Designing low power sensor networks has been the general goal of design engineers, scientist and end users. It is desired to have a wireless sensor network (WSN) that will run on little power (if possible, none at all...Designing low power sensor networks has been the general goal of design engineers, scientist and end users. It is desired to have a wireless sensor network (WSN) that will run on little power (if possible, none at all) thereby saving cost, and the inconveniences of having to replace batteries in some difficult to access areas of usage. Previous researches on WSN energy models have focused less on the aggregate transceiver energy consumption models as compared to studies on other components of the node, hence a large portion of energy in a WSN still get depleted through data transmission. By studying the energy consumption map of the transceiver of a WSN node in different states and within state transitions, we propose in this paper the energy consumption model of the transceiver unit of a typical sensor node and the transceiver design parameters that significantly influences this energy consumption. The contribution of this paper is an innovative energy consumption model based on simple finite automata which reveals the relationship between the aggregate energy consumption and important power parameters that characterize the energy consumption map of the transceiver in a WSN;an ideal tool to design low power WSN.展开更多
Sea cucumbers usually live in an environment where lighting and visibility are generally not controllable,which cause the underwater image of sea cucumbers to be distorted,blurred,and severely attenuated.Therefore,the...Sea cucumbers usually live in an environment where lighting and visibility are generally not controllable,which cause the underwater image of sea cucumbers to be distorted,blurred,and severely attenuated.Therefore,the valuable information from such an image cannot be fully extracted for further processing.To solve the problems mentioned above and improve the quality of the underwater images of sea cucumbers,pre-processing of a sea cucumber image is attracting increasing interest.This paper presents a newmethod based on contrast limited adaptive histogram equalization and wavelet transform(CLAHE-WT)to enhance the sea cucumber image quality.CLAHE was used to process the underwater image for increasing contrast based on the Rayleigh distribution,and WTwas used for de-noising based on a soft threshold.Qualitative analysis indicated that the proposed method exhibited better performance in enhancing the quality and retaining the image details.For quantitative analysis,the test with 120 underwater images showed that for the proposed method,the mean square error(MSE),peak signal to noise ratio(PSNR),and entropy were 49.2098,13.3909,and 6.6815,respectively.The proposed method outperformed three established methods in enhancing the visual quality of sea cucumber underwater gray image.展开更多
Water quality models are important in predicting the changes in surface water quality for environmental management.A range of water quality models are wildly used,but every model has its advantages and limitations for...Water quality models are important in predicting the changes in surface water quality for environmental management.A range of water quality models are wildly used,but every model has its advantages and limitations for specific situations.The aim of this review is to provide a guide to researcher for selecting a suitable water quality model.Eight well known water quality models were selected for this review:SWAT,WASP,QUALs,MIKE 11,HSPF,CE-QUAL-W2,ELCOM-CAEDYM and EFDC.Each model is described according to its intended use,development,simulation elements,basic principles and applicability(e.g.,for rivers,lakes,and reservoirs and estuaries).Currently,the most important trends for future model development are:(1)combination models—individual models cannot completely solve the complex situations so combined models are needed to obtain the most appropriate results,(2)application of artificial intelligence and mechanistic models combined with nonmechanistic models will provide more accurate results because of the realistic parameters derived from nonmechanistic models,and(3)integration with remote sensing,geographical information and global position systems(3S)-3S can solve problems requiring large amounts of data.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
The use of electrical energy for heating and cooling systems to control the temperature in greenhouses will lead to high production and product costs.To solve this problem,shallow geothermal energy as a local source o...The use of electrical energy for heating and cooling systems to control the temperature in greenhouses will lead to high production and product costs.To solve this problem,shallow geothermal energy as a local source of energy could be applied.In this study,a measurement model,the distribution profiles of temperature,and a preliminary assessment of the geothermal potential in the shallow zone at depths of 0.1 m to 3.6 m in Shouguang City,Shandong Province,eastern China were presented.The measurement results showed that the annual average temperature at depths of 0.1–3.6 m ranged from 13.1℃ to 17.6℃.Preliminary assessment results of the geothermal potential showed that the daily average temperature difference between the air and at depths of 1.5–3.6 m was mainly from 10℃ to 25℃ during the winter months and between-15℃ and-5℃ during the summer months.Therefore,the heating systems could operate during January,February,November,and December.In May,June,and July,the cooling systems could be applied.Moreover,the measurement model gave good stability results,and it could be used in combination with the monitoring of the groundwater table,a survey of the thermal conductivity of the soil,climate change studies,which helps reduce unnecessary time and costs.展开更多
