Aging Mongolian pine plantations face high risks of drought-induced growth decline:evidence from both individual tree and forest stand measurements

Discerning vulnerability differences among different aged trees to drought-driven growth decline or to mortality is critical to implement age-specific countermeasures for forest management in water-limited areas.An important species for afforestation in dry environments of northern China,Mongolian pine(Pinus sylvestris var.mongolica Litv.)has recently exhibited growth decline and dieback on many sites,particularly pronounced in old-growth plantations.However,changes in response to drought stress by this species with age as well as the underlying mechanisms are poorly understood.In this study,tree-ring data and remotely sensed vegetation data were combined to investigate variations in growth at individual tree and stand scales for young(9-13 years)and aging(35-52 years)plantations of Mongolian pine in a water-limited area of northern China.A recent decline in tree-ring width in the older plantation also had lower values in satellited-derived normalized difference vegetation indices and normalized difference water indices relative to the younger plantations.In addition,all measured growth-related metrics were strongly correlated with the self-calibrating Palmer drought severity index during the growing season in the older plantation.Sensitivity of growth to drought of the older plantation might be attributed to more severe hydraulic limitations,as reflected by their lower sapwood-and leaf-specific hydraulic conductivities.Our study presents a comprehensive view on changes of growth with age by integrating multiple methods and provides an explanation from the perspective of plant hydraulics for growth decline with age.The results indicate that old-growth Mongolian pine plantations in water-limited environments may face increased growth declines under the context of climate warming and drying.

An Analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion

The learning status of learners directly affects the quality of learning.Compared with offline teachers,it is difficult for online teachers to capture the learning status of students in the whole class,and it is even more difficult to continue to pay attention to studentswhile teaching.Therefore,this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion.Specifically,a facial expression recognition model and an eye state recognition model are constructed to detect students’emotions and fatigue,respectively.By integrating the detected data with the homework test score data after online learning,an analysis model of students’online learning status is constructed.According to the PAD model,the learning state is expressed as three dimensions of students’understanding,engagement and interest,and then analyzed from multiple perspectives.Finally,the proposed model is applied to actual teaching,and procedural analysis of 5 different types of online classroom learners is carried out,and the validity of the model is verified by comparing with the results of the manual analysis.

A PERT-BiLSTM-Att Model for Online Public Opinion Text Sentiment Analysis

As an essential category of public event management and control,sentiment analysis of online public opinion text plays a vital role in public opinion early warning,network rumor management,and netizens’person-ality portraits under massive public opinion data.The traditional sentiment analysis model is not sensitive to the location information of words,it is difficult to solve the problem of polysemy,and the learning representation ability of long and short sentences is very different,which leads to the low accuracy of sentiment classification.This paper proposes a sentiment analysis model PERT-BiLSTM-Att for public opinion text based on the pre-training model of the disordered language model,bidirectional long-term and short-term memory network and attention mechanism.The model first uses the PERT model pre-trained from the lexical location information of a large amount of corpus to process the text data and obtain the dynamic feature representation of the text.Then the semantic features are input into BiLSTM to learn context sequence information and enhance the model’s ability to represent long sequences.Finally,the attention mechanism is used to focus on the words that contribute more to the overall emotional tendency to make up for the lack of short text representation ability of the traditional model,and then the classification results are output through the fully connected network.The experimental results show that the classification accuracy of the model on NLPCC14 and weibo_senti_100k public data sets reach 88.56%and 97.05%,respectively,and the accuracy reaches 95.95%on the data set MDC22 composed of Meituan,Dianping and Ctrip comment.It proves that the model has a good effect on sentiment analysis of online public opinion texts on different platforms.The experimental results on different datasets verify the model’s effectiveness in applying sentiment analysis of texts.At the same time,the model has a strong generalization ability and can achieve good results for sentiment analysis of datasets in different fields.

