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In the food domain, text mining techniques are extensively employed to derive valuable insights from large volumes of text data, facilitating applications such as aiding food recalls, offering personalized recipes, and reinforcing food safety regulation. To provide researchers and practitioners with a comprehensive understanding of the latest technology and application scenarios of text mining in the food domain, the pertinent literature is reviewed and analyzed. Initially, the fundamental concepts, principles, and primary tasks of text mining, encompassing text categorization, sentiment analysis, and entity recognition, are elucidated. Subsequently, an analysis of diverse types of data sources within the food domain and the characteristics of text data mining is conducted, spanning social media, reviews, recipe websites, and food safety reports. Furthermore, the applications of text mining in the food domain are scrutinized from the perspective of various scenarios, including leveraging consumer food reviews and feedback to enhance product quality, providing personalized recipe recommendations based on user preferences and dietary requirements, and employing text mining for food safety and fraud monitoring. Lastly, the opportunities and challenges associated with the adoption of text mining techniques in the food domain are summarized and evaluated. In conclusion, text mining holds considerable potential for application in the food domain, thereby propelling the advancement of the food industry and upholding food safety standards. [ABSTRACT FROM AUTHOR]

To study the fruit image fusion technology in the case of thermal infrared and visible heterogeneous sources and the method of online defect detection on fruit fusion images, this paper takes apple as the research object and proposes a registration algorithm for thermal infrared and visible images of apple based on the registration of feature points with an active contour model. First, by designing a thermal infrared and visible image acquisition system, thermal infrared and visible images of apple in the same scene are obtained simultaneously. Then, the improved Chan-vese model is adopted to obtain the active contour segmentation curves of the thermal infrared and visible images of apple respectively. Next, the average Euclidean distance of all adjacent edge points on the active contour segmentation curve is calculated, and the alignment feature point set is constructed by the linear interpolation method based on the obtained average distance, and the optimal scale transformation factor and the optimal horizontal transformation factor are obtained by calculating the partial Hausdorff distance between the two feature point sets. Finally, the registered visible image is acquired based on the obtained affine transformation matrix, thus realizing the registration of the thermal infrared and visible images of apple. The experimental results on the self-built image dataset indicate that the algorithm proposed in this paper can accurately match the heterogeneous images of intact fruit, calyx/stems, and defective fruit, and it performs excellently in terms of precise matching rate and root mean square error, and the high alignment success rate of 96%. Also, it has much better performance than other methods. The proposed registration algorithm can accurately match thermal infrared and visible images of apple, and lays the foundation for further research on the image fusion of thermal infrared and visible, apple surface defect detection, as well as the construction of online dual-light apple grading systems. [ABSTRACT FROM AUTHOR]

Apples, as the fourth-largest globally produced fruit, play a crucial role in modern agriculture. However, accurately identifying apple diseases remains a significant challenge as failure in this regard leads to economic losses and poses threats to food safety. With the rapid development of artificial intelligence, advanced deep learning methods such as convolutional neural networks (CNNs) and Transformer-based technologies have made notable achievements in the agricultural field. In this study, we propose a dual-branch model named DBCoST, integrating CNN and Swin Transformer. CNNs focus on extracting local information, while Transformers are known for their ability to capture global information. The model aims to fully leverage the advantages of both in extracting local and global information. Additionally, we introduce the feature fusion module (FFM), which comprises a residual module and an enhanced Squeeze-and-Excitation (SE) attention mechanism, for more effective fusion and retention of both local and global information. In the natural environment, there are various sources of noise, such as the overlapping of apple branches and leaves, as well as the presence of fruits, which increase the complexity of accurately identifying diseases on apple leaves. This unique challenge provides a robust experimental foundation for validating the performance of our model. We comprehensively evaluate our model by conducting comparative experiments with other classification models under identical conditions. The experimental results demonstrate that our model outperforms other models across various metrics, including accuracy, recall, precision, and F1 score, achieving values of 97.32%, 97.33%, 97.40%, and 97.36%, respectively. Furthermore, detailed comparisons of our model's accuracy across different diseases reveal accuracy rates exceeding 96% for each disease. In summary, our model performs better overall, achieving balanced accuracy across different apple leaf diseases. [ABSTRACT FROM AUTHOR]

