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Autonomous navigation is an important guarantee for intelligent agricultural machinery to complete harvesting operations. Navigation line recognition is an important step in achieving autonomous navigation of intelligent agricultural machinery. Real-time and accurate detection of harvesting navigation lines can effectively accelerate the progress of operations and reduce crop loss rates. Visual sensors have the advantages of low cost and rich information acquisition, making them widely used in route detection. Frequent changes in lighting under cloudy weather make it difficult for a single feature to cope with wheat field environments. In this study, a wheat harvesting boundary detection method based on multi texture feature fusion was proposed. In the harvested area, the characteristics of low stubble and no top wheat ears result in differences in uniformity, density, and other aspects when harvesting under both light and backlight compared to the non-harvested area. At the same time, due to the exposed wheat stubble in the harvested area, it appears more regular on the image, and its gradient direction is more consistent than that in the non-harvested area. Therefore, a two-dimensional feature vector composed of image entropy and directional gradient was constructed to classify the harvested and unharvested areas of the wheat field, and then the harvesting boundary was extracted. Due to the susceptibility of image entropy features to perspective phenomena, this paper compared the entropy features of different regions in the image when fixing the camera installation angle and height, and ultimately selected the middle region of the image as the region of interest. After analyzing the characteristics of image entropy feature extraction, a histogram statistical method based on sliding windows was proposed to accelerate the speed of image entropy feature extraction. By further dividing the window to be calculated into several sub windows, sliding and combining these sub windows significantly reduced the computation. The entropy feature extraction in this paper took 0.53 seconds, which is 49.52% faster than the traditional method of directly extracting entropy from the entire window. After extracting twodimensional features, based on the distribution characteristics of the feature histogram and combined with the maximum entropy threshold segmentation algorithm, the wheat field image was preliminarily classified. For entropy features, low noise points affected the threshold segmentation effect. By comparing the impact of discarding different proportions of low entropy points on the segmentation threshold, this paper discarded 10% of low entropy points, enhancing the threshold segmentation effect. The method of removing small connected regions was used to eliminate the misclassified regions in the initial classification binary image. Then, the Canny operator was used to extract edge contour points distributed near the harvesting boundary. Due to the consistent direction between the harvesting boundary and the agricultural machinery's forward direction, the Ransac algorithm was used to restrict the fitting line in the region, obtaining the accurate harvesting boundary. In order to verify the feasibility of the method proposed in this article, wheat field images under different lighting conditions were collected, including 2 200 weak light images, 758 local strong light images, and 1 134 strong light images. After selecting the region of interest, the size of the image to be processed was 240 pixels×1 280 pixels. The algorithm in this paper took an average of 0.88 s to process each image on laptops. The detection rates under weak light, local strong light, and strong light were 90.41%, 88.26%, and 89.68%, respectively, with an average of 89.45%. Compared with the traditional method of using Adaboost algorithm for machine learning, the detection speed of this algorithm was improved by 73.89%, and the detection rate was increased by 46.19 percentage points, 46.00 percentage points, and 49.64 percentage points under weak light, local strong light, and strong light, respectively. Compared with traditional algorithms, the method proposed in this paper significantly improved real-time performance and detection rate, which can provide reference for the detection of field agricultural machinery navigation lines. [ABSTRACT FROM AUTHOR]

Agricultural named entity recognition is a fundamental task for information extraction in the agricultural domain. Aiming at the problems of local context features、unable to solve the polysemy of the word、low recognition rate of rare entities in the process of entity recognition, the model combined with character level features and dictionary feature was proposed to automatically identify entities in the text，the character level features were obtained from the BERT(Bidirectional Encoder Representations from Transformers)model. Firstly, the BERT pre-trained language model was used to integrate the left and right contextual information to obtain the character level features, enhance the semantic representation of words, in order to alleviate the problem of polysemy; Secondly, we built an agricultural dictionary and introduced external dictionary information through the feature extraction strategy to improve the recognition accuracy of the model for rare or unknown entities. Among them, two feature extraction strategies were designed to capture the dictionary features, included N-gram feature template algorithm and bi-direction maximum matching algorithm. Then, the character level features and dictionary features were fused as the input of the next neural network layer. Finally, the fused feature information were encoded by the