Abstract:Smart agriculture is not only an important way to achieve agricultural and rural modernization，but a key lever to comprehensively promote rural revitalization as well.This article reviews the policy evolution of smart agriculture development in China in the context of rural revitalization.The transmission mechanisms of smart agriculture development empowering rural revitalization are explained.The key issues in the current development of smart agriculture are analyzed. Suggestions for path optimization are proposed based on the analyses mentioned above.Results showed that smart agriculture mainly empowered rural revitalization from five aspects including stimulating industrial dynamics，helping talent cultivation，enhancing cultural confidence，improving ecological environment，and optimizing the governance of grassroots.At present，the development of smart agriculture in China still faces key issues including imperfect policies with mid-term of long-term goal，shortcomings in key core technologies，unsound guarantee mechanisms of investment，low levels of data collection and management and utilization，and weak willingness of business managers to participate. In order to better play the role of smart agriculture in promoting rural revitalization，this article proposes corresponding countermeasures and suggestions including formulating mid-term and long-term refined policy planning，winning the battle for key core technologies，improving guarantee mechanisms of investment，uplifting the level of data collection and management and utilization，and building a team of talent with high-quality.

Abstract:Smart agriculture offers new insights for reducing emissions and increasing carbon sinks in agriculture， and contributes to the carbon peaking and carbon neutrality. Based on clarifying the connotation and characteristics of smart agriculture under the goals of carbon peaking and carbon neutrality， this article analyzes the problems and challenges faced by the development of smart agriculture， and proposes countermeasures and suggestions. The results showed that the development of smart agriculture under the goals of carbon peaking and carbon neutrality should emphasize the philosophy of leading low-carbon with intelligence and driving intelligence with low-carbon. The science and technology should focus on the low-carbon oriented innovation and application. Attentions should be paid to the double-wheel drive of government and market in terms of system. The development of smart agriculture in China is still at its initial stage. There are shortcomings and bottlenecks in data resources， technical equipment， production capacity， talent reserve， policy support， and other aspects. Therefore， we should establish a carbon data system for agriculture， strengthen the innovation and R & D in agricultural technology， improve the training system of talent， promote the moderate scale operation of agriculture， construct a low-carbon development mechanism for smart agriculture with “government doing something， market being effective” in the future.

Abstract:The intelligent water-fertilizer integrated system is the integration of advanced information technology and traditional water-fertilizer integrated technology. It serves as a vital platform to enhance the utilization efficiency of water-fertilizer through the application of advanced information technology. However， the development of intelligent water-fertilizer integrated systems in China is still in its early stages. The intelligence and quality of products on the market are low in general. The standard of product is needed to be established. This article uses game theory to study the behavioral and institutional factors behind the failure of market and propose potential solutions. The results showed that the subsidy policy failed to identify products with high quality， leading firms to produce intelligent water-fertilizer integrated systems with low quality. The collaboration between industries and universities or institutes in China did not provide enough reward to researchers， resulting in a decline of the research effort. Standard negotiation in a monopolistic competitive market caused the cost of transition higher than the potential benefits. In response to the issues and reasons mentioned above， the suggestions are put forward as followings： the government should optimize the subsidy system to strengthen the identification and subsidy of the intelligent agricultural machinery equipment with high-performance. The assessment mechanism of scientific institutions should be improved to enhance the service power of the transformation department of scientific research achievements in higher education institutions. Subsidies should be given to enterprises for changing the production standards of product， guiding the establishment of unified market standards.

Abstract:Smart agriculture integrates modern information technology， agricultural machinery and equipment， and biotechnology， which is the development trend of modern agriculture. China is in the early stage of transitioning from traditional agriculture to intelligent agriculture. This article used the bibliometric analysis to analyze 40 812 relevant literatures in the field of global smart agriculture collected by the SCIE database. A knowledge map was drawn to conduct in-depth analysis on the core elements of knowledge， research topics， and cutting-edge hotspots of smart agriculture to provide reference and guidance for the development and study of smart agriculture in China. Results showed that the number of publications in the field of smart agriculture has increased significantly since 2016. China is the country with the fastest development in this field globally. Results of co-occurrence clustering analysis on keywords from 632 highly cited papers on smart agriculture in the past decade showed that the core elements of knowledge in smart agriculture included remote sensing， artificial intelligence， drones， the Internet of Things， and big data. smart agriculture can be divided into three major research topics including modern biotechnology represented by biological big data， information technology represented by the Internet of Things， artificial intelligence and remote sensing， intelligent agricultural machinery and equipment represented by drones and agricultural robots. The development of smart agriculture is a process of interdisciplinary integration to achieve highly precise， intelligent， and efficient agricultural production. Results of analyzing the evolution of keywords showed that information perception， processing， and management represented by the Internet of Things， as well as artificial intelligence algorithms represented by machine learning and deep learning， have been cutting-edge hotspots in smart agriculture research in recent years. The development of smart agriculture in the future was discussed from the perspectives of policy formulation， talent cultivation， and key technologies. It was proposed to achieve the agricultural transformation and upgrading in China by laying out key areas， cultivating new application-oriented talents， and developing original innovation.