In addition to the conventional situation of detecting a single disease on a single leaf in corn leaves, there is a complex phenomenon of multiple diseases overlapping on a single leaf (compound diseases). Current research on corn leaf disease detection predominantly focuses on single leaves with single diseases, with limited attention given to the detection of compound diseases on a single leaf. However, the occurrence of compound diseases complicates the accuracy of traditional deep learning algorithms for disease detection, necessitating the exploration of new models for the identification of compound diseases on corn leaves. To achieve rapid and accurate identification of compound diseases in corn fields, this study adopts the YOLOv5s model as the base network, chosen for its smaller size and faster detection speed. We propose a corn leaf compound disease recognition method, YOLOv5s-C3CBAM, based on an attention mechanism. To address the challenge of limited data for corn leaf compound diseases, a CycleGAN model is employed to generate synthetic images. The scarcity of real data is thereby mitigated, facilitating the training of deep learning models with sufficient data. The YOLOv5s model is selected as the base network, and an attention mechanism is introduced to enhance the network's focus on disease lesions while mitigating interference from compound diseases. This augmentation results in improved recognition accuracy. The YOLOv5s-C3CBAM compound disease recognition model, incorporating the attention mechanism, achieves an average precision of 83%, an F1 score of 81.98%, and a model size of 12.6 Mb. Compared to the baseline model, the average precision is improved by 3.1 percentage points. Furthermore, it outperforms Faster R-CNN and YOLOv7-tiny models by 27.57 and 2.7 percentage points, respectively. This recognition method demonstrates the ability to rapidly and accurately identify compound diseases on corn leaves, offering valuable insights for future research on precise identification of compound agricultural crop diseases in field conditions. [ABSTRACT FROM AUTHOR]

In order to address the significant prediction errors resulting from the substantial fluctuations in agricultural product prices and the non-linear features, this paper proposes a hybrid forecasting model based on variational mode decomposition (VMD), ensemble empirical mode decomposition (EEMD), and long short-term memory networks (LSTM). This combined model is referred to as the VMD–EEMD–LSTM model. Initially, the original time series of agricultural product prices undergoes decomposition using VMD to obtain a series of variational mode functions (VMFs) and a residual component with higher complexity. Subsequently, the residual component undergoes a secondary decomposition using EEMD. All components are then fed into an LSTM model for training to obtain predictions for each component. Finally, the predictions for each component are linearly combined to generate the ultimate price forecast. To validate the effectiveness of the VMD–EEMD–LSTM model, empirical analyses were conducted for one-step and multi-step forecasts using weekly price data for pork, Chinese chives, shiitake mushrooms, and cauliflower from China's wholesale agricultural markets. The results indicate that the composite model developed in this study provides enhanced forecasting accuracy. [ABSTRACT FROM AUTHOR]

Accurate and rapid estimation of leaf nitrogen content (LNC) in winter wheat using hyperspectral techniques is important for growth monitoring and accurate fertilization. Based on field experiments conducted over four years at three ecological sites with four different nitrogen (N) application treatments and four different N-efficient wheat varieties in Henan Province, the canopy spectra and LNC of winter wheat were obtained simultaneously in the main growth stages. The original spectrum was subjected to continuum removal (CR), the correlation between LNC and various vegetation indices was systematically analysed, and the optimal vegetation index estimation model for LNC was constructed. Simultaneously, discrete wavelet transform (DWT) was used to further compress and extract the CR spectrum. The model was combined with partial least squares regression (PLSR) and K-nearest neighbour (KNN) algorithms to estimate LNC in winter wheat. The results showed that the spectrum treated with CR was significantly improved in its correlation with LNC and that the model established based on normalized vegetation index NDVI (CR728, CR977) combined with the CR spectrum was better compared to existing vegetation indices. The calibration and validation coefficients of determination (R2) and root mean square error (RMSE) were 0.816 and 0.799, and 0.352% and 0.342%, respectively. DWT was used to transform the CR spectrum, and the results showed that a combination of the CR spectrum and a sym8 wavelet function with PLSR based on the approximation coefficients at decomposition level 2 predicted LNC most accurately. The coefficient of determination RC2, root mean square error RMSEC, and relative percent deviation (RPDC) of the calibration set were 0.884, 0.279%, and 2.932, respectively, and RV2, RMSEV, and RPDV of the validation set were 0.855, 0.291%, and 2.619, respectively, indicating that the model had good stability and predictive ability. Combination of CR and DWT improved the modelling accuracy of wheat LNC and showed better prediction results for multi-year and multi-point samples. The results of the study can provide a basis and reference for rapid monitoring of LNC in winter wheat. [ABSTRACT FROM AUTHOR]

