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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1892-1894页

作者： Tao Chen Ruiyuan Zhao Jian Chen Zichao Zhang College of Engineering China Agricultural UniversityBeijing 100083China key laboratory of smart agricultural technology in Tropical South China Ministry of Agriculture and Rural AffairsGuangzhou 510642China

Dear Editor,This letter proposes a fuzzy indirect iterative learning(FIIL)active disturbance rejection control(ADRC)scheme to address the impact of uncertain factors of plant-protection unmanned ground vehicle(UGV),in which ADRC is a data-driven model-free control algorithm that only relies on the input and output data of the *** on the established nonlinear time-varying dynamic model including dynamic load(medicine box),the FIIL technology is adopted to turn the bandwidth and control channel gain online,in which the fuzzy logic system is used to update the gain parameters of iterative learning in real *** and experiment show the FIIL-ADRC scheme has better control performance.

关键词： Active scheme iterative

Cow-YOLO:Automatic cow mounting detection based on non-local CSPDarknet53 and multiscale neck

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International Journal of agricultural and Biological Engineering 2024年第3期17卷 193-202页

作者： De Li Junhao Wang Zhe Zhang Baisheng Dai Kaixuan Zhao Weizheng Shen Yanling Yin Yang Li College of Electrical Engineering and Information Northeast Agricultural UniversityHarbin 150030China College of agricultural Equipment Engineering Henan University of Science and TechnologyLuoyang 471023HenanChina College of Animal Sciences and technology Northeast Agriculture UniversityHarbin 150030China key laboratory of northeast smart agricultural technology of ministry of agriculture and rural affairs Northeast Agricultural UniversityHarbin 150030China

Cows mounting behavior is a significant manifestation of estrus in *** timely detection of cows mounting behavior can make cows conceive in time,thereby improving milk production of cows and economic benefits of the *** methods of mounting behavior detection are difficult to achieve precise detection under occlusion and severe scale change environments and meet real-time ***,this study proposed a Cow-YOLO model to detect cows mounting *** meet the needs of real-time performance,YOLOv5s model is used as the baseline *** order to solve the problem of difficult detection of cows mounting behavior in an occluded environment,the CSPDarknet53 of YOLOv5s is replaced with Non-local CSPDarknet53,which enables the network to obtain global information and improves the model’s ability to detect the mounting ***,the neck of YOLOv5s is redesigned to Multiscale Neck,reinforcing the multi-scale feature fusion capability of model to solve difficulty detection under dramatic scale ***,to further increase the detection accuracy,the Coordinate Attention Head is integrated into ***,these improvements form a novel cow mounting detection model called Cow-YOLO and make Cow-YOLO more suitable for cows mounting behavior detection in occluded and drastic scale changes ***-YOLO achieved a precision of 99.7%,a recall of 99.5%,a mean average precision of 99.5%,and a detection speed of 156.3 f/s on the test *** with existing detection methods of cows mounting behavior,Cow-YOLO achieved higher detection accuracy and faster detection speed in an occluded and drastic scale-change ***-YOLO can assist ranch breeders in achieving real-time monitoring of cows estrus,enhancing ranch economic efficiency.

关键词： cows mounting automatic detection Cow-YOLO computer vision CSPDarknet53 multiscale neck

Feasible strategy for the nitrogen fixation and humification quality improvement by spent mushroom substrate as conditioning agent

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International Journal of agricultural and Biological Engineering 2024年第3期17卷 235-240页

作者： Shuang Ai Yuanbei Zhang Guoxiang Zheng College of Engineering Northeast Agricultural UniversityHarbin 150030China key laboratory of Swine Facilities Engineering Ministry of Agriculture and Rural AffairsHarbin 50030China Heilongjiang key laboratory of technology and Equipment for the Utilization of agricultural Renewable Resources Harbin 150030China

