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Automated garden-insect recognition using improved lightweight convolution network

Information Processing in Agriculture 2023年第2期10卷 256-266页

作者： Zhankui yang xinting yang Ming Li Wenyong Li College of Computer Science and Technology Beijing University of TechnologyBeijing 100124China National Engineering Research Center for Information Technology in Agriculture Beijing 100089China National Engineering Laboratory for Quality and Safety Traceability Technology and Application of Agricultural Products Beijing 100089China

Automated recognition of insect category,which currently is performed mainly by agriculture experts,is a challenging problem that has received increasing attention in recent *** goal of the present research is to develop an intelligent mobile-terminal recognition system based on deep neural networks to recognize garden insects in a device that can be conveniently deployed in mobile ***-of-the-art lightweight convolutional neural networks(such as SqueezeNet and ShuffleNet)have the same accuracy as classical convolutional neural networks such as AlexNet but fewer parameters,thereby not only requiring communication across servers during distributed training but also being more feasible to deploy on mobile terminals and other hardware with limited *** this research,we connect with the rich details of the low-level network features and the rich semantic information of the high-level network features to construct more rich semantic information feature maps which can effectively improve SqueezeNet model with a small computational *** addition,we developed an off-line insect recognition software that can be deployed on the mobile terminal to solve no network and the timedelay problems in the *** demonstrate that the proposed method is promising for recognition while remaining within a limited computational budget and delivers a much higher recognition accuracy of 91.64%with less training time relative to other classical convolutional neural *** have also verified the results that the improved SqueezeNet model has a 2.3%higher than of the original model in the open insect data IP102.

关键词： Insect classification Mobile-terminal recognition SqueezeNet model Deep lightweight convolution network

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Fish Feeding Behavior Recognition Using Adaptive DMCA-UMT Algorithm

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Journal of Beijing Institute of Technology 2023年第3期32卷 285-297页

作者： Caiwei yang xinting yang Kaijie Zhu Chao Zhou National Engineering Research Center for Information Technology in Agriculture Beijing 100097China Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China National Engineering Laboratory for Agri-product Quality Traceability Beijing 100097China College of Computer and Information Engineering Tianjin Agricultural UniversityTianjin 300384China

Realtime analyzing the feeding behavior of fish is the premise and key to accurate guidance on *** identification of fish behavior using a single information is susceptible to various *** overcome the problems,this paper proposes an adaptive deep modular co-attention unified multi-modal transformers(DMCA-UMT).By fusing the video,audio and water quality parameters,the whole process of fish feeding behavior could be ***,for the input video,audio and water quality parameter information,features are extracted to obtain feature vectors of different ***,deep modular co-attention(DMCA)is introduced on the basis of the original cross-modal encoder,and the adaptive learnable weights are *** feature vector of video and audio joint representation is obtained by automatic learning based on fusion ***,the information of visual-audio modality fusion and text features are used to generate clip-level moment *** query decoder decodes the input features and uses the prediction head to obtain the final joint moment retrieval,which is the start-end time of feeding the *** results show that the mAP Avg of the proposed algorithm reaches 75.3%,which is37.8%higher than that of unified multi-modal transformers(UMT)algorithm.

关键词： aquaculture multi-modal fusion deep modular co-attention(DMCA) unified multimodal transformers(UMT) video moment retrieval

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Automatic greenhouse pest recognition based on multiple color space features

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International Journal of Agricultural and Biological Engineering 2021年第2期14卷 188-195页

作者： Zhankui yang Wenyong Li Ming Li xinting yang College of Computer Science and Technology Beijing University of TechnologyBeijing 100124China National Engineering Research Center for Information Technology in Agriculture Beijing 100089China National Engineering Laboratory for Quality and Safety Traceability Technology and Application of Agricultural Products Beijing 100089China

Recognition and counting of greenhouse pests are important for monitoring and forecasting pest population *** study used image processing techniques to recognize and count whiteflies and thrips on a sticky trap located in a greenhouse *** digital images of sticky traps were collected using an image-acquisition system under different greenhouse *** a single color space is used,it is difficult to segment the small pests correctly because of the detrimental effects of non-uniform illumination in complex ***,a method that first segments object pests in two color spaces using the Prewitt operator in I component of the hue-saturation-intensity(HSI)color space and the Canny operator in the B component of the Lab color space was ***,the segmented results for the two-color spaces were summed and achieved 91.57%segmentation ***,because different features of pests contribute differently to the classification of pest species,the study extracted multiple features(e.g.,color and shape features)in different color spaces for each segmented pest region to improve the recognition *** decision trees were used to form a strong ensemble learning classifier that used a majority voting mechanism and obtains 95.73%recognition *** proposed method is a feasible and effective way to process greenhouse pest *** system accurately recognized and counted pests in sticky trap images captured under real greenhouse conditions.