Abstract:Image segmentation， as an important component of the vision system in smart agricultural farming， is widely used in the smart farming of livestock and poultry. In recent years， deep learning algorithms have been booming， and image segmentation technology based on deep learning have also made significant breakthroughs. These methods give more accurate semantic information to the segmented region， making the segmentation more accurate and intelligent， and providing stronger technical support for poultry and livestock smart farming. Through extensive collection and analysis of relevant domestic and foreign research literature， this paper first elaborates the application of image segmentation based on deep learning of poultry and livestock farming in detail， such as measurement of body size and weight， attitude estimation and behavior recognition， body condition and disease detection， precision feeding， etc. Suggestions are given on how to choose appropriate image segmentation methods based on actual performance requirements （accuracy， processing speed）， datasets， and computational resources. Furthermore， the open datasets， which are summarized and analyzed in current literature， related to livestock and poultry farming can be used for image segmentation training. This paper points out the challenges and future development trends of image segmentation technology based on deep learning in livestock and poultry farming， hoping to provide reference for the specific application of image segmentation technology in livestock and poultry farming.

Abstract:It is an important study direction in the area of smart farming to explore the combination of new progress in the field of pig farming with artificial intelligence. Among them， how to automatically identify the individual identity and behavior of pigs is a key and hot issue to be solved in the current pig breeding industry. This article summarizes the existed methods of using deep neural networks to identify the individual identity and behavior of pigs based on the progress of computer vision and deep learning models in human recognition. The problems in the existed methods are analyzed， and the key study directions in the future are proposed. Five aspects urgently needed to be developed are as follows： （1） the methods of accurately extracting the features of pig’s identity and behavior under the conditions that pig’s behavior cannot be controlled and the key parts of pig’s body are often contaminated； （2） the deep learning models based on computer vision that dedicate for pigs to recognize the identity and behavior based on the specific features of pigs； （3） the studies on multi-task deep learning models that can recognize pig’s identity and behavior simultaneously； （4）the studies on general-purpose pig behavior recognition methods based on basic postures and movements that are applicable to multiple scenarios； （5） the studies on the deployment methods of pig identification and behavior recognition based on edge computing.

Abstract:To obtain the core body temperature of breeding pigs， a total of 108 female pigs from three breeds， Yorkshire， Landrace， and Yorkshire×Landrace hybrids were collected. A handheld infrared thermal imager was used to obtain infrared images of 11 body parts， including the eyes， ears， neck， shoulders， front back， hind back， rump， tail， genital area， hindquarters， and abdomen. Environmental information of the corresponding pig farm， including temperature， humidity， and wind speed， was obtained through temperature， humidity， and wind speed sensors. The data was divided into training and testing sets using a nested 5×4 cross-validation method. The preprocessed data was then used to build quantitative analysis models， including the least squares support vector regression （LSSVR）， support vector machine （SVM）， random forest （RF）， and ridge regression methods based on infrared image processing technology， as well as the local infrared imaging and environmental factors of breeding pigs. The LSSVR model was determined to be the best-performing model with a coefficient of determination （R2） of 0.639， and the root mean squared error （RMSE） and mean absolute error （MAE） were 0.133 and 0.110 ℃， respectively. To improve the model’s fitting effect， four possible influencing factors， including pig breed， pregnancy period， estrus， and sampling time （morning or afternoon）， were added. The results showed that except for pig breed， other factors increased the model’s performance by 4%， 8% and 10%， respectively. Finally， the R2 of the optimized model was 0.773 with an RMSE and MAE of 0.106 and 0.09 ℃， respectively. These results indicate that adding pregnancy period， estrus， and sampling time as factors can significantly improve the model’s fitting degree， making it more accurate and therefore useful as a factor for core body temperature inversion of breeding pigs.

Abstract:In order to solve the problem of a large amount of redundant data in the retrieval process of sheep disease and the waste of resources caused by manual selection of accurate answers after retrieval， this study constructed a question-and-answer system based on the knowledge graph of sheep & goat disease through the following three steps： （1） The data was obtained through web crawlers and some is manually extracted， automated information extraction was carried out using the bidirectional long short-term memory recurrent neural network （Bi-LSTM） model with an attention mechanism for improved recognition efficiency in the named entity recognition task. The entity annotation was performed using the BIO rule to complete the information extraction. The data was then integrated and stored in the Neo4j graph database . （2） For the attribute mapping， we constructed the Bert-softmax model； according to the user’s question， the Bert model was used to calculate the semantic similarity between the question and the attribute to determine the user’s intention， then the softmax algorithm was used to for normalization， finally， the most suitable answer was found and fed back to the system. （3） We built a sheep & goat disease diagnosis platform using Bootstrap， Echarts， and Vue components to visualize the sheep disease question-and-answer system.We used flask framework included in the Python language to build a backend， encapsulate disease information， present it to users through the web frontend， and establish a connection on the backend to enable data interaction. The results in the study show that the F₁ value of entity recognition based on Bi-LSTM + Attention + CRF model is 83.16%， and the constructed knowledge graph contains 4 576 entities and more than 13 000 entity relationships. The pre-trained model Bert was added to the question answering system， and the F₁ value of problem recognition was 85.24%. The results indicated that the system can quickly retrieve and accurately answer various types of questions such as the prevention and control measures of sheep diseases， and assist the farmers to make production decisions when faced with sheep diseases.