There is an overlooked problem which increasing microbial abundance while reducing nitrogen loss during *** study investigated the viability of spent mushroom substrate(SMS)as conditioners in the aerobic composting of kitchen waste(KW)with cattle manure(CM).The variation of temperature,pH,C/N,organic matter,cellulose,hemicelluloses,germination index(GI),and microflora structure were explored to evaluate the potential in accelerating maturity and nitrogen fixation by SMS *** results showed that the addition of SMS accelerated the heating rate,prolonged the high temperature time,and decreased organic matter,hemicellulose,and cellulose by 17.49%,23.61%,and 18.62%,*** GI reached 105.86%with SMS addition,while 74.17%was found in control treatment after *** changed the microbial community composition and increased the species ***,Clostridium XI and Azomonas were dominant bacteria,which increased the retention of nitrogen,promoted organic matter degradation and reduce compost *** study can provide a feasible strategy for nitrogen fixation in the field of organic waste recycling.

关键词： spent mushroom substrate conditioning agent nitrogen fixation humification quality aerobic composting

Simulation and forecasting of fishery weather based on statistical machine learning

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Information Processing in agriculture 2024年第1期11卷 127-142页

作者： Xueqian Fu Chunyu Zhang Fuhao Chang Lingling Han Xiaolong Zhao Zhengjie Wang Qiaoyu Ma National Innovation Center for Digital Fishery China Agricultural UniversityBeijingChina key laboratory of smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural AffairsBeijingChina Beijing Engineering and technology Research Center for Internet of Things in agriculture BeijingChina College of Information and Electrical Engineering China Agricultural UniversityBeijingChina

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 *** 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 *** 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 ***,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 *** also illustrate the main technical means and principles of SML ***,we summarize the most advanced SML fields and provide an outlook on their application value in the field of fishery weather.

关键词： Fishery weather Statistical machine learning Weather generation Weather forecasting Extreme weather warning

Characterization of the pheromone receptors in Mythimna loreyi reveals the differentiation of sex pheromone recognition in Mythimna species

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Insect Science 2024年第1期31卷 173-185页

作者： Chan Wang Lei Liu Tian-Yu Huang Yu Zhang Yang Liu Gui-Rong Wang State key laboratory for Biology of Plant Diseases and Insect Pests Institute of Plant ProtectionChinese Academy of Agricultural SciencesBeijingChina Guangdong laboratory of Lingnan Modern agriculture ShenzhenGenome Analysis Laboratory of the Ministry of AgricultureAgricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhenChina School of Forestry Northeast Forestry UniversityHarbinChina key laboratory of Biohazard Monitoring Green Prevention and Control for Artificial GrasslandMinistry of Agriculture and Rural AffairsInstitute of Grassland Research of Chinese Academy of Agricultural SciencesInner Mongolia HohhotChina

Pheromone receptors(PRs)are key proteins in the molecular mechanism of pheromone recognition,and exploring the functional differentiation of PRs between closely related species helps to understand the evolution of moth mating *** components of the agricultural pest Mythimna loreyi have turned into(Z)-9-tetradecen-1-yl acetate(Z9-14:OAc),(Z)-7-dodecen-1-yl acetate(Z7-12:OAc),and(Z)-11-hexadecen-1-yl acetate,while the composition differs from that of *** in the genus *** understand the molecular mechanism of pheromone recognition,we sequenced and analyzed antennal transcriptomes to identify 62 odorant receptor(OR)*** expression levels of all putative ORs were analyzed using differentially expressed gene *** candidate PRs were quantified and functionally characterized in the Xenopus oocytes ***6 and MlorPR3 were determined to be the receptors of major and minor components Z9-14:OAc and Z7-12:***1 and female antennae(FA)-biased MlorPR5 both possessed the ability to detect pheromones of sympatric species,including(Z,E)-9,12-tetradecadien-1-ol,(Z)-9-tetradecen-1-ol,and(Z)-*** on the comparison of PR functions between *** and ***,we analyzed the differentiation of pheromone recognition mechanisms during the evolution of the mating systems of 2 Mythimna species.