Research Progress and Prospect of Intelligent Tea Garden Technology Application

The development and application of internet plus modern tea industry technology is more and more extensive.As an important part of the development process of tea industry,intelligent tea garden plays an important role in the development of the whole industry.At present,intelligent tea garden technology is widely used in many fields such as intelligent monitoring,water and fertilizer integration,green prevention and control,quality and safety traceability.In this paper,the application of intelligent tea garden technology in tea gardens was reviewed.On this basis,the development trend of new information technology and tea industry was prospected,in order to provide some reference and thinking for the innovative research of new technology in tea garden in the future.

The cooling effect of crushed rock structures on permafrost under an embankment

Based on the analysis and comparison of soil temperature, thermal regime and permafrost table under the experimental embankment of crushed rock structures in Beiluhe, results show that crushed rock structures provide an extensive cooling effect, which produces a rising permafrost table and decreasing soil temperatures. The rise of the permafrost table under the embankment ranges from an increase of 1.08 m to 1.67 m, with an average of 1.27 m from 2004 to 2007. Mean annual soil temperatures under the crushed rock layer embankment decreased significantly from 2005 to 2007, with average decreases of ?1.03 °C at the depth of 0.5 m, ?1.14 °C at the depth of 1.5 m, and ?0.5 °C at the depth of 5 m. During this period, mean annual soil temperatures under the crushed rock cover embankment showed a slight decrease at shallow depths, with an average decrease of ?0.2 °C at the depth of 0.5 m and 1.5 m, but a slight rise at the depth of 5 m. After the crushed rock structures were closed or crammed with sand, the cooling effect of the crushed rock layer embankment was greatly reduced and that of the crushed rock cover embankment was just slightly reduced.

TECMH:Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

In recent years,cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low storage.Cross-modal retrieval technology can be applied to search engines,crossmodalmedical processing,etc.The existing main method is to use amulti-label matching paradigm to finish the retrieval tasks.However,such methods do not use fine-grained information in the multi-modal data,which may lead to suboptimal results.To avoid cross-modal matching turning into label matching,this paper proposes an end-to-end fine-grained cross-modal hash retrieval method,which can focus more on the fine-grained semantic information of multi-modal data.First,the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for processing.Second,this method uses the inference capabilities of the transformer encoder to generate global fine-grained features.Finally,in order to better judge the effect of the fine-grained model,this paper uses the datasets in the image text matching field instead of the traditional label-matching datasets.This article experiment on Microsoft COCO(MS-COCO)and Flickr30K datasets and compare it with the previous classicalmethods.The experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.

ViT2CMH:Vision Transformer Cross-Modal Hashing for Fine-Grained Vision-Text Retrieval

In recent years,the development of deep learning has further improved hash retrieval technology.Most of the existing hashing methods currently use Convolutional Neural Networks(CNNs)and Recurrent Neural Networks(RNNs)to process image and text information,respectively.This makes images or texts subject to local constraints,and inherent label matching cannot capture finegrained information,often leading to suboptimal results.Driven by the development of the transformer model,we propose a framework called ViT2CMH mainly based on the Vision Transformer to handle deep Cross-modal Hashing tasks rather than CNNs or RNNs.Specifically,we use a BERT network to extract text features and use the vision transformer as the image network of the model.Finally,the features are transformed into hash codes for efficient and fast retrieval.We conduct extensive experiments on Microsoft COCO(MS-COCO)and Flickr30K,comparing with baselines of some hashing methods and image-text matching methods,showing that our method has better performance.

