established in this paper is an original work,and the exploration of fishery energy internet can draw lessons from it.展开更多
Portable measurement of ammonia nitrogen in water is of great significance for water qual-ity monitoring.It’s beneficial to reduce biological diseases and promote aquatic product safety.Traditional methods such as Ne...Portable measurement of ammonia nitrogen in water is of great significance for water qual-ity monitoring.It’s beneficial to reduce biological diseases and promote aquatic product safety.Traditional methods such as Nessler’s reagent method suffer from complex opera-tion,time delays and toxic residues.To realize simple and pollution-free detection,this paper develops a low-cost portable device for ammonia nitrogen detection.A test paper was proposed to cooperate the device and offer a chromogenic reaction.The portable device reduces the impact of any ambient light,simplifies the operation,and provides human–computer interaction.The result obtained for the detection range of 0.4–10 mg/L(R^(2) are 0.9902 and 0.9893 for the rang of 0.4–4.5 and 4.5–10 mg/L,respectively)with the detection limit of 0.36 mg/L,and the average recovery of aquaculture water is 100.98–137.75%.The results show that the portable device can provide a great potential for on-site detection ammonia nitrogen concentration.展开更多
Deep learning techniques can automatically learn features from a large number of image data set.Automatic vegetable image classification is the base of many applications.This paper proposed a high performance method f...Deep learning techniques can automatically learn features from a large number of image data set.Automatic vegetable image classification is the base of many applications.This paper proposed a high performance method for vegetable images classification based on deep learning framework.The AlexNet network model in Caffe was used to train the vegetable image data set.The vegetable image data set was obtained from ImageNet and divided into training data set and test data set.The output function of the AlexNet network adopted the Rectified Linear Units(ReLU)instead of the traditional sigmoid function and the tanh function,which can speed up the training of the deep learning network.The dropout technology was used to improve the generalization of the model.The image data extension method was used to reduce overfitting in the learning process.With AlexNet network model used for training different number of vegetable image data set,the experimental results showed that the classification accuracy decreases as the number of data set decreases.The experimental verification indicated that the accuracy rate of the deep learning method in the test data set reached as high as 92.1%,which was greatly improved compared with BP neural network(78%)and SVM classifier(80.5%)methods.展开更多
Aquaculture is the world’s fastest growing sector within the food industry,supplying humans with over half their aquatic products.Water quality monitoring or cage inspection is an indispensable part in aquaculture an...Aquaculture is the world’s fastest growing sector within the food industry,supplying humans with over half their aquatic products.Water quality monitoring or cage inspection is an indispensable part in aquaculture and is usually done manually.Autonomous underwater vehicles(AUVs)are increasingly being used in aquaculture as technology advances and the cost reduction.Autonomous navigation is considered as a basic function of AUVs but is a challenging issue primarily due to the attenuated nature of electromagnetic waves in water and unstructured underwater environments.An inertial navigation system(INS)is usually selected as the core navigation equipment for AUV navigation because it never fails to measure.This paper reviews and surveys the latest advances in integrated navigation technologies for AUVs and provides a comprehensive reference for researchers who intend to apply AUVs to autonomous monitoring of aquaculture.Pure INS has difficulty obtaining long-range precision navigation due to the inherent error accumulation of inertial sensors over time;aiding inertial navigation systems with auxiliary sensors are common means to improve the navigation accuracy of an INS for AUVs.The survey is conducted according to different assisted navigation technologies for inertial navigation.Finally,the future challenges of the AUV navigation are also presented.展开更多
The development of distributed renewable energy,such as photovoltaic power and wind power generation,makes the energy system cleaner,and is of great significance in reducing carbon emissions.However,weather can affect...The development of distributed renewable energy,such as photovoltaic power and wind power generation,makes the energy system cleaner,and is of great significance in reducing carbon emissions.However,weather can affect distributed renewable energy power generation,and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy.Energy systems with high penetration of distributed renewable energy involve the high-dimensional,nonlinear dynamics of large-scale complex systems,and the optimal solution of the uncertainty model is a difficult problem.From the perspective of statistical machine learning,the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.展开更多