关键词： antennal transcriptome Mythimna loreyi pheromone receptor sex pheromone Xenopus oocyte

Crop pest image recognition based on the improved ViT method Author links open overlay panel

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Information Processing in agriculture 2024年第2期11卷 249-259页

作者： Xueqian Fu Qiaoyu Ma Feifei Yang Chunyu Zhang Xiaolong Zhao Fuhao Chang Lingling Han College of Information and Electrical Engineering China Agricultural UniversityBeijingChina National Innovation Center for Digital Fishery China Agricultural UniversityBeijingChina key laboratory of smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural AffairsBeijingChina Beijing Engineering and technology Research Center for Internet of Things in agriculture BeijingChina

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 ***,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 *** recognition method based on pattern recognition and deep learning can automatically fit image features,and use features to classify and predict *** study introduced the improved Vision Transformer(ViT)method for crop pest image *** them,the region with the most obvious features can be effectively selected by block *** self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion *** the experiment,data with 7 classes of examples are used for *** 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.

关键词： Crop diseases recognition Fruit tree leaf disease Vision Transformer Deep learning Computer Vision

A state of art review on time series forecasting with machine learning for environmental parameters in agricultural greenhouses

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Information Processing in agriculture 2024年第2期11卷 143-162页

作者： Gedi Liu keyang Zhong Huilin Li Tao Chen Yang Wang National Innovation Center for Digital Fishery China Agricultural University100083 BeijingChina key laboratory of smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural AffairsChina Agricultural University100083 BeijingChina Beijing Engineering and technology Research Center for Internet of Things in agriculture China Agricultural University100083 BeijingChina College of Information and Electrical Engineering China Agricultural University100083 BeijingChina Department of Chemical and Process Engineering University of SurreyGU27XH GuildfordUnited Kingdom

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 *** modeling based on machine learning methods,e.g.,intelligent time series prediction modeling,is a popular and suitable way to solve the above *** 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 *** historical process of time series model application from the use of data and information strategies was first ***,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 ***,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.

关键词： Horticultural greenhouse environment Time series algorithms Prediction Deep neural networks

A CRISPR/Cas12a-based platform for rapid on-site bovine viral diarrhea virus diagnostics

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Journal of Integrative agriculture 2024年第8期23卷 2872-2876页

作者： Meixi Wang Jitao Chang Yuxin Han Chaonan Wang Songkang Qin Jun Wang Lulu Zhang Yuanmao Zhu Fei Xue Fang Wang Hongliang Chai Yulong Wang Xinjie Wang Xin Yin State key laboratory for Animal Disease Control and Prevention Harbin Veterinary Research InstituteChinese Academy of Agricultural SciencesHarbin 150069China Shenzhen Branch Guangdong Laboratory of Lingnan Modern AgricultureGenome Analysis Laboratory of the Ministry of Agriculture and Rural AffairsAgricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhen 518000China College of Wildlife and Protected Area Northeast Forestry UniversityHarbin 150040China Institute of Western agriculture Chinese Academy of Agricultural SciencesChangji 831100China

Bovine viral diarrhea virus(BVDV)is a positive-sense single-stranded RNA virus,belonging to the genus Pestivirus in the Flaviviridae family(Riitho et al.2020).Currently,BVDV is divided into 3 main genotypes,BVDV-1,BVD... 详细信息

关键词： BVDV CRISPR/Cas diarrhea

Quantifying the agreement and accuracy characteristics of four satellite-based LULC products for cropland classification in China

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Journal of Integrative agriculture 2024年第1期23卷 283-297页