Fish behavior analysis for recognizing stress is very important for fish welfare and production management in aquaculture.Recent advances have been made in fish behavior analysis based on deep learning.However,most ex...Fish behavior analysis for recognizing stress is very important for fish welfare and production management in aquaculture.Recent advances have been made in fish behavior analysis based on deep learning.However,most existing methods with top performance rely on considerable memory and computational resources,which is impractical in the real-world scenario.In order to overcome the limitations of these methods,a new method based on knowledge distillation is proposed to identify the stress states of fish schools.The knowledge distillation architecture transfers additional inter-class information via a mixed relative loss function,and it forces a lightweight network(GhostNet)to mimic the soft probabilities output of a well-trained fish stress state recognition network(ResNeXt101).The fish school stress state recognition model’s accuracy is improved from 94.17%to 98.12%benefiting from the method.The proposed model has about 5.18 M parameters and requires 0.15 G FLOPs(floating-point operations)to process an image of size 224×224.Furthermore,fish behavior images are collected in a land-based factory,and a dataset is constructed and extended through flip,rotation,and color jitter augmentation techniques.The proposed method is also compared with other state-of-the-art methods.The experimental results show that the proposed model is more suitable for deployment on resource-constrained devices or real-time applications,and it is conducive for real-time monitoring of fish behavior.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
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
Sea cucumber culture and fishing are primarily dependent on manual work.For fast and accurate automatic identification of sea cucumbers,deep residual networks with different configures were conducted in this experimen...Sea cucumber culture and fishing are primarily dependent on manual work.For fast and accurate automatic identification of sea cucumbers,deep residual networks with different configures were conducted in this experiment to identify underwater sea cucumber.Sea cucumber images were captured by a C-Watch remotely operated underwater vehicle(ROV)in a sea cucumber fishery at Haiyang Qiandao Lake in Shandong Province,China and sliced to positive samples and negative samples.Two training algorithms,namely,the stochastic gradient descent algorithm(SGD)and Adam,activation functions ReLU and leaky ReLU,as well as learning rates of 0.001,0.005,0.01,0.05,and 0.1 were combined to form different models,which were trained with epochs 200 times and mini-batch of 100.The results showed that the accuracy of each model was higher than 82%,and the highest accuracy reached 89.53%under the SGD algorithm with ReLU and a learning rate of 0.05 or 0.1,which showed better generalization ability than that of other models.The performance of the proposed method indicates a great potential for automatic sea cucumber identification.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
Acoustic signals travels rapidly in water without attenuating fish telemetry.The digital sonar and passive acoustic has been used for fish monitoring and fish feeding.However,it is an urgent need to introduce new tech...Acoustic signals travels rapidly in water without attenuating fish telemetry.The digital sonar and passive acoustic has been used for fish monitoring and fish feeding.However,it is an urgent need to introduce new techniques in order to monitor the growth rate of fish during harvesting and without causing adverse effects to the harvested fish.Therefore,a novel technique was introduced to probe the acoustic signal frequency ratio in absence and presence of the fish in tanks,which basically uses an acoustic sensor(hydrophone),acoustic signal processing system(scope meter),and a signal monitoring system(fluke view).Acoustic signals were selected from 48-52 Hz frequency,measure of dispersion of frequency signal represented as a function of time via Xlstat software.Measure of dispersion displayed a significant effect of acoustic signal in the presence and absence of the fish in tanks.These optimised protocols of this study will help to control and prevent excessive wastage of feed and enhance proper utilization of feed that chiefly enhance fish growth in aquaculture.展开更多
This work develops a distributed environmental monitoring system for the combination of hydroponics and aquaculture based on the internet of things technology,which mainly includes the information perception layer,the...This work develops a distributed environmental monitoring system for the combination of hydroponics and aquaculture based on the internet of things technology,which mainly includes the information perception layer,the information transmission layer and the sys-tem architecture.The system has employed multiple sensors terminal to real-time acqui-sition,including air and water temperatures,dissolved oxygen etc.LoRa protocol is suitable for sending small data and the 4G was employed to collect data and send to the cloud plat-form.Java is used to develop background applications,to access cloud platforms and local data processing.Based on the collection and processing of environmental data and cloud service platform,the mobile application program client and remote login desktop have been developed.It has been implemented and tested in Tongzhou,Beijing for 3 months in 2020.The results showed the proposed monitoring system stability for overall operation and accuracy data transmission,which can support the actual hydroponics and aquacul-ture production management.After analysis of monitoring data collected from the devel-oped monitoring system,indoor air and water temperature have the obvious correlation with atmospheric pressure(0.7 and 0.9)and outdoor temperature(1.0 and 0.9),respectively.展开更多