Agricultural greenhouse production has to require a stable and acceptable environment,it is therefore essential for future greenhouse production to obtain full and precisely internal dynamic environment parameters.Dyn...Agricultural greenhouse production has to require a stable and acceptable environment,it is therefore essential for future greenhouse production to obtain full and precisely internal dynamic environment parameters.Dynamic modeling based on machine learning methods,e.g.,intelligent time series prediction modeling,is a popular and suitable way to solve the above issue.In this article,a systematic literature review on applying advanced time series models has been systematically conducted via a detailed analysis and evaluation of 61 pieces selected from 221 articles.The historical process of time series model application from the use of data and information strategies was first discussed.Subsequently,the accuracy and generalization of the model from the selection of model parameters and time steps,providing a new perspective for model development in this field,were compared and analyzed.Finally,the systematic review results demonstrate that,compared with traditional models,deep neural networks could increase data structure mining capabilities and overall information simulation capabilities through innovative and effective structures,thereby it could also broaden the selection range of environmental parameters for agricultural facilities and achieve environmental prediction end-to-end optimization via intelligent time series model based on deep neural networks.展开更多
The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality,which threaten macroeconomic stability a...The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality,which threaten macroeconomic stability and sustainable development.However,the recognition method based on manual and instruments has been unable to meet the needs of scientific research and production due to its strong subjectivity and low efficiency.The recognition method based on pattern recognition and deep learning can automatically fit image features,and use features to classify and predict images.This study introduced the improved Vision Transformer(ViT)method for crop pest image recognition.Among them,the region with the most obvious features can be effectively selected by block partition.The self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion area.In the experiment,data with 7 classes of examples are used for verification.It can be illustrated from results that this method has high accuracy and can give full play to the advantages of image processing and recognition technology,accurately judge the crop diseases and pests category,provide method reference for agricultural diseases and pests identification research,and further optimize the crop diseases and pests control work for agricultural workers in need.展开更多
Environmental parameter data collected by sensors for monitoring the environment of agricultural facility operations are usually incomplete due to external environmental disturbances and device failures.And the missin...Environmental parameter data collected by sensors for monitoring the environment of agricultural facility operations are usually incomplete due to external environmental disturbances and device failures.And the missing of collected data is completely at random.In practice,missing data could create biased estimations and make multivariate time series predictions of environmental parameters difficult,leading to imprecise environmental control.A multivariate time series imputation model based on generative adversarial networks and multi-head attention(ATTN-GAN)is proposed in this work to reducing the negative consequence of missing data.ATTN-GAN can capture the temporal and spatial correlation of time series,and has a good capacity to learn data distribution.In the downstream experiments,we used ATTN-GAN and baseline models for data imputation,and predicted the imputed data,respectively.For the imputation of missing data,over the 20%,50%and 80%missing rate,ATTN-GAN had the lowest RMSE,0.1593,0.2012 and 0.2688 respectively.For water temperature prediction,data processed with ATTN-GAN over MLP,LSTM,DA-RNN prediction methods had the lowest MSE,0.6816,0.8375 and 0.3736 respectively.Those results revealed that ATTN-GAN outperformed all baseline models in terms of data imputation accuracy.The data processed by ATTN-GAN is the best for time series prediction.展开更多
文摘[Objective]Fish pose estimation(FPE)provides fish physiological information,facilitating health monitoring in aquaculture.It aids decision-making in areas such as fish behavior recognition.When fish are injured or deficient,they often display abnormal behaviors and noticeable changes in the positioning of their body parts.Moreover,the unpredictable posture and orientation of fish during swimming,combined with the rapid swimming speed of fish,restrict the current scope of research in FPE.In this research,a FPE model named HPFPE is presented to capture the swimming posture of fish and accurately detect their key points.[Methods]On the one hand,this model incorporated the CBAM module into the HRNet framework.The attention module enhanced accuracy without adding computational complexity,while effectively capturing a broader range of contextual information.On the other hand,the model incorporated dilated convolution to increase the receptive field,allowing it to capture more spatial context.[Results and Discussions]Experiments showed that compared with the baseline method,the average precision(AP)of HPFPE based on different backbones and input sizes on the oplegnathus punctatus datasets had increased by 0.62,1.35,1.76,and 1.28 percent point,respectively,while the average recall(AR)had also increased by 0.85,1.50,1.40,and 1.00,respectively.Additionally,HPFPE outperformed other mainstream methods,including DeepPose,CPM,SCNet,and Lite-HRNet.Furthermore,when compared to other methods using the ornamental fish data,HPFPE achieved the highest AP and AR values of 52.96%,and 59.50%,respectively.[Conclusions]The proposed HPFPE can accurately estimate fish posture and assess their swimming patterns,serving as a valuable reference for applications such as fish behavior recognition.
