Key technologies and applications of agricultural energy Internet for agricultural planting and fisheries industry

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。

Modeling of comprehensive power load of fishery energy internet considering fishery meteorology

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.

Information fusion in aquaculture:a state-of the art review

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.

A hybrid intelligent soft computing method for ammonia nitrogen prediction in aquaculture

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.

High performance vegetable classification from images based on AlexNet deep learning model

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.

Integrated navigation for autonomous underwater vehicles in aquaculture: A review

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.

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

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.

Portable device for on-site detection of ammonia nitrogen

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.