关键词： ensemble learning classifier greenhouse sticky trap automated pest recognition and counting HSI and Lab color spaces multiple color space features

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Feed intake prediction model for group fish using the MEA-BP neural network in intensive aquaculture

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Information Processing in Agriculture 2020年第2期7卷 261-271页

作者： Lan Chen xinting yang Chuanheng Sun Yizhong Wang Daming Xu Chao Zhou Beijing Research Center for Information Technology in Agriculture Beijing 100097China Tianjin University of Science and Technology Tianjin 300222China National Engineering Research Center for Information Technology in Agriculture Beijing 100097China National Engineering Laboratory for Agri-product Quality Traceability Beijing 100097China

In aquaculture,the accurate prediction of feed intake for group fish is considered to be crucial to any feeding *** studies mainly used mathematical statistics to establish the mapping relationship between feed intake and influencing *** result was easily influenced by subjective *** solve the above issues,this paper proposed a feed intake prediction model for group fish using the back-propagation neural network(BPNN)and mind evolutionary algorithm(MEA).Firstly,four factors,including water temperature,dissolved oxygen,the average fish weight and the number of fish were selected as the input of the BPNN ***,the initial weight and threshold of the BPNN were optimized by the MEA to improve the matching ***,the prediction model was achieved after *** results showed that the correlation coefficient between the predicted and measured values reached *** the root mean squared error,mean square error,mean absolute error,mean absolute percent error of the model was 6.89,47.53,6.17 and 0.04,*** addition,the proposed method also had the better nonlinear fitting ability than BPNN and *** using an intelligent optimization algorithm,the mapping relationship between fish intake and environmental factors was automatically established,thus avoiding the subjectivity of traditional ***,it can lay a theoretical foundation for the development of intelligent feeding equipment and meet the needs of the smart fishery.

关键词： BP neural network Feed intake prediction Group fish Mind evolutionary algorithm

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Agricultural cooperatives and the role of organisational models in new intelligent traceability systems and big data analysis

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International Journal of Agricultural and Biological Engineering 2017年第5期10卷 115-125页

作者： Cynthia Giagnocavo Fernando Bienvenido Li Ming Zhao Yurong Jorge Antonio Sanchez-Molina yang xinting Cátedra(Chair)Coexphal-UAL of Horticulture Cooperative Studies and Sustainable DevelopmentUniversity of AlmeriaSpain Department of Informatics University of AlmeriaAlmeriaSpain Beijing Research Center for Information Technology in Agriculture/National Engineering Research Center for Information Technology in Agriculture/National Engineering Laboratory for Agri-product Quality Traceability/Key Laboratory for Information Technologies in Agriculture Ministry of AgricultureChina College of Economics and Management Shandong Agricultural UniversityChina

Traceability systems are key to assuring food safety,creating a benefit for food supply chain ***,the appearance of new technologies such as IoT and Big Data Analysis leads to a new generation of more functional,but complex,traceability *** models based on cooperation of multiple small/medium size agents,for example of small/family farming cooperatives,play an important role in high standard agricultural production and commercialization *** function as both social and economic networks,with high social and economic impact in the rural *** case of Almeria as an example of this model was used to analyze its cooperative *** actual traceability systems in the Almeria model were studied,taking account of the different networked agents and their *** study includes two main parts:a)analysis of the net-chains that constitute the food supply chains and their different relationships,and b)actual *** next step studied how the net-chain model,including many diverse agents,may be applied to develop a new generation of traceability systems based of IoT and Big *** implies extending the special and functional scope of the actual systems and defining clear rules of exchange of the results of the Big Data Analysis,taking account of the adequate Privacy *** work analyses the current organisation as a base for a new generation of traceability systems considered in the European project IoF2020(Internet of the Food and Farm 2020).Some parallels between Almeria’s model and certain areas in China,mainly in the areas of Beijing-Tianjin-Hebei and Shandong were *** of the objectives of this work is to deepen the understanding of these similarities and analyze the possible adaptation of the results from Almeria to China.