基金supported by the Shandong Postdoctoral Science Foundation(No.SDCX-ZG-202302007)the National Key Research and Development Program of China(No.2019YFD0901000)+1 种基金the National Natural Science Foundation of China(Nos.U1906206 and 31872575)the Major Science and Technology Innovation Project of Shandong Province(No.SD2019YY006)。
文摘The diurnal variation in the sun's altitude alters the red-to-blue light spectrum ratio under identical water layers.This study explores how juvenile steelhead trout(Oncorhynchus mykiss)(initial weight:34.67 g±2.69 g)responds to these fluctuating light conditions,focusing on growth,daily activity levels,and energy budget.During 16 weeks,the experiment was conducted to examine six lighting scenarios:12 h white light followed by 12 h darkness(12W);12 h blue light followed by 12 h darkness(12B);12 h red light followed by 12 h darkness(12R);1.5 h blue light,9 h red light,and again 1.5 h blue light followed by 12 h darkness(3B9R);3 h blue light,6h red light,and again 3 h blue light followed by 12 h darkness(6B6R);and 12 h combined blue and red lights followed by 12 h darkness(T12BR).The findings reveal that the 3B9R lighting condition notably enhanced specific growth rate(SGR),feed conversion efficiency(FCE),and growth energy allocation,while diminishing daily activity levels in steelhead trout.Conversely,under the 6B6R condition,there was a significant reduction in SGR and FCE,indicating that growth was inhibited.Under the 12R condition,despite the high activity and respiratory energy loss,the trout exhibited improved SGR and FCE with reduced fecal energy loss.The study suggests that 3B9R and 12R lighting conditions might be beneficial in commercial steelhead trout farming,potentially lowering costs and boosting production.These results offer valuable insights for the application of supplementary lighting technology in salmon aquaculture.
基金financially supported by the Fundamental Research Funds for the Central Universities(No.2020QN69)。
文摘Methane is an explosive gas in coalmines and needs to be monitored by methane sensors.Conductivetype methane sensors are small,simple and stable,and they are very promising for mining safety or home safety applications.They can even be employed in mining Internet of things if the power consumption can be lowered down to few milliwatts.Many researches of nanomaterialsbased conductive-type methane sensors have been reported recently.This review intends to present a comprehensive and critical summary on the recent progresses in the nanomaterials-based conductive-type methane sensors field.Many excellent methane-sensitive nanomaterials will be present,such as SnO2,ZnO,TiO2,WO3,carbon nanotubes,graphene,rare earth metal-based perovskite oxides and their hybrids.Particular attention is given to the synthetic methods of the nanomaterials,sensing mechanisms of the nanomaterials and the relationship between the sensing performance and the structures and components of the nanomaterials.Finally,the future trends and perspectives of nanomaterials-based conductive-type methane sensors are proposed.
基金the National Natural Science Foun-dation of China under Grant 52007193 and The 2115 Talent Development Program of China Agricultural University.
文摘Energy consumption in the agricultural sector is significant,reaching 20%of the total energy consumption in China.Agricultural Energy Internet,an important extension of Energy Internet in the agricultural field,significantly contributes to agricultural modernization.Key technologies of Agricultural Energy Internet are vital factors supporting its development.This article systematically reviews the key technologies of Agricultural Energy Internet for two areas:agriculture and fishery.The working mechanisms and power consumption characteristics of some state-of-the-art new-energy agricultural intelligent equipment are described.In addition,the principles and profit methods underlying the agro-industrial complementary operation model are introduced.Moreover,against the Agricultural Energy Internet background,the development trends of some state-of-theart new energy agricultural intelligent equipment,agro-industrial complementary,and carbon–neutral technology are proposed in this paper,providing novel perspectives on the promotion of the development of Agricultural Energy Internet and related technological innovation research.An unmanned farm is the main form of the future agricultural system,which is powered by the Agricultural Energy Internet based on smart agriculture and a smart grid.It will become the inevitable trend of modern agriculture to replace oil agriculture with electric farms.The electricity in farming is mainly generated by renewable energy.Renewable energy power generation has low carbon emissions and is the future direction for the development of agricultural energy systems.In addition,the Internet of Things will be further strengthened to realize automation and intelligence of agricultural energy systems。
基金the National Natural Science Foundation of China under Grant 52007193 and The 2115 Talent Development Program of China Agricultural University.