Abstract:In order to solve the problems of low image segmentation accuracy and poor segmentation results for small targets caused by the uneven density of Takifugu rubripes， an instance segmentation method based on improved light SOLOv2 is proposed. Firstly， the structure of deformable convolutional networks （DCN） is optimized by adjusting the receptive field of the convolution using offset parameters.This adjustment enables the receptive field to be closer to the actual shape of the object， leading to better segmentation accuracy.Next，the parameter-free attention mechanism SimAM is fused in the last layer of the residual module to capture more local information in the image， obtain target features at different scales， and optimize the performance of the model for small target segmentation. The experimental results show that the average segmentation accuracy of the improved lightweight SOLOv2 model was improved by 3.7 percentage， and the segmentation accuracy of small targets was improved by 1.4 percentage compared with the original model. After adding both DCN and SimAM attention modules， the segmentation accuracy of the model increased to 65.2%. The results show that the improved SOLOv2 model can improve the detail perception at the boundary， strengthen the model’s ability to extract the features of small target fish stocks， and can be used for accurate instance segmentation in high-density scenarios to achive accurate pixel-level segmentation of Takifugu rubripes.

Abstract:In order to enhance the accuracy of event extraction for aquatic animal disease prevention and control， and effectively address issues such as ambiguous boundaries of proprietary terms and excessively lengthy event entities during the extraction process， the research introduces the idea of dynamic weight into the event extraction method of multi-model integration. Two pre-training models，ERNIE（enhanced representation through knowledge integration）and MacBERT（MLM as correction BERT）， are used to learn the text semantic information.A gate module with dynamic weights is used to fuse features to enhance the semantic information of the original text.Pass the learned semantic information into BiLSTM （bi-directional long shortterm memory）， and constrain the output label sequence through CRF （conditional random field）.Select the ERNIE⊕MacBERT-CRF model and the ERNIE⊕MacBERT-BiLSTM-CRF model （⊕ represents the fusion method of simple addition and averaging） as the control model to conduct a comparative test of the fusion performance of the proposed method.The results show that the F1-score of this method reaches 74.15%， which is 20.02 percentage points higher than the classic model BiLSTM-CRF.The results show that this method has a better effect in the extraction of aquatic animal disease prevention and control events.

Abstract:In order to understand and master the water quality status of sea cucumber aquaculture， a multi-level fuzzy evaluation method using a fusion of triangular fuzzy number and analytic hierarchy process was used to evaluate the water quality of sea cucumber farming. Firstly， the water quality key factors were classified based on their positive and negative effects on sea cucumber growth during the farming process， and a “positive/negative correlation factor fuzzy reasoning system for sea cucumber aquaculture water quality” was designed based on the two categories of key factors. Secondly， the two fuzzy reasoning systems were used as inputs for the first level fuzzy system， and the results were used as inputs for the second level fuzzy system. The final water quality evaluation results were obtained through the second level fuzzy system based on the first level positive/negative correlation factor reasoning results. During the fuzzy reasoning process， the triangular fuzzy number analytic hierarchy process was used to assign weights to the sea cucumber water quality key factors in order to improve the accuracy of the evaluation results. Finally， this method was compared to the single-level fuzzy evaluation system and ANFIS fuzzy evaluation system using a fusion of triangular fuzzy number and analytic hierarchy process for evaluating the water quality of sea cucumber aquaculture. The results showed that the three methods produced consistent evaluation results. The multi-level fuzzy evaluation method for sea cucumber aquaculture water quality using a fusion of triangular fuzzy number and analytic hierarchy process reduced the number of fuzzy rules from the original 243 to 45， alleviating the problem of dimensionality catastrophe. Moreover， it requires less parameter tuning and training compared to the ANFIS fuzzy evaluation system and occupies fewer system resources. These results indicate that the fusion of triangular fuzzy number and analytic hierarchy process is more suitable for water quality evaluation and management in sea cucumber aquaculture.

Abstract:Bird sounds have diverse features， and most of the current convolutional neural network models based on a single receptive field are difficult to learn the diversity of bird sound features from audio containing complex background noise. In this article， we proposed a method of classifying bird sounds based on the Inception-CSA deep learning model， which consists of three steps including bird audio sample preprocessing， feature extraction， and classifier classification. First， the samples of bird sounds were preprocessed into Mel spectrum maps with the same size as the feature maps of bird sounds. Then the feature of bird sounds was extracted with the Inception-CSA model including the Inception module extracting the multi-scale local time-frequency domain features in the feature map of bird sounds and the CSA module obtaining the global attention weights of the feature map of bird sounds. The output of both was combined to obtain a stronger feature map. The feature maps were downsampled with the maximum pooling layer. Finally， the results of final classification were obtained with the fully connected layer. The calls of 10 wild bird species in the natural environment of south China were collected and the dataset was constructed to verify the effectiveness of the method. The results showed that the proposed method achieved 93.11% accuracy on the self-built dataset. The classification method based on the Inception-CSA model had higher accuracy with fewer model parameters compared with the classification methods based on other classical models.