关键词： food traceability system big data internet of things organisational structure business model agricultural cooperatives net-chains food supply chain

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Method for segmentation of overlapping fish images in aquaculture

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International Journal of Agricultural and Biological Engineering 2019年第6期12卷 135-142页

作者： Chao Zhou Kai Lin Daming Xu Jintao Liu Song Zhang Chuanheng Sun xinting yang Beijing Research Center for Information Technology in Agriculture Beijing 100097China National Engineering Research Center for Information Technology in Agriculture Beijing 100097China National Engineering Laboratory for Agri-product Quality Traceability Beijing 100097China Beijing Fisheries Research Institute Beijing 100068China Department of Computer Science University of AlmeriaAlmeria04120Spain

Individual fish segmentation is a prerequisite for feature extraction and object identification in any machine vision *** this paper,a method for segmentation of overlapping fish images in aquaculture was ***,the shape factor was used to determine whether an overlap exists in the ***,the corner points were extracted using the curvature scale space algorithm,and the skeleton obtained by the improved Zhang-Suen thinning ***,intersecting points were obtained,and the overlapped region was *** results show that the average error rate and average segmentation efficiency of this method was 10%and 90%,*** with the traditional watershed method,the separation point is accurate,and the segmentation accuracy is ***,the proposed method achieves better performance in segmentation accuracy and *** method can be applied to multi-target segmentation and fish behavior analysis systems,and it can effectively improve recognition precision.

关键词： aquaculture image processing overlapping segmentation corner detection improved Zhang-Suen algorithm

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Segmentation of thermal infrared images of cucumber leaves using K-means clustering for estimating leaf wetness duration

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International Journal of Agricultural and Biological Engineering 2020年第3期13卷 161-167页

作者： Dongmei Wen Aixin Ren Tao Ji Isabel Maria Flores-Parra xinting yang Ming Li Beijing Research Center for Information Technology in Agriculture/National Engineering Research Center for Information Technology in Agriculture/National Engineering Laboratory for Agri-product Quality Traceability/National Meteorological Service Center of Urban Agriculture Beijing 100097China College of Plant Protection China Agricultural UniversityBeijing 100193China Department of Sustainable Crop Production UniversitàCattolica del Sacro Cuore29122 PiacenzaItaly Department of Informatics University of AlmeriaAlmeriaCP04120Spain

Leaf wetness duration(LWD)is a critical parameter used to predict plant disease,but its determination under actual field conditions is a major *** this study,a method for determining LWD using thermal infrared imaging was developed and applied to cucumber plants grown in a solar *** images of the plant leaves were captured using an infrared scanning camera,and a leaf wetness area segmentation method consisting of two procedures was ***,a color space conversion was performed automatically by an image-processing ***,the K-means clustering algorithm was applied to enable the segmentation of the wetness area on the thermal ***,to enable overall thermal image analysis,an initial leaf wetness threshold(LWT)of 5%was defined(where wetness values higher than 5%indicated that the leaf was in a wet state).The results of comparative experiments conducted using thermal images of plant leaves captured using an infrared scanning camera and human visual observation indicated that the estimated LWD values were generally higher than the observed LWD values,because slight leaf wetness condensations were overlooked by the human eye but detected by the infrared scanning *** these differences were not found to be statistically significant in this study,the proposed method for determining LWD using thermal infrared imaging may provide a new LWD detection method for cucumber and other plants grown in solar greenhouses.

关键词： thermal imaging K-means clustering algorithm leaf wetness duration cucumber

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Development and test verification of air temperature model for Chinese solar and Spainish Almeria-type greenhouses

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International Journal of Agricultural and Biological Engineering 2017年第4期10卷 66-76页

作者： Jorge Antonio Sanchez-Molina Li Ming Francisco Rodriguez Jose Luis Guzman Wang Hui yang xinting The Automatic Control Robotics and Mechatronics Research GroupDepartment of InformaticsceiA3CIESOLUniversity of AlmeriaAlmeria 04120Spain Beijing Research Center for Information Technology in Agriculture/National Engineering Research Center for Information Technology in Agriculture/National Engineering Laboratory for Agri-product Quality Traceability/Key Laboratory for Information Technologies in Agriculture Ministry of AgricultureBeijing 100097China

Growth can be defined as an increment in biomass or an increment in weight or height of the organs of the plant influenced by physiological *** of these processes have their limits genetically determined,but climate and irrigation play an important *** of its importance,microclimate has been extensively studied in the modeling as a surrounding condition which is imposed by the exterior *** main objective of this work was to develop a temperature model based on the energy balance dynamics at two different greenhouse locations-South-eastern Spain and Northern China,and the traditional structures of Chinese solar greenhouse and Almería-type multi-span greenhouse were taken into *** final model was developed by combining the external conditions,the actuator influence and the crop growth,where the temperature is influenced by soil,crop,cover,actuators,back wall and greenhouse *** model took into account the energy lost by convective and conductive fluxes,as well as the energy supplied by solar radiation and heating *** soil and the back wall are the main media for energy *** temperature dynamic was determined by a physical model,which considered the energy balance from a holistic point of view-as a sub-model for a customizable interface among the external climate,the plant and the greenhouse *** influences of different subsystems included in the temperature model were analyzed and *** results showed a high R^(2)value of 0.94 for Beijing and 0.95 for Almeria,and the average error was low,of which the MAE and RMSE were 0.71 and 1.365 for Almeria and 0.62 and 1.102 for Beijing,***,the model can be considered as a powerful tool for control design purposes in microclimate systems.