Take-all is a root disease that can severely reduce wheat yield, and wheat leaves with take-all disease show a large amount of chlorophyll loss. The PROSAIL model has been widely used for the inversion of vegetation physiological parameters with a clear physical meaning of the model and high simulation accuracy. Based on the chlorophyll deficiency characteristics, the reflectance data under different canopy chlorophyll contents were simulated using the PROSAIL model. In addition, inverse models of spectral reflectance profiles and canopy chlorophyll contents were constructed using a one-dimensional convolutional neural network (1D-CNN), and a transfer learning approach was used to detect the take-all disease levels. The spectral reflectance data of winter wheat acquired by an airborne imaging spectrometer during the filling period were used as input parameters of the model to obtain the chlorophyll content of the canopy. Finally, the results of the distribution of winter wheat take-all disease were mapped based on the relationship between take-all disease and the chlorophyll content of the canopy. The results showed that classification based on the deep learning model performed well for winter wheat take-all monitoring. This study can provide some reference basis for high-precision winter wheat take-all disease monitoring and can also provide some technical method references and ideas for remote sensing crop pest and disease remote sensing mapping. [ABSTRACT FROM AUTHOR]

Apples are ranked third, after bananas and oranges, in global fruit production. Fresh apples are more likely to be appreciated by consumers during the marketing process. However, apples inevitably suffer mechanical damage during transport, which can affect their economic performance. Therefore, the timely detection of apples with surface defects can effectively reduce economic losses. In this paper, we propose an apple surface defect detection method based on weight contrast transfer and the MobileNetV3 model. By means of an acquisition device, a thermal, infrared, and visible apple surface defect dataset is constructed. In addition, a model training strategy for weight contrast transfer is proposed in this paper. The MobileNetV3 model with weight comparison transfer (Weight Compare-MobileNetV3, WC-MobileNetV3) showed a 16% improvement in accuracy, 14.68% improvement in precision, 14.4% improvement in recall, and 15.39% improvement in F1-score. WC-MobileNetV3 compared to MobileNetV3 with fine-tuning improved accuracy by 2.4%, precision by 2.67%, recall by 2.42% and F1-score by 2.56% compared to the classical neural networks AlexNet, ResNet50, DenseNet169, and EfficientNetV2. The experimental results show that the WC-MobileNetV3 model adequately balances accuracy and detection time and achieves better performance. In summary, the proposed method achieves high accuracy for apple surface defect detection and can meet the demand of online apple grading. [ABSTRACT FROM AUTHOR]

Yield estimation using remote sensing data is a research priority in modern agriculture. The rapid and accurate estimation of winter wheat yields over large areas is an important prerequisite for food security policy formulation and implementation. In most county-level yield estimation processes, multiple input data are used for yield prediction as much as possible, however, in some regions, data are more difficult to obtain, so we used the single-leaf area index (LAI) as input data for the model for yield prediction. In this study, the effects of different time steps as well as the LAI time series on the estimation results were analyzed for the properties of long short-term memory (LSTM), and multiple machine learning methods were compared with yield estimation models constructed by the LSTM networks. The results show that the accuracy of the yield estimation results using LSTM did not show an increasing trend with the increasing step size and data volume, while the yield estimation results of the LSTM were generally better than those of conventional machine learning methods, with the best R2 and RMSE results of 0.87 and 522.3 kg/ha, respectively, in the comparison between predicted and actual yields. Although the use of LAI as a single input factor may cause yield uncertainty in some extreme years, it is a reliable and promising method for improving the yield estimation, which has important implications for crop yield forecasting, agricultural disaster monitoring, food trade policy, and food security early warning. [ABSTRACT FROM AUTHOR]

As a kind of important insect pest of cotton crops, aphids cause serious damage in cotton yields and quality worldwide, posing a significant risk to economic losses. Automatic detection of the pest damage level plays an important role in cotton field management. However, it is usually regarded as a classification problem in machine learning, where the disease severity levels are taken as independent categories and the inter-level relationship has not fully been considered. To utilize the inherited relations among different severity levels caused by cotton aphids, a novel approach based on the spectral index reconstruction was proposed in this study. First, six types of initial spectral indices were reconstructed based on healthy samples in the training set. Then, the severity sequences corresponding to the reconstructed initial spectral indices (RISIs) were sorted and compared with the ideal sequence. After attaining sequences most consistent with the ideal one, the ratio between the inter- and intra- levels was calculated to select the sensitive RISI. Moreover, the range of each severity level was established by the thresholds between adjacent grades of the selected sensitive RISI, which was finally used to determine the disease severity level caused by cotton aphids. Results of the cotton aphids showed that the proposed approach achieved a grading performance with OA = 0.944, AA = 0.900, and Kappa coefficient = 0.928. Hence, the proposed approach based on hyperspectral index reconstruction is effective and has potential application in grading the aphid infestation severity of cotton. [ABSTRACT FROM AUTHOR]