Abstract:The statistics of pollination times of honeybee was evaluated based on machine vision to evaluate the rationality of the dosage of pollinating honeybee during the window period of blueberry blooming in the solar greenhouse. The dataset was processed with the method of improved poisson blending data enhancement to solve problems that the detection environment is complex， the target scale is small， and it is easy to be covered. The structure of YOLOv5 was optimized. The detection precision of model was improved by introducing GAM attention mechanism and Transformer module. BiFPN and CARAFE were introduced in feature pyramid network to complement the contextual information. EIoU_loss and Soft NMS were used to enhance the positioning precision of bounding box and solve the problem of detecting target occlusion. The results showed that the mean average precision of the improved YOLOv5 was 96.6%， 3.5 percentage points higher than that of the original algorithm. The detection time of a single blueberry pollination image on the GPU was 11.4 ms.

Abstract:During the production，operation and processing of duck egg，the egg shells are easily broken and microorganisms including bacteria tend to invade the egg from the shell cracks，which in turn affect the quality of the eggs and damage economic benefits of production. An identification algorithm of duck-egg shell crack based on MEL spectrum was established by using ResNet34 network model to solve the problem of the subjectivity and large fluctuation of accuracy in manual identification of duck egg shell cracks. First， the egg knocker was used to collect the sound data， and the audio was transformed into the MEL spectrum graph to construct the dataset of MEL spectrum graph. Then the ResNet34 model was built and the transfer learning mechanism was introduced to train the model. The gradient was updated by Adam optimization algorithm， the attention mechanism module was added， and the convolution structure was replaced by a deeply separable convolution to improve the network model. The parameters were adjusted for optimization and the duck egg shell cracks were identified with the model.The results showed that the average detection accuracy of the ResNet34DP_CA enhanced network model was 92.4%，which was 5.5 percentage points higher than that of the original ResNet34 network model. The quantity of parameter was reduced by 32%. Compared with other network models including VGG16， MobileNetv2 and EfficientNet， the average accuracy was improved by 10.9， 13.7 and 16.3 percentage points，respectively. The recognition time was 21.5 ms. It is indicated that the established identification algorithm of duck-egg shell crack based on Mel spectrogram and the improved ResNet34 model can efficiently identify the duck-egg shell cracks. It will be of great significance to improve the economic benefits of production and to build an intelligent and modern poultry factory.

Abstract:The detection of multitemporal semantic changes is often used to monitor changes in agricultural ecology and to track the development of agricultural land because it uses semantic information to analyze the specific types of changes. A method of detecting multitemporal semantic changes using weak labels with noise and low resolution instead of high-resolution labels was proposed to solve the problem that the scarcity of high-resolution remote sensing image labels and the slow growth of labeling technology limit the development of detecting multitemporal semantic changes. First，low resolution satellite data were used to smooth the quality differences of high-resolution remote sensing image inputs. Secondly，the high-resolution remote sensing image classification map was estimated by combining the epitomes model and the label super-resolution algorithm as a statistical inference algorithm，and a small FCN network was fitted to post-process the remote sensing image classification map generated to improve its classification. Finally，the results of detecting change were obtained by comparing the differences between different simultaneous land cover classification images. The results showed that the proposed method improved the mean intersection over uion （mIoU） by 8.9 percentage points compared with other methods of detecting multitemporal semantic changes，and detected the changes of land cover classification effectively.

Abstract:In order to address the challenges of manual identification of pests in images collected by light traps， as well as the low reliability and poor accuracy of statistical results， this study proposes an improved Cascade R-CNN algorithm for field pest detection. The algorithm is based on the Cascade R-CNN framework and uses ResNeSt-50 as the backbone network， incorporating cross-channel attention mechanisms to obtain feature maps more conducive to pest detection. A unifying object detection head with attentions （DyHead） is used， incorporating scale awareness， spatial position awareness， and task awareness to improve the performance of the detection head. Additionally， the simple copy-paste （SCP） method is employed for data augmentation to enhance the model’s detection capabilities in complex scenarios. A total of 1 500 images of 20 pest categories were collected， and a monitoring lamp field pest dataset compliant with the microsoft common objects in context （MS COCO 2017） format was created. The results show that the F₁-score of the proposed method reaches 86.2%. When the intersection over union （IoU） is set to 0.5， the F₁-score increases by 2.8， 5.8， and 8.2 percentages compared to the classic Cascade R-CNN， Faster R-CNN， and YOLOv4， respectively. The results shows that the proposed method meets the requirements of discriminative ability and real-time performance for monitoring lamp pest detection tasks， achieving high-precision automatic identification and counting of pests， and can be directly applied to field pest detection.