文摘As the new generation of artificial intelligence(AI)continues to evolve,weather big data and statistical machine learning(SML)technologies complement each other and are deeply integrated to significantly improve the processing and forecasting accuracy of fishery weather.Accurate fishery weather services play a crucial role in fishery production,serving as a great safeguard for economic benefits and personal safety,enabling fishermen to carry out fishery production better,and contributing to the sustainable development of the fishery industry.The objective of this paper is to offer an understanding of the present state of research and development in SML technology for simulating and forecasting fishery weather.Specifically,we analyze the current state of research and technical features of SML in weather and summarize the applications of SML in simulation and forecasting of fishery weather,which mainly include three aspects:fishery weather scenario generation,fishery weather forecasting,and fishery extreme weather warning.We also illustrate the main technical means and principles of SML technology.Finally,we summarize the most advanced SML fields and provide an outlook on their application value in the field of fishery weather.
基金funded by the National Natural Science Foundation of China under Grant(52007193)The 2115 Talent Development Program of China Agricultural University,Inter school cooperation plan for 2021 college students’innovation and entrepreneurship training programin Beijing Universities(202198026)URP of China Agricultural University(X2021100190668).
文摘Rural energy plays an important role in realizing the goals of“carbon peak”and“carbon neutrality”in China.In this paper,the countryside was regarded as the research object,and the rural energy internet was constructed to study the impact of rural energy development on rural carbon emissions.The most advanced energy and informative technologies in the development of rural energy were introduced from three perspectives,including rural living,rural planting and rural breeding.The benefits of rural energy internet in practical application,including energy and carbon benefits,were presented through three application cases.In general,a low-carbon,digital and intelligent rural energy will be developed,and the goals of“carbon peak”and“carbon neutrality”will be achieved by constructing and applying of rural energy internet in China.
基金supported by the National Natural Science Foundation of China under Grant 52007193,and The 2115 Talent Development Program of China Agricultural University。
文摘Impact of rural electrification is picking up,which has changed the energy structure and significantly reduced greenhouse gas emissions in rural areas.At present,due to separation management of agricultural and energy systems,agricultural cost is high,and new energy consumption in the local microgrid is small,but carbon emission is high.We propose a novel optimal operation strategy for a rural microgrid considering greenhouse load control,which is greenhouse environment control.We establish a greenhouse load control model,including an artificial lighting model,a heating load model,and a load shifting model.Our characteristic work is to establish a carbon dioxide model of greenhouse consumption,and we build a unique optimal operation model of a rural microgrid by combining control of carbon dioxide and control of the energy system.We simulate a rural microgrid with wind power,photovoltaic,gas-fired boiler,and cogeneration system.Summer and winter scenarios are used for analysis,as energy consumption patterns in greenhouses during these seasons are highly representative.Results show the proposed optimization strategy can effectively cut operating expenses for the rural microgrid,improve rate of new energy consumption in the local microgrid,and reduce carbon dioxide emissions.
基金supported by the National Shrimp and Crab Industry Technical System Construction Project 2022(No.CARS-48)the National Natural Science Foundation of China(No.62076244)+2 种基金the Chinese Universities Scientific Fund(No.2022TC109)the Double First-class Project of China Agricultural University(2022)and the Double First-class International Cooperation Project of China Agricultural University(No.10020799).