At this stage, with the continuous development of data acquisition technology, the mining of high-dimensional spatial data has become a hot issue. And so on. The spatial data mining component studied in this paper is part of the spatiotemporal data middleware. The main research result is to propose a density-based and grid-based extended adjacent spatial clustering method and apply this method to the space for agricultural informatition. In data mining, we also used the middleware technology to complete the agricultural geographic information system based on MapXtreme, and applied the spatial data mining for agricultural informatization to develop the middleware of agricultural variety division, which solved the agricultural informatization. The practical application is of our country. [ABSTRACT FROM AUTHOR]

Provable security theory generally adopts the method of reduction, which makes use of the unsolvable mathematical problems in number theory to reduce the scheme to be safe. The idea of proof is a method of proof by contradiction: First, it is assumed that it is not difficult to solve the scheme presented in this paper, then the process of proving it, and finally, it is deduced that it is not difficult to calculate a certain difficult problem, which contradicts the difficulty of the mathematical problem. Then, it means that the assumption is not valid, and the scheme is proved to be safe. In this paper, the security of the scheme proposed in our previous paper is proved in detail.the scheme is proved to be secure against existential forgery under selective attributes and adaptive chosen-message attack. Its security can be reduced to the hardness of the computational Diffie-Hellman problem. [ABSTRACT FROM AUTHOR]

Wheat take-all is a quarantine disease, which will lead to a disaster in wheat production without timely monitoring and management. Remote sensing technique, especially the field-based imaging spectrum technique, can achieve real-time monitoring of the disease development. For rapid extraction of take-all disease information, we try to monitor wheat take-all disease using imaging spectrometer. The experiment was carried out in Baisha village, Yuanyang County of China. We designed test of three concentration gradients and repeated three times, the experimental field was 30 m2. The wheat take-all white head rate was surveyed two weeks before harvest. The wheat's canopy spectrum was collected by two kinds of spectrometer, ASD Handheld non-imaging spectrometer (ASD Handheld, ASD Inc.) and Headwall imaging spectrometer (HyperSpec® VNIR, Headwall Photonics, Inc.). All data were collected between 10:00 to 13:00 in sunny days. In this study, based on gray association analysis (GAA) and support vector machine (SVM) classifier, a spectral feature extraction and classification method was proposed to separate the spectral features of the different take-all levels from spectral images. The field-based spectral images were acquired by Headwall imaging sensor. Meanwhile, the spectral data about different white head rate were collected by ASD HandHeld non-imaging sensor. Because of better accuracy and resolution, ASD spectral data had a better capacity to express the spectral features of take-all levels. These spectral features were extracted using kernel principle component analysis (K-PCA). Characteristic bands of the first four of principal component was mainly green band, red band and near infrared band, indicated in the spectrum curve, peak and valley phenomenon was the main distinguishing feature of white head rate and take-all disease grade. Then Jeffries-Matusita distances between feature bands were calculated, if Jeffries-Matusita distances between feature bands were greater than 1.8, the selected characteristic bands can distinguish different damage degree of wheat take-all disease. The spectral separability of take-all levels was tested and assessed by grey association analysis. Based on these significant features, some of Headwall imaging spectral data with different take-all levels were selected as the training data for the field-based spectral images. Through the SVM classifier based on RBF kernel function, a hyperspectral classification image of take-all was calculated. Results showed that the wheat take-all widely existed in the experimental zone, but its distribution had own specific characteristic with different disease levels. The slight disease wheat and the heavy disease wheat were mixture in the experimental zone. The distribution characteristics of serious take-all wheat disease (white head rate greater than 60%) were intensive and block. Slight wheat disease (white head rate between 10%-30%) were widely distributed in the middle of heavy wheat disease(white ear rate between 30%-60%), the proportion of slight wheat disease and heavy heat disease was 29.53% and 26.06%, respectively, very serious wheat take-all disease (white head rate between 60%-90%) and death of wheat disease showed regional distribution in the image, accounted for 10.73% and 19.91%.The overall accuracy of the classification was greater than 94% (Kappa>0.8). To further validate the classification accuracy, field experiment survey data was compared with the spectral classification, misclassification existed mainly in white head rate 30%~40%.These results proves the field-based imaging spectrum has the capacity to achieve the real-time monitoring and classification of wheat take-all condition, and to support the guidance on wheat production. [ABSTRACT FROM AUTHOR]