作者： Jie Xue Xianglin Zhang Songchao Chen Bifeng Hu Nan Wang Zhou Shi Department of Land Management Zhejiang UniversityHangzhou 310058China Institute of Applied Remote Sensing and Information technology College of Environmental and Resource SciencesZhejiang UniversityHangzhou 310058China ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang UniversityHangzhou 311215China Department of Land Resource Management School of Public Finance and Public AdministrationJiangxi University of Finance and EconomicsNanchang 330013China key laboratory of Data Science in Finance and Economics Jiangxi University of Finance and EconomicsNanchang 330013China key laboratory of Spectroscopy Sensing Ministry of Agriculture and Rural AffairsHangzhou 310058China

Various land use and land cover(LULC)products have been produced over the past decade with the development of remote sensing *** the differences in LULC classification schemes,there is a lack of research on assessing the accuracy of their application to croplands in a unified ***,this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products(i.e.,MCD12Q1V6,GlobCover2009,FROM-GLC and GlobeLand30)based on the harmonised FAO criterion,and quantified the relationships between four factors(i.e.,slope,elevation,field size and crop system)and cropland classification *** validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency,with overall accuracies of 94.90 and 93.52%,*** FROMGLC showed the worst performance,with an overall accuracy of 83.17%.Overlaying the cropland generated by the four global LULC products,we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification,*** consistency was mainly observed in the northeast China Plain,the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain,*** contrast,low consistency was detected primarily on the eastern edge of the northern and semiarid region,the Yunnan-Guizhou Plateau and southern *** size was the most important factor for mapping *** area accuracy,compared with China Statistical Yearbook data at the provincial scale,the accuracies of different products in descending order were:GlobeLand30,FROM-GLC,MCD12Q1,and *** cropland classification schemes mainly caused large area deviations among the four products,and they also resulted in the different ranks of spatial accuracy and area accuracy among the four *** results can provide valuable suggestions for selecting cropland products at the national or provinc

关键词： global LULC products cropland mapping accuracy evaluation food security China

Maize leaf disease identification using deep transfer convolutional neural networks

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International Journal of agricultural and Biological Engineering 2022年第5期15卷 187-195,F0004页

作者： Zheng Ma Yue Wang Tengsheng Zhang Hongguang Wang Yingjiang Jia Rui Gao Zhongbin Su Institute of Electrical and Information Northeast Agricultural UniversityHarbin 150030China agricultural Products and Veterinary Drug Feed Technical Identification Station Department of Agriculture and Rural Affairs of Heilongjiang ProvinceHarbin 150090China key laboratory of northeast smart agricultural technology Ministry of Agriculture and Rural AffairsHeilongjiang ProvinceHarbin 150030China

Gray leaf spot,common rust,and northern leaf blight are three common maize leaf diseases that cause great economic losses to the worldwide maize *** and accurate disease identification can reduce economic losses,pesticide usage,and ensure maize yield and food *** learning methods,represented by convolutional neural networks(CNNs),provide accurate,effective,and automatic diagnosis on server platforms when enormous training data is *** by dataset scale and application scenarios,CNNs are difficult to identify small-scale data sets on mobile terminals,while the lightweight networks,designed for the mobile terminal,achieve a better balance between efficiency and *** paper proposes a two-staged deep-transfer learning method to identify maize leaf diseases in the *** the deep learning period,8 deep and 4 lightweight CNN models were trained and compared on the Plant Village dataset,and ResNet and MobileNet achieved test accuracy of 99.48%and 98.69%respectively,which were then migrated onto the field maize leave disease dataset collected on mobile *** using layer-freezing and fine-tuning strategies on ResNet and MobileNet,fine-tuned MobileNet achieved the best accuracy of 99.11%.Results confirmed that disease identification performance from lightweight CNNs was not inferior to that of deep CNNs and transfer learning training efficiency was higher when lacking training ***,the smaller gaps between source and target domains,the better the identification performance for transfer *** study provides an application example for maize disease identification in the field using deep-transfer learning and provides a theoretical basis for intelligent maize leaf disease identification from images captured with mobile devices.

关键词： maize leaf disease deep learning transfer learning convolutional neural networks