文摘The swift evolution of deep learning has greatly benefited the field of intensive aquaculture.Specifically,deep learning-based shrimp larvae detection has offered important technical assistance for counting shrimp larvae and recognizing abnormal behaviors.Firstly,the transparent bodies and small sizes of shrimp larvae,combined with complex scenarios due to variations in light intensity and water turbidity,make it challenging for current detection methods to achieve high accuracy.Secondly,deep learning-based object detection demands substantial computing power and storage space,which restricts its application on edge devices.This paper proposes an efficient one-stage shrimp larvae detection method,FAMDet,specifically designed for complex scenarios in intensive aquaculture.Firstly,different from the ordinary detection methods,it exploits an efficient FasterNet backbone,constructed with partial convolution,to extract effective multi-scale shrimp larvae features.Meanwhile,we construct an adaptively bi-directional fusion neck to integrate high-level semantic information and low-level detail information of shrimp larvae in a matter that sufficiently merges features and further mitigates noise interference.Finally,a decoupled detection head equipped with MPDIoU is used for precise bounding box regression of shrimp larvae.We collected images of shrimp larvae from multiple scenarios and labeled 108,365 targets for experiments.Compared with the ordinary detection methods(Faster RCNN,SSD,RetinaNet,CenterNet,FCOS,DETR,and YOLOX_s),FAMDet has obtained considerable advantages in accuracy,speed,and complexity.Compared with the outstanding one-stage method YOLOv8s,it has improved accuracy while reducing 57%parameters,37%FLOPs,22%inference latency per image on CPU,and 56%storage overhead.Furthermore,FAMDet has still outperformed multiple lightweight methods(EfficientDet,RT-DETR,GhostNetV2,EfficientFormerV2,EfficientViT,and MobileNetV4).In addition,we conducted experiments on the public dataset(VOC 07+12)to further verify the effectiveness of FAMDet.Consequently,the proposed method can effectively alleviate the limitations faced by resource-constrained devices and achieve superior shrimp larvae detection results.
基金supported by International Science & Technology Cooperation Program of China (2013DFA11320)
文摘Efficient fish feeding is currently one of biggest challenges in aquaculture to enhance the production of fish quality and quantity. In this review, an information fusion approach was used to integrate multisensor and computer vision techniques to make fish feeding more efficient and accurate. Information fusion is a well-known technology that has been used in different fields of artificial intelligence, robotics, image processing,computer vision, sensors and wireless sensor networks.Information fusion in aquaculture is a growing field of research that is used to enhance the performance of an"industrialized" ecosystem. This review study surveys different fish feeding systems using multi-sensor data fusion, computer vision technology, and different food intake models. In addition, different fish behavior monitoring techniques are discussed, and the parameters of water, p H, dissolved oxygen, turbidity, temperature etc.,necessary for the fish feeding process, are examined.Moreover, the different waste management and fish disease diagnosis techniques using different technologies,expert systems and modeling are also reviewed.
基金The authors thank National Natural Science Foundation of China(30971693,61170039)Ministry of Education of People’s Republic of China(NCET-09-0731)+2 种基金Hebei Education Department(Q2012063)Hebei University(2010-207)Key Laboratory of Modern Precision Agriculture System Integration Research,Ministry of Education(X11-01),for their financial support.
文摘In the textile industry,it is always the case that cotton products are constitutive of many types of foreign fibers which affect the overall quality of cotton products.As the foundation of the foreign fiber automated inspection,image process exerts a critical impact on the process of foreign fiber identification.This paper presents a new approach for the fast processing of foreign fiber images.This approach includes five main steps,image block,image predecision,image background extraction,image enhancement and segmentation,and image connection.At first,the captured color images were transformed into gray-scale images;followed by the inversion of gray-scale of the transformed images;then the whole image was divided into several blocks.Thereafter,the subsequent step is to judge which image block contains the target foreign fiber image through image pre-decision.Then we segment the image block via OSTU which possibly contains target images after background eradication and image strengthening.Finally,we connect those relevant segmented image blocks to get an intact and clear foreign fiber target image.The experimental result shows that this method of segmentation has the advantage of accuracy and speed over the other segmentation methods.On the other hand,this method also connects the target image that produce fractures therefore getting an intact and clear foreign fiber target image.
基金This work was supported by“the Fundamental Research Funds for the Central Universities”(Project Number:BLX201825)the“Next Generation Precision Aquaculture:R&D on intelligent measurement,control technology”(Project Number:2017YFE0122100).