Meta-analysis is a statistical analysis of the data obtained from multiple studies and pro-vides a quantitative synthesis of research results.It can be a key tool for facilitating rapid progress in aquaculture by quan...Meta-analysis is a statistical analysis of the data obtained from multiple studies and pro-vides a quantitative synthesis of research results.It can be a key tool for facilitating rapid progress in aquaculture by quantifying what is known and identifying what is not yet known.However,due to the complexity of the environment and problems associated with the use of model in aquaculture,it remain few guidelines for the design,implementation or interpretation of meta-analysis in the field of aquaculture.Here,we first briefly reviewed the history of meta-analysis,then summarized the applications of meta-analysis in terms of major procedures,standards,and methods.Next,we critically reviewed the results of meta-analysis studies in the production chain of aquaculture and identified the potentials for improving yield in both quantity and quality.Overall,there is a large room for improving yield along the production chain.Large contributions can be found in breeding,feed,and farm management.For example,improving breeding can increase yield by 5.6%to 49%,depending on fish species and type of improvements.This study revealed large potentials for improving yield in the production chain of aquaculture and summarized the application of meta-analysis in aquaculture.Some recommendations of standardizing and improving meta-analysis in aquaculture were proposed.展开更多
Welcome to the first issue of Information Processing in Agriculture(IPA)!This is an opportune time to clarify the goals and scopes of this new journal and to explain what we plan to achieve.Agriculture is a critical h...Welcome to the first issue of Information Processing in Agriculture(IPA)!This is an opportune time to clarify the goals and scopes of this new journal and to explain what we plan to achieve.Agriculture is a critical human activity that will become increasingly important as the world population grows.Given the necessity of feeding a populous planet,we must increase our output with sustainable resource utilizations.To achieve the desired increase in productivity,there are many challenges associated with agriculture that must be overcome;such as inherent heterogeneity,unanticipated disturbances,large geospatial dispersion,security and safety requirements,resource constraints,and so on.展开更多
文摘Designing low power sensor networks has been the general goal of design engineers, scientist and end users. It is desired to have a wireless sensor network (WSN) that will run on little power (if possible, none at all) thereby saving cost, and the inconveniences of having to replace batteries in some difficult to access areas of usage. Previous researches on WSN energy models have focused less on the aggregate transceiver energy consumption models as compared to studies on other components of the node, hence a large portion of energy in a WSN still get depleted through data transmission. By studying the energy consumption map of the transceiver of a WSN node in different states and within state transitions, we propose in this paper the energy consumption model of the transceiver unit of a typical sensor node and the transceiver design parameters that significantly influences this energy consumption. The contribution of this paper is an innovative energy consumption model based on simple finite automata which reveals the relationship between the aggregate energy consumption and important power parameters that characterize the energy consumption map of the transceiver in a WSN;an ideal tool to design low power WSN.
基金supported by the International Science&Technology Cooperation Program of China(2015DFA00090)Special Fund for Agro-scientific Research in the Public Interest(201203017).
文摘Sea cucumbers usually live in an environment where lighting and visibility are generally not controllable,which cause the underwater image of sea cucumbers to be distorted,blurred,and severely attenuated.Therefore,the valuable information from such an image cannot be fully extracted for further processing.To solve the problems mentioned above and improve the quality of the underwater images of sea cucumbers,pre-processing of a sea cucumber image is attracting increasing interest.This paper presents a newmethod based on contrast limited adaptive histogram equalization and wavelet transform(CLAHE-WT)to enhance the sea cucumber image quality.CLAHE was used to process the underwater image for increasing contrast based on the Rayleigh distribution,and WTwas used for de-noising based on a soft threshold.Qualitative analysis indicated that the proposed method exhibited better performance in enhancing the quality and retaining the image details.For quantitative analysis,the test with 120 underwater images showed that for the proposed method,the mean square error(MSE),peak signal to noise ratio(PSNR),and entropy were 49.2098,13.3909,and 6.6815,respectively.The proposed method outperformed three established methods in enhancing the visual quality of sea cucumber underwater gray image.
基金supported by the International Science and Technology Cooperation Program of China(2013DFA11320).
