Intent detection and slot filling have been two of the most vital tasks to question the crop diseases and pests using knowledge graph. Among them, intent detection is to determine the user's intention or purpose from the text entered by the user in the crop pest field. Slot filling is to identify and extract specific information from the user's text, especially for the slot, for example, "what is the control of tomato bacterial wilt?". The task identifies the semantic slot "tomato bacterial wilt" (head entity) and the intention "control " (relation) to form a triple (tomato bacterial wilt, control, -). Where"-" represents the semantic slot and the intention to find the answer from the knowledge graph. A graph convolution encoder has been proposed to capture the spatiotemporal semantic features of conversation discourse in recent years, in order to learn the co-occurrence relationship between intention detection and slot filling. The slot decoding context has improved the accuracy of intention detection. In turn, the target of intention detection can be optimized to further improve the performance of slot filling. The best performance has been achieved in the experiments on public data sets. Sub word attention adapter (SAA) has also been used to effectively extract the context features from complex tags, and then retain the overall discourse information. In addition, an intention attention adapter (IAA) has been proposed to obtain the comprehensive sentence features, in order to predict the users’ intention. In experiments, the knowledge question and answer have been focused mainly on the pipeline in the existing agricultural research. The named entity recognition has been firstly used to identify the head entity in the question. The text classification can be then used to classify the question for the intention. However, such a pipeline can be vulnerable to error propagation that caused by word segmentation errors. It is very necessary to make full use of the correlation between intent detection and slot filling in the agricultural field. In this study, a joint model of intent detection and slot filling was proposed to fully utilize the semantic information using CNN-Transformer (CDPCT-IDSF). According to the semantic complexity of crop pest text, the CNN network and multi-layer Transformer were designed to emphasize the local useful information. The semantic loss was alleviated to introduce the alignment guarantee one-to-one relationship between input and output in the Transformer decoder. As such, the model was improved to identify the correct slot labels. In addition, a question-and-answer dataset was constructed for the agricultural pest knowledge, including 20 intention categories, 12 slot categories, and 11242 labeled samples. Comparative experiments show that the slot-filling F1 value of the CDPCT-IDSF model on the corpus was 94.36%, the intent detection accuracy was 92.99%, and the overall recognition accuracy was 87.23%, which was better than other comparison models. The improved model was also performed better for the intent detection and slot filling in crop diseases and pests. The finding can provide the theoretical support to knowledge question and answer research on crop diseases and pests. Moreover, the experiments were also carried out on two public datasets. CDPCT-IDSF was also suitable for English corpus scenarios. On ATIS datasets, the slot filling F1 value was 95.43%, intent detection accuracy was 97.95%, and overall recognition accuracy was 87.68%. On SNIPS datasets, the slot filling F1 value, intent detection accuracy and overall recognition accuracy of CDPCT-IDSF were 95.70%, 98.57% and 89.96%, respectively. At the same time, the better generalization and robustness were achieved in the rest corpus.

The new shoot density of slash pine serves as a vital indicator for assessing its growth and photosynthetic capacity, while the number of new shoots offers an intuitive reflection of this density. With deep learning methods becoming increasingly popular, automated counting of new shoots has greatly improved in recent years but is still limited by tedious and expensive data collection and labeling. To resolve these issues, this paper proposes a semi-supervised counting network (MTSC-Net) for estimating the number of slash pine new shoots. First, based on the mean-teacher framework, we introduce the improved VGG19 to extract multiscale new shoot features. Second, to connect local new shoot feature information with global channel features, attention feature fusion module is introduced to achieve effective feature fusion. Finally, the new shoot density map and density probability distribution are processed in a fine-grained manner through multiscale dilated convolution of the regression head and classification head. In addition, a masked image modeling strategy is introduced to encourage the contextual understanding of global new shoot features and improve the counting performance. The experimental results show that MTSC-Net outperforms other semi-supervised counting models with labeled percentages ranging from 5% to 50%. When the labeled percentage is 5%, the mean absolute error and root mean square error are 17.71 and 25.49, respectively. These findings demonstrate that our work can be used as an efficient semi-supervised counting method to provide automated support for tree breeding and genetic utilization.