Abstract:Because rice pests are usually small in size， some different types of pests have similar appearance， and the same type of pests have different appearance in different growth processes， it is very difficult to identify rice pest types. We improved YOLOv7 neural network by introducing convolutional block attention module and feature pyramid module and constructed a challenging rice pest dataset， which is collected from rice planting base of Ezhou City in Hubei province to recognize rice pests. According to the characteristics of sample distribution， data enhancement was carried out， and random noise， Mixup， Cutout and other data enhancement methods were introduced to make the deep learning model learn the visual features of pest discrimination from a deeper dimension. Taking MobileNetv3 as the backbone network， the YOLOv7 network was improved， and a multi-scale neural network model based on feature pyramid was constructed to improve the identification accuracy of small individual pests. The results showed that the average accuracy rate of rice pest detection based on the improved method is 85.46%， surpassing the networks such as YOLOv7 and Efficient Net-B0. The size of the improved YOLOv7 model is 20.6 M， and the detection speed is 92.2 frames/s， which is more than 5 times that of the original YOLOv7 algorithm. The results indicate that this method can be applied for remote automatic recognition of rice pests.

Abstract:In order to solve the problems of poor generalization ability and low recognition accuracy in the identification of cucumber leaf disease with small samples under complex field environment， the self-attention mechanism module was introduced into the activation reconstruction network AR-GAN （activation reconstruction GAN）， and the smooth L₁ regularization was used as the loss function to design and improve the activation reconstruction network IAR-GAN （improved AR-GAN） to expand the cucumber leaf disease image. By adding void convolution and deformation convolution on the basis of the Inception network， the void and deformation convolution neural network （DDCNN） was designed for cucumber leaf disease identification. The test results showed that the proposed IAR-GAN effectively alleviated the over-fitting phenomenon and enriched the diversity of generated samples. The average recognition accuracy of the proposed DDCNN for cucumber anthracnose， spot target disease and downy mildew was more than 96%， which is 9% higher than the Inrception-V3 model. The above results showed that the data expansion method and disease identification model proposed in this paper can provide new ideas for the accurate identification of crop leaf diseases with small samples in complex environments.

Abstract:Pest infestation reduces fruit quality and causes economic losses. Accurate identification of citrus pests is conducive to pest control. However， as the features to distinguish these pests are not obvious， manual classification is time-consuming and labor-intense， while advanced algorithms have high computational costs. Therefore， it is necessary to develop lightweight and accurate identification tools. In this article， a data set of insect pest images containing 10 types of pest images that are most harmful to citrus was constructed. A network featuring lightweight and high precision was developed based on MobileNet-V2 and the attention mechanism ECA. Moreover， an edge computing APP was also developed that can be run on Android phones. The ECA attention mechanism was embedded in the tail of the anti-residual structure of the improved MobileNetV2 network to enhance the cross-channel information interaction ability and improve the feature extraction ability. The results of testing showed that the ECA_MobileNetV2 model had a classification accuracy of 93.63% for citrus pests， 1.68， 1.44 and 2.40 percentages higher than that of the MobileNetV2， GoogLeNet and ResNet18 models， respectively. The parameter， FLOPS and size of model was 3.50×10⁶， 328.06×10⁶ and 8.72 MB， respectively. Its complexity is only slightly higher than that of MobileNetV2， and it can run in the form of edge computing on mobile phones. It is indicated that the developed intelligent recognition tool can quickly and effectively classify and identify different types of citrus pests.

Abstract:An improved statistical texture residual learning network （STRLNet） for the fine-grained classification of grape leaves was constructed to solve the problem of low classification accuracy of intra-class varieties caused by high inter-class similarity between grape leaves. SE attention mechanism was added on the basis of ResNet50 backbone network. The feature enhancement layer of the underlying information was built. The enhanced underlying features with the high-level semantic information extracted from the backbone network were integrated. The output was connected to the full connection layer used for storing the characteristics of classification. The collected dataset of mature grape leaves of 11 cultivars were used for training and testing. The results showed that STRLNet fully utilized the underlying feature information while improving the spatial performance of the network， with a classification accuracy of 92.26% for the collected dataset of grape leaves. It was about 2.8 percentage points higher than that of the ResNet backbone network. It had higher accuracy in fine-grained classification of grape leaves compared with mainstream classification networks including VGG16， Inception v4， and ResNet. It is indicated that the improved model can focus on more feature information compared with the backbone network in the classification of grape leaves from multi-cultivars. It can achieve higher classification accuracy compared with the mainstream classification network models and further improve model performance.

Abstract:The 3D point clouds obtained from 3D scanner and multi-view data lack semantic information，leading to difficulties in discriminating the plant organ parts from the point clouds when the number of plant point clouds is large，or when different organs of the plant are similarly colored or obscured.To deal with the problem，this article proposes a three-dimensional semantic modeling method for soybean leaves embedded with a two-dimensional semantic prior.The semantic segmentation of soybean leaves based on Mask R-CNN was conducted.The three-dimensional reconstruction，fusion and learning of the segmentation results and multi-view data were performed to transfer the semantic information of leave from 2D semantics to 3D point clouds and obtain the point cloud semantic information of plant leaf.The 3D semantic model of plant leaves was established.The model was validated through multiple sets of potted soybean plant experiments.The length and width of leaf were extracted and compared with the manual measurement data.Results showed that the mean square error of the length and width of leaf was 2.53 and 1.52 mm，with the determination coefficients of 0.97 and 0.89，respectively.It is indicated that the proposed method can conveniently and accurately construct the 3D semantic model of plant leaves.