关键词： air temperature model Chinese solar greenhouse Spanish Almería-type greenhouse energy balance dynamics microclimate

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Nondestructive determination of the freshness change in bighead carp heads under variable temperatures by using excitation-emission matrix fl uorescence and back-propagation neural networks

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Journal of Future Foods 2022年第2期2卷 160-166页

作者： Ce Shi Zengtao Ji xinting yang Zhixin Jia Ruize Dong Ge Shi Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China National Engineering Research Center for Information Technology in Agriculture Beijing Academy of Agricultural and Forestry SciencesBeijing 100097China National Engineering Laboratory for Agri-product Quality Traceability Beijing Academy of Agricultural and Forestry SciencesBeijing 100097China Key Laboratory of Cold Chain Logistics Technology for Agro-product Ministry of Agriculture and Rural AffairsBeijing 100097China

This study established back-propagation neural networks(BPNNs)for evaluating the freshness of bighead carp(Hypophthalmichthys nobilis)heads during chilled storage via fluorescence spectroscopy using an excitation-emission matrix(EEM).The total volatile basic nitrogen(TVB-N)and total aerobic count(TAC)of fish increased obviously during storage at 0,4,8,12,and 16°C,while sensory scores decreased with increasing storage *** EEM fluorescence intensity was measured,and its change was correlated with the freshness indicators of the *** characteristic components of EEM data were extracted by parallel factor analysis,and two freshness indicators were used to construct the EEM-BPNNs *** results demonstrated that the relative errors of the EEM-BPNNs model for TVB-N and TAC were less than 14%.This result indicated that the EEM-BPNNs model could determine the freshness of fish in cold chains in a rapid and nondestructive way.

关键词： Excitation-emission matrix Freshness Back-propagation neural networks Parallel factor analysis Chilled storage

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Image processing methods to evaluate tomato and zucchini damage in post-harvest stages

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International Journal of Agricultural and Biological Engineering 2017年第5期10卷 126-133页

作者： José Antonio Alvarez-Bermejo Cynthia Giagnocavo Li Ming Encarnación Castillo Morales Diego P.Morales Santos yang xinting Department of Informatics CeiA3University of AlmeriaAlmeriaCP 04120Spain Cátedra(Chair)Coexphal-UAL of Horticulture Cooperative Studies and Sustainable DevelopmentUniversity of AlmeríaSpain Beijing Research Center for Information Technology in Agriculture/National Engineering Research Center for Information Technology in Agriculture/National Engineering Laboratory for Agri-product Quality Traceability/Key Laboratory for Information Technologies in Agriculture Ministry of AgricultureBeijing 100097China Department of Electronics and Computer Technology Facultad de CienciasGranadaCP 18071Spain

Through the supply chain,the quality or quality change of the products can generate important *** quality control in some steps is made manually that supposes a high level of subjectivity,controlling the quality and its evolution using automatic systems can suppose a reduction of the *** some automatic image analysis techniques in the case of tomatoes and zucchini is the main objective of this *** steps in the supply chain are considered,the feeding of the raw products into the handling chain(because low quality generates a reduction of the chain productivity)and the cool storage of the processed products(as the value at the market is reduced).It was proposed to analyze the incoming products at the head the processing line using CCD cameras to detect low quality and/or dirty products(corresponding to specific farmers/suppliers,it should be asked to improve to maintain the productivity of the line).The second stage is analyzing the evolution of the products along the cool chain(storage and transport),the use of an App developed to be use under Android was proposed to substitute the“visual”evaluation used in *** algorithms used,including stages of pre-treatment,segmentation,analysis and presentation of the results take account of the short time available and the limited capacity of the *** performance techniques were applied to the homography stage to discard some of the images,resulting in better *** threads and renderscript kernels were created to parallelize the methods used on the resulting images being able to inspect faster the *** proposed method achieves success rates comparable to,and improving,the expert inspection.

关键词： image processing color space smartphone efficient stitching homography controlled supervision artificial vision embedded parallel processing injury assessment traceability post-harvest control feature detection

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