文摘Ammonia nitrogen is one of the key parameters in determining the aquaculture water quality condition in pond.The high level of ammonia nitrogen is likely to cause deterioration of water quality and mass death of cultured subjects.Therefore,accurate detection of the cultured water ammonia nitrogen content is crucially important for aquaculture management.While,at present,the accuracy of equipment for measuring the ammonia nitrogen content of aquaculture water in real time cannot meet the requirements for aquaculture.In this paper,the soft computing method is firstly proposed to predict the ammonia nitrogen content in aquaculture water in real time.This method includes empirical mode decomposition(EMD),improved particle swarm optimization(IPSO)and extreme learning machine(ELM).To evaluate the performance of the soft computing techniques,three different statistic indicators were used,including root mean square error(RMSE),the mean absolute error(MAE),and the mean absolute percentage error(MAPE)to compare three artificial soft computing methods.Results showed that the EMD-IPSO-ELM soft computing method showed the best performance among other studied methods in the ammonia nitrogen real time prediction.The EMD-IPSO-ELM model provides moderately and roughly accurately real time prediction value of ammonia nitrogen in aquaculture water.
基金supported by Science and Technology Cooperation-Sino-Malta Fund 2019:Research and Demonstration of Real-time Accurate Monitoring System for Early-stage Fish in Recirculating Aquaculture System(AquaDetector,Grant No.2019YFE0103700)Overseas Highlevel Youth Talents Program(China Agricultural University,China,Grant No.62339001)+2 种基金China Agricultural University Excellent Talents Plan(Grant No.31051015)Major Science and Technology Innovation Fund 2019 of Shandong Province(Grant No.2019JZZY010703)National Innovation Center for Digital Fishery,and Beijing Engineering and Technology Research Center for Internet of Things in Agriculture.The authors also appreciate constructive。
文摘Maintaining suitable temperature level around tomato in the greenhouse is essential for the high-quality production.However,in summer,the temperature level around the tomato is usually unclear except using a high-precision temperature imager.To solve this problem,thermal performance of 3D(three-dimensional)tomato model built based on SolidWorks was investigated by the computational fluid dynamics(CFD)simulations.To assess the effect of temperature distribution around the tomato,a simplified 3D tomato numerical model was firstly validated by a set of field measurement data.The light intensity and indoor ventilation were regarded as the mainly environment factors in the Venlo greenhouse,thermal stratification around tomatoes at different time of day was further studied.The numerical results illustrated the different temperature distribution around tomato body under different radiation intensity.It was found that ventilation could obviously adjust the temperature gradient around the tomato,and alleviate high temperature effect particularly in summer.Suitable ventilation could create a suitable thermal environment for the tomato growth.This study clearly demonstrated 3D temperature distribution around tomatoes,which is beneficial to provide the reference for accurate detection of 3D tomato temperature and appropriate thermal environment design.
基金supported by the National Natural Science Foundation of China under Grant 52007193 and The 2115 Talent Development Program of China Agricultural University.
文摘Accurate calculation for comprehensive power load of fishery energy internet plays a significant role in reasonable using of energy and reducing environmental pollution.However,as fishery power load is of greatly unique meteorology sensitivity,it continues to be a difficult problem.Therefore,the research of fishery meteorology is an important part of the rational development of fishery resources,the protection of production safety,and the pursuit of high and stable yield.This paper makes a deep study on the power load of the fishery energy internet under the influence of fishery meteorology and takes onshore fish pond as the research object.First of all,the power load is divided into three parts:oxygen enrichment power load,feeding power load,and water replenishment and drainage power load.The impact mechanism of fishery meteorology(including temperature,surface wind speed,precipitation,relative humidity,etc.)on it is described,and then the overall power load is obtained through modeling and integration.Finally,taking the Yuguang Complementary Project in Zhouquan Town,Tongxiang,Zhejiang Province,China as an example,using the meteorological data of its typical spring day and using the MATLAB tool to solve,the hourly comparison of the three types of power loads,the comprehensive power load demand,the full-day electricity charge forecast and the total annual power consumption are calculated.The annual power consumption per hectare and per kilogram of output calculated by simulation are basically consistent with the order of magnitude of the survey data,which proves the validity of the model proposed.The model established in this paper is an original work,and the exploration of fishery energy internet can draw lessons from it.