Abstract:Biomass accumulation during the growth and development stages of rice is one of the key factors determining the rice yield.With the continuous development of UAV remote sensing technology in recent years，quantitative remote sensing inversion of rice biomass with UAV high-definition images，multispectral and hyperspectral remote sensing data has become an important technique to quickly obtain biomass information at the critical reproductive stages of rice.The UAV hyperspectral remote sensing platform was used to obtain the hyperspectral reflectance information of rice canopy at 400 to 1 000 nm to solve the poor universality and mechanism of inversion models for rice leaf biomass.The sensitivity of parameters for PROSAIL model was analyzed，and the sensitive wavelengths were extracted with continuous projection method according to the results of analyses.On this basis，the bald eagle algorithm （BES） was used to optimize the biomass parameters of the PROSAIL model to quickly and accurately retrieve leaf biomass inversion at the critical reproductive stages of rice through combining the PROSAIL crop radiation transmission model with rice hyperspectral data.The results showed that the improved Sobol method was used to analyze the global sensitivity of rice leaf biomass，and the sensitivity range was 700-1 000 nm.Six characteristic wavelengths of rice leaf biomass，namely 750，788，898，940，962 and 999 nm，were extracted with continuous projection method for the spectra at the sensitive interval.The PROSAIL-BES numerical optimization method was constructed by combining the PROSAIL model with the BES optimization algorithm.Using the spectral reflectance of rice characteristic wavelengths as the input of model，the parameters for the PROSAIL model were corrected by PROSAIL-BES numerical optimization method.The results of leaf biomass inversion showed that R² was 0.694 and RMSE was 0.002. It is indicated that the PROSAIL-BES numerical optimization method has better accuracy of inversion compared with the inversion results of traditional machine learning models，and has better practical value and application potential in the field of rice biomass inversion.

Abstract:With the development of hyperspectral remote sensing technology,hyperspectral prediction of crop growth can provide scientific management for agricultural production,which can improve crop yields and quality while avoiding excessive use of nitrogenous fertilizers.A mathematical model to invert the content of chlorophyll in cotton leaves was developed using continuous wavelet analysis and conventional spectral transformation to decompose and transform the raw leaf spectra of cotton.The characteristic wavelet coefficients and spectral characteristic bands were used as independent variables.Methods including univariate,stepwise regression and partial least squares were used.The results showed that different spectral treatments improved the correlation between the content of chlorophyll and spectral reflectance of cotton leaves.For the conventional spectral transformation,the inverse logarithmic first order differential lg(1/R′) improved the chlorophyll correlation of cotton leaves by 0.41.It is indicated that the continuous wavelet analysis is superior to traditional spectral models in terms of information noise reduction and mining of feature information.The model established has good stability with RPD>2 and good prediction ability for data sampled.

Abstract:Jujube as one important economic crop in Southern Xinjiang was used to analyze the relationship between raw spectra and first-order differential spectra of jujube leaves and the content of total nitrogen with hyperspectral techniques. A model for predicting the content of nitrogen was established to provide a theoretical basis for nitrogen monitoring and precise fertilization during jujube cultivation. Spectral sensitive variables were used to construct vegetation indices as derivative variables. Multiple linear and nonlinear models for predicting the content of nitrogen were established using derivative variables as variables. The accuracy of models for predicting the content of nitrogen was tested. Results showed that the fitted decision coefficients of models based on the original spectra and first-order differential spectra of jujube trees were greater than 0.75. The overall prediction performance of the original spectral variables was better than that of first-order differential spectra. The best prediction was based on the power function model of the original spectral variables 4： Nit =1.097x^0.735， R²=0.821， and RMSE=0.024 5. It is indicated that the model established for predicting the content of nitrogen can achieve good effect of monitoring nitrogen in jujube tree based on hyperspectral reflectance characteristics， and can serve as an important theoretical basis for the nutrient diagnosis of jujube tree.

Abstract:The content of amylose， crude protein and water is an important index to measure the quality of rice grain. The transmittance and reflectance spectral data of rice grains from 100 rice core germplasm resources were collected using a near-infrared hyperspectral camera， and the spectral parameters were extracted to study the method for the non-destructive testing of quality traits in rice seed. Index of rice quality components was measured using a near infrared grain analyzer after the rice kernel was shelled. A model for predicting index of rice quality was established using the spectral parameters of rice grains as independent variables and index of rice quality as dependent variables. The results showed that the modeling effect of transmission spectrum was better than that of reflection spectrum when a single spectral model was used. Combined with characteristic spectral sets of transmission and reflection， the R² of the model for predicting crude protein， amylose and water was increased from 0.74 to 0.91， from 0.40 to 0.69， and from 0.53 to 0.68， respectively. It is indicated that the modeling effect can be improved by using both spectrum of transmission and reflection， and index of rice quality can be predicted nondestructively by using the spectral parameters of rice grains.