Online diagnosis platform for tomato seedling diseases in greenhouse production

The facility-based production method is an important stage in the development of modern agriculture,lifting natural light and temperature restrictions and helping to improve agricultural production efficiency.To address the problems of difficulty and low accuracy in detecting pests and diseases in the dense production environment of tomato facilities,an online diagnosis platform for tomato plant diseases based on deep learning and cluster fusion was proposed by collecting images of eight major prevalent pests and diseases during the growing period of tomatoes in a facility-based environment.The diagnostic platform consists of three main parts:pest and disease information detection,clustering and decision-making of detection results,and platform diagnostic display.Firstly,based on the You Only Look Once(YOLO)algorithm,the key information of the disease was extracted by adding attention module(CBAM),multi-scale feature fusion was performed using weighted bi-directional feature pyramid network(BiFPN),and the overall construction was designed to be compressed and lightweight;Secondly,the k-means clustering algorithm is used to fuse with the deep learning results to output pest identification decision values to further improve the accuracy of identification applications;Finally,a detection platform was designed and developed using Python,including the front-end,back-end,and database of the system to realize online diagnosis and interaction of tomato plant pests and diseases.The experiment shows that the algorithm detects tomato plant diseases and insect pests with mAP(mean Average Precision)of 92.7%,weights of 12.8 Megabyte(M),inference time of 33.6 ms.Compared with the current mainstream single-stage detection series algorithms,the improved algorithm model has achieved better performance;The accuracy rate of the platform diagnosis output pests and diseases information of 91.2%for images and 95.2%for videos.It is a great significance to tomato pest control research and the development of smart agriculture.

Design and test of 2ZYM-2 potted vegetable seedlings transplanting machine

In order to improve the efficiency and quality of transplanting vegetables in dry land,based on the seedling technology,transplanting effect and analysis of the planting process,a potted vegetable seedlings transplanting machine was designed.It mainly comprised rotary disc type feeding mechanism,five-bar duckbill type planting mechanism(simulated duckbill mechanism,disc cam,connecting rod,crank,fork,cable)and power transmission system.Based on the physical parameters of the seedlings and design requirements,it was determined that the diameter of the duckbill was D=90 mm,the opening and closing angle was 25°,and the taper angle was 17°.A test bench with adjustable parameters was built by analyzing the structure of the planting mechanism and the motion of the working process.The digital speckle technique was used to optimize the parameters,so that the length of the crankshaftΙwas S1=100 mm,the length of the crankshaftΙΙwas S2=80 mm,the length of the connecting rodΙwas S3=140 mm,the length of the connecting rodΙΙwas S4=260 mm,the length of rod to connecting the rack was S6=314 mm,and the height of planting track was H=450 mm.According to the above parameters and the control requirements of the duckbill mechanism,the cam stroke was determined to be S=15 mm.And the initial phase difference between the two cams was 180°.The experiment was carried out with pepper seedlings as transplanting objects.The results showed that when the planting frequency was 50-70 plants/min,the seedling upright rate was 93%-91.1%,the planting depth qualified rate was 96%-92%.The leakage rate was 0-0.26%,the variation coefficient of plant spacing was 0.37%-0.67%,the injury rate was 0%,and the mechanical damage degree of the mining surface was 2.43-3.77 mm/m2.The machine can effectively improve the quality of transplanting,which can meet the production needs and design requirements of the mechanism.

Development of a PVDF sensor for potted seedling clamping force of vegetable transplanting

Aiming at the problems of loose bowl,nutritive bowl damage and planting leakage caused by lack of perception and detection of seedling clamping force in vegetable transplanting,a semi-micro-column structure sensor for measuring potted seedling clamping force was developed based on polyvinylidene fluoride(PVDF)piezoelectric film.The diameter of the semi-micro-column is 4 mm.The size of the force probe is 14 mm×30 mm,the encapsulation position of the force probe is 25 mm away from the tip of the seedling claw.The hardware circuit for sensor signal acquisition including pre-amplification module,power frequency notch module,low-pass filter module,processor module and power module was designed.The circuit completes charge-voltage conversion,clamping force signal amplification,power frequency signal elimination,vibration and noise elimination,and ensures the accurate acquisition of clamping force signal.In order to verify the sensor performance,sensor calibration tests and indoor experiments were carried out respectively.The calibration tests showed that the sensitivity of the clamping force sensor was 0.5264 V/N,the linearity was 4.74%,the accuracy was 6.51%,the hysteresis was 3.63%,and the range was 8 N under the impact of different waveforms and frequencies,which fully meet the accuracy requirements of the clamping force detection during transplanting.Indoor experiments showed that the clamping force sensor had good stability and adaptability under different seedling frequencies.The sensor can provide useful reference for the perception and detection of seedling clamping force and the feedback regulation of seedling clamping force.