基金The study was supported by Shandong Province Major Science and Technology Innovation Project (Project No.2019JZZY010703)the National Key Research and Devel-opment Program of China:Sino-Malta Fund 2019 (Project No.2019YFE0103700).
文摘Portable measurement of ammonia nitrogen in water is of great significance for water qual-ity monitoring.It’s beneficial to reduce biological diseases and promote aquatic product safety.Traditional methods such as Nessler’s reagent method suffer from complex opera-tion,time delays and toxic residues.To realize simple and pollution-free detection,this paper develops a low-cost portable device for ammonia nitrogen detection.A test paper was proposed to cooperate the device and offer a chromogenic reaction.The portable device reduces the impact of any ambient light,simplifies the operation,and provides human–computer interaction.The result obtained for the detection range of 0.4–10 mg/L(R^(2) are 0.9902 and 0.9893 for the rang of 0.4–4.5 and 4.5–10 mg/L,respectively)with the detection limit of 0.36 mg/L,and the average recovery of aquaculture water is 100.98–137.75%.The results show that the portable device can provide a great potential for on-site detection ammonia nitrogen concentration.
基金This research was financially supported by the International Science&Technology Cooperation Program of China(2015DFA00530)Key Research and Development Plan Project of Shandong Province(2016CYJS03A02).
文摘Deep learning techniques can automatically learn features from a large number of image data set.Automatic vegetable image classification is the base of many applications.This paper proposed a high performance method for vegetable images classification based on deep learning framework.The AlexNet network model in Caffe was used to train the vegetable image data set.The vegetable image data set was obtained from ImageNet and divided into training data set and test data set.The output function of the AlexNet network adopted the Rectified Linear Units(ReLU)instead of the traditional sigmoid function and the tanh function,which can speed up the training of the deep learning network.The dropout technology was used to improve the generalization of the model.The image data extension method was used to reduce overfitting in the learning process.With AlexNet network model used for training different number of vegetable image data set,the experimental results showed that the classification accuracy decreases as the number of data set decreases.The experimental verification indicated that the accuracy rate of the deep learning method in the test data set reached as high as 92.1%,which was greatly improved compared with BP neural network(78%)and SVM classifier(80.5%)methods.
基金The authors would like to thank native English speaker Leila A.for polishing our paper.Finally,this paper was supported by the International Science&Technology Cooperation Program of China(2015DFA00090,2015DFA00530).
文摘Aquaculture is the world’s fastest growing sector within the food industry,supplying humans with over half their aquatic products.Water quality monitoring or cage inspection is an indispensable part in aquaculture and is usually done manually.Autonomous underwater vehicles(AUVs)are increasingly being used in aquaculture as technology advances and the cost reduction.Autonomous navigation is considered as a basic function of AUVs but is a challenging issue primarily due to the attenuated nature of electromagnetic waves in water and unstructured underwater environments.An inertial navigation system(INS)is usually selected as the core navigation equipment for AUV navigation because it never fails to measure.This paper reviews and surveys the latest advances in integrated navigation technologies for AUVs and provides a comprehensive reference for researchers who intend to apply AUVs to autonomous monitoring of aquaculture.Pure INS has difficulty obtaining long-range precision navigation due to the inherent error accumulation of inertial sensors over time;aiding inertial navigation systems with auxiliary sensors are common means to improve the navigation accuracy of an INS for AUVs.The survey is conducted according to different assisted navigation technologies for inertial navigation.Finally,the future challenges of the AUV navigation are also presented.
基金supported by the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources under Grant No.LAPS21016the National Natural Science Foundation of China under Grant 52007193the 2115 Talent Development Program of China Agricultural University.
文摘The development of distributed renewable energy,such as photovoltaic power and wind power generation,makes the energy system cleaner,and is of great significance in reducing carbon emissions.However,weather can affect distributed renewable energy power generation,and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy.Energy systems with high penetration of distributed renewable energy involve the high-dimensional,nonlinear dynamics of large-scale complex systems,and the optimal solution of the uncertainty model is a difficult problem.From the perspective of statistical machine learning,the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.