文摘Water quality models are important in predicting the changes in surface water quality for environmental management.A range of water quality models are wildly used,but every model has its advantages and limitations for specific situations.The aim of this review is to provide a guide to researcher for selecting a suitable water quality model.Eight well known water quality models were selected for this review:SWAT,WASP,QUALs,MIKE 11,HSPF,CE-QUAL-W2,ELCOM-CAEDYM and EFDC.Each model is described according to its intended use,development,simulation elements,basic principles and applicability(e.g.,for rivers,lakes,and reservoirs and estuaries).Currently,the most important trends for future model development are:(1)combination models—individual models cannot completely solve the complex situations so combined models are needed to obtain the most appropriate results,(2)application of artificial intelligence and mechanistic models combined with nonmechanistic models will provide more accurate results because of the realistic parameters derived from nonmechanistic models,and(3)integration with remote sensing,geographical information and global position systems(3S)-3S can solve problems requiring large amounts of data.
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financially supported by The International Technology Cooperation of China(2015DFA00090)Key Laboratory of Agricultural Information Acquisition Technology,Thousand Youth Talents Plan from the Organization Department of CCP Central Committee(China Agricultural University,China,China Grant No.62339001)Fundamental Research Funds for the Central Universities in China,China(Grant No.2018QC174)。
文摘The use of electrical energy for heating and cooling systems to control the temperature in greenhouses will lead to high production and product costs.To solve this problem,shallow geothermal energy as a local source of energy could be applied.In this study,a measurement model,the distribution profiles of temperature,and a preliminary assessment of the geothermal potential in the shallow zone at depths of 0.1 m to 3.6 m in Shouguang City,Shandong Province,eastern China were presented.The measurement results showed that the annual average temperature at depths of 0.1–3.6 m ranged from 13.1℃ to 17.6℃.Preliminary assessment results of the geothermal potential showed that the daily average temperature difference between the air and at depths of 1.5–3.6 m was mainly from 10℃ to 25℃ during the winter months and between-15℃ and-5℃ during the summer months.Therefore,the heating systems could operate during January,February,November,and December.In May,June,and July,the cooling systems could be applied.Moreover,the measurement model gave good stability results,and it could be used in combination with the monitoring of the groundwater table,a survey of the thermal conductivity of the soil,climate change studies,which helps reduce unnecessary time and costs.
基金supported by the National Science Foundation of China‘Analysis and feature recognition on feeding behavior of fish school in facility farming based on machine vision’(No.62076244)the National Key R&D Program of China‘Next generation precision aquaculture:R&D on intelligent measurement,control and equipment technologies’(China Grant No.2017YFE0122100).
文摘Fish behavior analysis for recognizing stress is very important for fish welfare and production management in aquaculture.Recent advances have been made in fish behavior analysis based on deep learning.However,most existing methods with top performance rely on considerable memory and computational resources,which is impractical in the real-world scenario.In order to overcome the limitations of these methods,a new method based on knowledge distillation is proposed to identify the stress states of fish schools.The knowledge distillation architecture transfers additional inter-class information via a mixed relative loss function,and it forces a lightweight network(GhostNet)to mimic the soft probabilities output of a well-trained fish stress state recognition network(ResNeXt101).The fish school stress state recognition model’s accuracy is improved from 94.17%to 98.12%benefiting from the method.The proposed model has about 5.18 M parameters and requires 0.15 G FLOPs(floating-point operations)to process an image of size 224×224.Furthermore,fish behavior images are collected in a land-based factory,and a dataset is constructed and extended through flip,rotation,and color jitter augmentation techniques.The proposed method is also compared with other state-of-the-art methods.The experimental results show that the proposed model is more suitable for deployment on resource-constrained devices or real-time applications,and it is conducive for real-time monitoring of fish behavior.
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 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This research was supported by International Science&Technology Cooperation Program of China(2015DFA00090).
文摘Sea cucumber culture and fishing are primarily dependent on manual work.For fast and accurate automatic identification of sea cucumbers,deep residual networks with different configures were conducted in this experiment to identify underwater sea cucumber.Sea cucumber images were captured by a C-Watch remotely operated underwater vehicle(ROV)in a sea cucumber fishery at Haiyang Qiandao Lake in Shandong Province,China and sliced to positive samples and negative samples.Two training algorithms,namely,the stochastic gradient descent algorithm(SGD)and Adam,activation functions ReLU and leaky ReLU,as well as learning rates of 0.001,0.005,0.01,0.05,and 0.1 were combined to form different models,which were trained with epochs 200 times and mini-batch of 100.The results showed that the accuracy of each model was higher than 82%,and the highest accuracy reached 89.53%under the SGD algorithm with ReLU and a learning rate of 0.05 or 0.1,which showed better generalization ability than that of other models.The performance of the proposed method indicates a great potential for automatic sea cucumber identification.