Design and experiment of intelligent sorting and transplanting system for healthy vegetable seedlings

Healthy vegetable seedlings are surviving seedlings with good biological characteristics.Selective planting of healthy seedlings in the mechanized transplanting process can effectively avoid the reduction in yield caused by missed planting.Aiming at the current transplanting machinery that cannot achieve the selective planting of healthy seedlings,a healthy seedling intelligent sorting and transplanting system was proposed.The system consisted of a seedling delivery mechanism,sorting mechanism,photoelectric sensor,image sensor,PLC control system,and computer control system.It can realize automatic transmission of seedling trays,automatically identify the information of healthy seedlings in the trays and selectively transplant them.Also it can reduce the missed planting rate caused by the poor quality of plug seedlings after planting and the lack of seedlings in the hole.A sorting test of plug seedlings was carried out for the age-appropriate pepper plug seedlings cultivated in the factory.The results showed that the system had an average recognition accuracy rate of 89.14%and an average sorting success rate of 93.20%in the process of sorting suitable age pepper plug seedlings.The whole system can identify,sort and transplant the plug seedlings of appropriate age according to healthy information,and effectively avoid missing planting.This research can provide technical support for the intelligent upgrade of transplanting equipment.

Design and test of 2ZLX-2 transplanting machine for oil peony

In order to improve the quality of oil peony transplanting,based on the characteristics of peony seedlings and the transplanting effect,the transplanting process was analyzed.A peony seedling transplanting machine was designed,which was mainly composed of a chain-clamp type planting mechanism(clamp,transmission chain,sprocket),slideway,profiling wheels,trencher,the power transmission system and rack.Through the movement analysis of the planting mechanism and its operation process,its structural parameters were optimized.The effective length of the clamp was determined as L_(1)=235 mm,the adjustment range was 235-285 mm,and the height of the planting trajectory was H=340 mm.Based on the physical characteristics and mechanical characteristics of the peony seedlings,the parameters of the slideway and the putter were determined.The effective length of the slideway was determined as L_(2)=420 cm,the width T_(2)=105 mm,the distance between the slideways S=45 mm.The initial angle difference of the putter wasθ=13°,and the putter rotation angleβ=37°,putter height ratioε=2.Based on the above parameters and design requirements,the radius of the planting sprocket R_(1)=42 mm,R_(2)=80 mm.“Feng Dan”peony seedlings were used as transplantation test objects.The results showed that:when the planting frequency was 60-75 plants/min,the upright rate was 89.2%-92.8%,the leakage rate was 0-0.28%,the injury rate of seedlings was 0,and the qualified rate of planting depth was 91.7%-95.3%,the variation coefficient of plant spacing was 0.25%-0.54%.This machine can effectively improve the quality and efficiency of transplanting,meeting the requirements of mechanical design and production needs.

Obstacle avoidance transplanting method on Kinect visual processing

In modern facility agriculture,to improve the quality and efficiency of transplanting,the application of transplanting robots based on visual processing is becoming more and more widespread.In order to reduce the damage to plants during the transplanting process and reduce the damage rate of plant stems,leaves and substrates,a transplanting method based on Kinect visual processing combined with an inclined transplanting manipulator was proposed.In the research,the Kinect visual processing was used to obtain and process the seedling height information and leaf edge information,and the working coordinate system of the transplanting manipulator was established and applied to plan the obstacle avoidance path.Combined with the oblique manipulator,the obstacle avoidance transplanting method was proposed.Through the structural design and force analysis of the seedling transplanting device,the key parameters that affect the transplanting quality were obtained,and the optimal transplanting performance parameters were obtained through experiments.In the experiment,with the aid of the Kinect vision processing system,the designed obstacle avoidance transplanting manipulator had a leaf damage degree of 4.70%,a stem bending rate of 16.67%,substrate integrity of 83.45%and a transplanting quality parameter of 87.36%.The time for a single seedling transplanting was(8.32±0.40)s.The experiment result proves that the obstacle avoidance transplanting method based on Kinect visual processing can effectively reduce the damage to seedlings when ensuring the transplanting efficiency.