BiLSTM (Bi-directional Long-short Term Memory) neural network layer, obtained the sequence feature matrix, and the optimal text label sequence was obtained by CRF (Conditional Random Field). Based on the knowledge of domain experts, a labeling strategy of named entities in the agricultural field was proposed to solve the problem of fuzzy boundaries of agricultural named entities, in order to ensure the integrity of the entities. The experiments were carried out on the corpus of agricultural, which contained 5 295 labeled corpora and 5 categories of agricultural entities. The results showed that better overall performance was achieved in the corpus, where the recognition precision, recall, and F1-score were 94.84%, 95.23%, and 95.03%, respectively. In terms of specific categories, due to the obvious boundary characteristics of crop diseases and pesticide, the model achieved higher recognition precision than the remaining three entities of agricultural, such as machinery, pests, and crop variety. Experimental comparison showed that for the effectiveness of the dictionary feature extraction strategy, the performance of the model based on the bi-direction maximum matching algorithm was better than the N-gram feature template algorithm. When the number of templates was 10, the performance of the model based on N-gram feature template was the best with the recognition precision of93.95%and F1-score of 94.03%. The bi-directional maximum matching algorithm using feature embedding can obtain more potential information, which was better than one-hot encoding. The precision and F1-score of the model were improved by 0.49 and 0.91 percentage points, respectively. Compared with the models based on BiLSTM-CRF, BERT-BiLSTM-CRF, the precision of the BERT-Dic-BiLSTM-CRF model proposed in this paper had obvious performance advantages with the highest recognition precision of 94.84%. Compared with the BERT-BiLSTM-CRF model, for the recognition performance of rare or unknown entities, the recognition precision of the BERT-Dic-BiLSTM-CRF model was improved by 5.93 and 6.44 percentage points, respectively. Further verifying that the integration of dictionary features into the model can improve the recognition accuracy of the model for such entities. [ABSTRACT FROM AUTHOR]

High-quality optical data is difficult to acquire in southwest China, due to the cloudy and rainy weather. The synthetic aperture radar (SAR) can work for all-time and all-weather, but the revisit period is usually long. Therefore, it is a high demand for the synergy of optical and radar data rather than a single remote sensing data source, particularly for the survey and assessment on the land resource, as well as the global change. This study performed an object-oriented guided segmentation on the difference images generated from 2016 and 2019 quad-pol Radarsat-2 data using the 2019 Sentinel-2 optical data of Meishan region. A Fractal Net Evolution Approach (FNEA) was first utilized to segment the 2016 optical image for an initial segmentation. Then, the second segmentation was implemented using the same approach, where the features of the SAR image were provided for the purpose of guided segmentation. As such, the regions of growth were merged from small to large, in order to eventually form a complete geographical object. A final segmentation was obtained to select the changed and unchanged samples. More importantly, an object contained both changed and unchanged pixels, particularly with a single type of pixel inside the samples. Then, the object-level multidimensional features were extracted using the samples. The initial radar parameters included the backward scattering coefficient, Pauli and Freeman-Durden polarization decomposition parameters. The segmentation was utilized to generate the geographic objects. Meanwhile, some parameters were calculated using the initial parameters, including the mean, and standard deviation, whereas, the texture feature parameters using the Gray-level co-occurrence matrix (GLCM) included the mean, standard deviation, homogeneity, contrast, entropy, and the correlation for a total of 72-dimensional features. The redundant feature dataset was filtered to select the extracted feature set. Consequently, an FSO-RF change detection framework was proposed to achieve the feature optimization and the acquisition of the final change detection. The inter-sample distance metric learning was used to optimize the separability of feature space. The optimal features were used to calculate the distance between the changed and unchanged samples in different features. Finally, the change detection task was implemented using an object-oriented random forest classifier. The existing change detection pixel-based methods were selected to verify the improved model, including the change vector analysis (CVA), principal component analysis (PCA), multivariate alteration detection (MAD), Iteration multivariate alteration detection (IR-MAD), support vector machines (SVM), and random forest (RF) change detection using radar difference image segmentation. The change detection framework showed a remarkable improvement, with an overall accuracy of 92.90% in the test area. Two representative areas were selected in the study area for sampling and validation. The experiments demonstrated that better performance was achieved than that of the traditional, although the accuracy indexes were degraded in some areas with the complex features. The overall accuracies of the two sample areas reached 95.08 % and 88.16%. The algorithm in this paper can well meet the needs of detecting changes in different feature categories such as towns and farmlands, and has certain application value in the monitoring of national land resources. [ABSTRACT FROM AUTHOR]