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We acknowledge that this work was financially supported by the Science and Technology Plan of Guangzhou City(Project No.201807010111)the Science and Technology Plan of Guangdong Province of China(Project No.2017B090903007)Innovative Research Team of Guangdong Province Agriculture Research System(2017LM2153)for funding this research.
文摘Acoustic signals travels rapidly in water without attenuating fish telemetry.The digital sonar and passive acoustic has been used for fish monitoring and fish feeding.However,it is an urgent need to introduce new techniques in order to monitor the growth rate of fish during harvesting and without causing adverse effects to the harvested fish.Therefore,a novel technique was introduced to probe the acoustic signal frequency ratio in absence and presence of the fish in tanks,which basically uses an acoustic sensor(hydrophone),acoustic signal processing system(scope meter),and a signal monitoring system(fluke view).Acoustic signals were selected from 48-52 Hz frequency,measure of dispersion of frequency signal represented as a function of time via Xlstat software.Measure of dispersion displayed a significant effect of acoustic signal in the presence and absence of the fish in tanks.These optimised protocols of this study will help to control and prevent excessive wastage of feed and enhance proper utilization of feed that chiefly enhance fish growth in aquaculture.
基金This research was financially supported by Research on Industrialization of Real-time Measurement and Control Technology for Efficient Fish-vegetable Symbiosis System(Project No.:KJ2019CX099).
文摘This work develops a distributed environmental monitoring system for the combination of hydroponics and aquaculture based on the internet of things technology,which mainly includes the information perception layer,the information transmission layer and the sys-tem architecture.The system has employed multiple sensors terminal to real-time acqui-sition,including air and water temperatures,dissolved oxygen etc.LoRa protocol is suitable for sending small data and the 4G was employed to collect data and send to the cloud plat-form.Java is used to develop background applications,to access cloud platforms and local data processing.Based on the collection and processing of environmental data and cloud service platform,the mobile application program client and remote login desktop have been developed.It has been implemented and tested in Tongzhou,Beijing for 3 months in 2020.The results showed the proposed monitoring system stability for overall operation and accuracy data transmission,which can support the actual hydroponics and aquacul-ture production management.After analysis of monitoring data collected from the devel-oped monitoring system,indoor air and water temperature have the obvious correlation with atmospheric pressure(0.7 and 0.9)and outdoor temperature(1.0 and 0.9),respectively.
基金This work was supported by the China Science and Technology Exchange Center[Grant no.2017YFE0122100]Institute of fishery machinery and instruments,Chinese Academy of Fishery Sciences[Grant no.2017YFD0701702]Guolian Aquatic Products Development Co.LTD[Grant no.2017B010126001].
文摘Meta-analysis is a statistical analysis of the data obtained from multiple studies and pro-vides a quantitative synthesis of research results.It can be a key tool for facilitating rapid progress in aquaculture by quantifying what is known and identifying what is not yet known.However,due to the complexity of the environment and problems associated with the use of model in aquaculture,it remain few guidelines for the design,implementation or interpretation of meta-analysis in the field of aquaculture.Here,we first briefly reviewed the history of meta-analysis,then summarized the applications of meta-analysis in terms of major procedures,standards,and methods.Next,we critically reviewed the results of meta-analysis studies in the production chain of aquaculture and identified the potentials for improving yield in both quantity and quality.Overall,there is a large room for improving yield along the production chain.Large contributions can be found in breeding,feed,and farm management.For example,improving breeding can increase yield by 5.6%to 49%,depending on fish species and type of improvements.This study revealed large potentials for improving yield in the production chain of aquaculture and summarized the application of meta-analysis in aquaculture.Some recommendations of standardizing and improving meta-analysis in aquaculture were proposed.
文摘Welcome to the first issue of Information Processing in Agriculture(IPA)!This is an opportune time to clarify the goals and scopes of this new journal and to explain what we plan to achieve.Agriculture is a critical human activity that will become increasingly important as the world population grows.Given the necessity of feeding a populous planet,we must increase our output with sustainable resource utilizations.To achieve the desired increase in productivity,there are many challenges associated with agriculture that must be overcome;such as inherent heterogeneity,unanticipated disturbances,large geospatial dispersion,security and safety requirements,resource constraints,and so on.