Abstract:Yellow peaches are soft and prone to damage，and the different level of damage can directly affect the end use and sale price of yellow peaches. The reflection （R），absorption （A），and Kubelka-Munk spectra （K-M） of yellow peaches were obtained by using hyperspectral techniques and used to detect the early damage level of yellow peaches. Partial least squares discriminant analysis （PLS-DA），extreme gradient boosting （XGBoost） and random forest （RF） models based on three raw spectra and various pretreated spectra were established. The results were compared to select the model with higher correctness. The model with its characteristic wavelength was constructed and compared again. The results showed that RF models based on the three raw spectra and SG pretreated spectra were superior in discriminating，with the overall accuracy rates all above 90.00%. The wavelength screening of the raw spectra and SG pretreated spectra was performed with the competitive adaptive reweighting （CARS） and uninformative variable elimination （UVE） algorithms，and the RF models were established again. The results showed that the A-RAW-CARS-RF model and the K-M-SG-CARS-RF model were improved in discriminating compared with the RF model at full spectrum. Among the RF models established based on the characteristic wavelengths，the A-RAW-CARS-RF model had the best discriminating effect with an overall accuracy of 97.12%. The number of misclassifications for the four subcategories were 0，1，1，and 1. It is indicated that the feasibility of detecting the early damage level of yellow peaches based on absorption spectroscopy （A）. It will provide some theoretical basis for detecting fruit bruise with hyperspectral techniques in the future.

Abstract:A three-vigor gradient classification model for maize seeds was constructed using machine learning and deep learning algorithms along with hyperspectral imaging technology to solve the problems of time-consuming and seed damage in the traditional method for detecting seed vigor and to realize the rapid， non-destructive detection of maize seed vigor. 1 012 maize seeds were divided into three vigor gradient samples by artificial aging. The hyperspectral noise was removed with convolution smoothing （SG） and multivariate scattering correction （MSC） after collecting the hyperspectral data of maize seeds. Principal component analysis （PCA） and continuous projection algorithm （CPA） were used for dimensionality reduction of spectral feature， respectively. Three bands including 1 156 nm， 1 191 nm， and 1 463 nm were extracted from the reduced dimensionality band to synthesize a false color image. The texture features of region of interest （ROI） were extracted using local binary mode （LBP） and fused with pure spectral features. Machine learning models including decision tree （DT） and support vector machine （SVM） models constructed based on pure spectral features and the random forest （RF）， SVM and extreme gradient lifting tree （XGBoost） models constructed based on fused features were established. Maize seed vigor was predicted by inputting the false color images into five deep learning models including ResNet18， MobileNetV2， DenseNet121， Efficientb0， and Efficientb2. The results showed that the PCA-SVM model performed best for pure spectral features， with a test set accuracy of 92.5% in terms of machine learning methods. The SVM model performed best for fusion features， with a test set accuracy of 93.1%. In terms of deep learning methods， the lightweight MobileNet achieved the highest test set accuracy of 99.5%. The classification activation mapping method based on interpretable gradient indicated that the classification network focused on the bottom or basal region of maize seeds. It will provide some references for the nondestructive detection of maize seed vigor in terms of data sources， deep neural network visual interpretation and machine learning， and deep learning performance analysis.

Abstract:Bacterial biofilms widely exist on the surfaces of food processing machinery， medical equipment and the environment， and bring a huge threat to human health due to strong drug resistance. Escherichia coli，Staphylococcus aureus， and Salmonella typhimurium were used to study the feasibility of species identification and evaluate film-forming ability of different bacterial biofilms by hyperspectral fluorescence imaging technology to solve the problems of time-consuming， laborious and inefficient detection of existing biofilms. The hyperspectral fluorescence images of bacterial biofilm samples were collected， and support vector classification machine （SVC） and partial least squares discriminant analysis （PLS-DA） models based on the spectral data preprocessed by five methods were established to classify bacterial biofilms. Characteristic wavelengths were extracted by employing successive projections algorithm （SPA） and competitive adaptive reweighted sampling （CARS） respectively， and the corresponding simplified models were established. The results showed that SVC outperformed the full-wavelength and characteristic-wavelength models of identifying bacterial biofilm species than the PLS-DA， with the optimal model being None-SPA-SVC， where the classification accuracy of calibration set （CCRC） and prediction set （CCRP） were both 96.67%. In the classification and discrimination of film-forming ability of bacterial biofilm， the full-wavelength SVC models generally outperformed PLS-DA with higher classification accuracy. For the simplified models， the optimal model was SPA-SVC， with CCRC and CCRP of 100.00% and 96.67%， respectively. It is indicated that hyperspectral fluorescence imaging technology can effectively， quickly and accurately classify the types of bacterial biofilms and the film-forming ability of biofilms.