基金Overseas High-level Youth Talents Program(China Agricultural University,China,Grant No.62339001)Science and Technology Cooperation-Sino-Malta Fund 2019:Research and Demonstration of Real-time Accurate Monitoring System for Early-stage Fish in Recirculating Aquaculture System(AquaDetector,Grant No.2019YFE0103700)+1 种基金China Agricultural University Excellent Talents Plan(Grant No.31051015)Major Science and Technology Innovation Fund 2019 of Shandong Province(Grant No.2019JZZY010703),National Innovation Center for Digital Fishery,and Beijing Engineering and Technology Research Center for Internet of Things in Agriculture.The authors also appreciate constructive and valuable comments provided by reviewers.
文摘Agricultural greenhouse production has to require a stable and acceptable environment,it is therefore essential for future greenhouse production to obtain full and precisely internal dynamic environment parameters.Dynamic modeling based on machine learning methods,e.g.,intelligent time series prediction modeling,is a popular and suitable way to solve the above issue.In this article,a systematic literature review on applying advanced time series models has been systematically conducted via a detailed analysis and evaluation of 61 pieces selected from 221 articles.The historical process of time series model application from the use of data and information strategies was first discussed.Subsequently,the accuracy and generalization of the model from the selection of model parameters and time steps,providing a new perspective for model development in this field,were compared and analyzed.Finally,the systematic review results demonstrate that,compared with traditional models,deep neural networks could increase data structure mining capabilities and overall information simulation capabilities through innovative and effective structures,thereby it could also broaden the selection range of environmental parameters for agricultural facilities and achieve environmental prediction end-to-end optimization via intelligent time series model based on deep neural networks.
基金the National Natural Science Foundation of China under Grant 52007193 and The 2115 Talent Development Program of China Agricultural University.
文摘The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality,which threaten macroeconomic stability and sustainable development.However,the recognition method based on manual and instruments has been unable to meet the needs of scientific research and production due to its strong subjectivity and low efficiency.The recognition method based on pattern recognition and deep learning can automatically fit image features,and use features to classify and predict images.This study introduced the improved Vision Transformer(ViT)method for crop pest image recognition.Among them,the region with the most obvious features can be effectively selected by block partition.The self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion area.In the experiment,data with 7 classes of examples are used for verification.It can be illustrated from results that this method has high accuracy and can give full play to the advantages of image processing and recognition technology,accurately judge the crop diseases and pests category,provide method reference for agricultural diseases and pests identification research,and further optimize the crop diseases and pests control work for agricultural workers in need.
基金supported by the National Natural Science Foundation of China:“Regularity and prediction model of juvenile fish growth under synergistic effect of water temperature and flow fields in recirculating aquaculture”(Grant No.32373185)2115 Talent Development Program of China Agricultural University,Overseas High-level Youth Talents Program(China Agricultural University,China,Grant No.62339001)+2 种基金China Agricultural University Excellent Talents Plan(Grant No.31051015)Major Science and Technology Innovation Fund 2019 of Shandong Province(Grant No.2019JZZY010703)National Innovation Center for Digital Fishery,and Beijing Engineering and Technology Research Center for Internet of Things in Agriculture.
文摘Environmental parameter data collected by sensors for monitoring the environment of agricultural facility operations are usually incomplete due to external environmental disturbances and device failures.And the missing of collected data is completely at random.In practice,missing data could create biased estimations and make multivariate time series predictions of environmental parameters difficult,leading to imprecise environmental control.A multivariate time series imputation model based on generative adversarial networks and multi-head attention(ATTN-GAN)is proposed in this work to reducing the negative consequence of missing data.ATTN-GAN can capture the temporal and spatial correlation of time series,and has a good capacity to learn data distribution.In the downstream experiments,we used ATTN-GAN and baseline models for data imputation,and predicted the imputed data,respectively.For the imputation of missing data,over the 20%,50%and 80%missing rate,ATTN-GAN had the lowest RMSE,0.1593,0.2012 and 0.2688 respectively.For water temperature prediction,data processed with ATTN-GAN over MLP,LSTM,DA-RNN prediction methods had the lowest MSE,0.6816,0.8375 and 0.3736 respectively.Those results revealed that ATTN-GAN outperformed all baseline models in terms of data imputation accuracy.The data processed by ATTN-GAN is the best for time series prediction.