Abstract:A two-stage mixed integer programming model was established to solve the problem of fine match of task allocation and scheduling in the “single base station multiple drones” mode of plant protection drones on the platform of the agricultural socialization service. First， the cost minimization goal of task allocation and scheduling of plant protection drone was solved under the condition of quickly and accurately solving the dispersed plant protection needs of farmers by introducing agricultural parameter variables including variable time windows for pesticide efficacy under the influence of temperature， flight mode selection of crop protection drone in farmland， and variable power consumption rate. Then， the improvement of the cost minimization goal was studied by changing the rated battery life time， flight mode in the field， optimal time window of drug efficacy corresponding to temperature and the size of demand of the plant protection drone while ensuring other parameters remain unchanged. The results showed that plant protection drones reduced the plant protection cost by 40.02%，12.45%，21.17% and 39.19%，respectively， by carrying batteries with long duration of flight，choosing the long-side turning grid flight， selecting pesticide types with the best efficacy and a wide range of temperature， and increasing the upper limit of platform service volume per unit time period. It is indicated that the mixed Integer programming model， which introduces specific agricultural production factors， is scientific and effective， and is more suitable for the task allocation and scheduling of plant protection drones on the platform of agricultural service.

Abstract:A remote monitoring system for unmanned sowing operation of rapeseed direct seeding unit was designed by taking the Reeva 804 tractor and its equipped 2BYQ-8 air-fed rapeseed direct seeding machine as the test platform to solve the problem that it is difficult to display the field sowing quality information in real time and intuitively during the unmanned sowing operation of rapeseed direct seeding unit. The system consists of three parts including unmanned sowing platform， unmanned sowing data collection system and sowing quality monitoring cloud platform. Corresponding control strategies were designed to achieve unmanned sowing operation of the direct seeding unit through the electric and hydraulic modification of the gear， clutch， power take off （PTO）， and suspension mechanism of the Lovol 804 tractor. A vehicle mounted router was used to establish a local area network between the seeding monitoring terminal and the on-board computer to realize the fusion and synchronization of seeding data and navigation data. Data were transmitted to cloud platforms through network connections for data storage and real-time display. The cloud platform calculated the seeding quality data and its corresponding field location data， and generated the seeding status map of the field operation area based on the high-precision map on the web page. Results showed that the average lateral deviation of the unmanned sowing operation section of direct seeding unit was 0.037 m， with a maximum deviation of 0.125 m. The electric and hydraulic modification system operates stably and reliably， meeting the unmanned operation requirements of the direct seeding unit. The maximum data transmission time delay of the cloud platform communication did not exceed 100 ms under the 4G network conditions. The cloud storage data is complete without omission， the accuracy of field sowing detection of each sowing channel is not less than 96.16%， meeting the real-time and accuracy requirements of remote monitoring system. It is indicated that the system can realize the unmanned sowing operation of rapeseed oil direct seeding unit in the field and the accurate collection and intuitive display of sowing information in the operation area. It will provide reference for the remote monitoring of rapeseed sowing operation， the analysis and visualization of sowing data.

Abstract:Insects are the most diverse animal group in nature. Some species are difficult to collect， which makes datasets often highly heterogeneous with long-tailed distributions. This article proposed a convolution recognition network model based on synthetic samples combined model （SSCM） to solve the problem that the uneven distribution of insect datasets leads to the poor recognition performance of recognition models in tail categories with less data. The model contains three modules including image segmentation and shuffle module， backbone network module and data fix branch module. Through the image segmentation and shuffle module， the training image was segmented and shuffled to obtain new training data and added to the training set. ResNet-50 was used as the network backbone to extract features of image. At the same time， the data fix branch module combined the mean square error and cross-entropy to calculate the error between the synthetic samples and the original image to reduce the adverse effect of the synthetic samples on the tail data. A butterfly dataset containing a total of 26 045 images of 300 species was constructed to evaluate the performance of the model proposed. The results showed that the accuracy of SSCM model was 3， 2.14 and 2.71 percentages higher than that of DRC， BBN and RIDE in the butterfly dataset， respectively. In addition， the validity of the SSCM in the public IP102 insect dataset was verified on the public insect dataset IP102. The results showed that the accuracy of SSCM model was 18.94， 3.02 and 3.36 percentages higher than that of DRC， BBN and RIDE， respectively.

Abstract:Using information technology to realize the effective integration and application of multi-source heterogeneous spatio-temporal multi-modal big data of agriculture is a key issue that needs to be urgently solved in the precision agriculture. A method of constructing， controlling and decision-making for precision fertilization was proposed based on the spatio-temporal multi-modal knowledge graph of agriculture to construct a management system and realize the fine management of nutrients in field. The nodes and relationships in the plots to be queried and the spatio-temporal multi-modal knowledge graph of agriculture were embedded and represented through the subgraph matching method based on deep learning. Vector similarity calculation was used to obtain candidate subgraphs. The fertilization model data suitable for query plots were obtained from the information of subgraphs storing historical data. The results showed sub maps isomorphic to the given land query map were obtained in the spatio-temporal multi-modal knowledge graph of agriculture based on the instantiated query map of the land to be fertilized. An agricultural fertilization model suitable for the current plot was obtained from the information of subgraph storing historical decision. It is indicated that the automatic selection of model based on spatio-temporal multi-modal knowledge graph of agriculture is accurate and reliable. It will provide decision-making support for precision fertilization.