## 1 Introduction  
As the process of informatization in agriculture continues to advance, increasingly more intelligent applications and services have emerged, such as fruit-picking robots, automatic plant disease detection applications [1, 2, 3], remote sensing field image processing [4], etc. The way to answer the questions of agricultural workers in production has gradually shifted from offline consultation to online. Machine reading comprehension (MRC) is a text-based question-and-answer task (Text-QA), and is a promising research direction in the field of artificial intelligence and natural language processing. It enables computers to help humans find the desired answers in a large amount of text, providing efficient and convenient services to users, thus reducing the cost of people's access to information. Based on machine reading comprehension to realize automatic question-answering of agricultural scenes, it can improve the processing efficiency of data and facilitate the subsequent research related to the question-answering (QA) system, which is of great significance to the informatization of the agricultural field. Currently, machine learning is widely used in various fields. Making machines accomplish reading comprehension and question-answering is currently a rather hot topic at the forefront of the artificial intelligence community, mainly involving deep learning, natural language processing, and information retrieval. Previous researchers have optimized different aspects of machine reading comprehension techniques to achieve breakthroughs. [PERSON] et al. [5] introduced COSMOS QA, a large-scale dataset containing 35,600 reading comprehension questions that require common sense. The dataset is expressed in a multiple-choice format. It focuses on various collections of people's everyday narratives. They experimented with several state-of-the-art neural architectures and proposed a new architecture that can improve the competing baselines. Finally, the baseline performance of COSMOS QA was established. [PERSON] et al. (2019) investigated the potential of using external knowledge bases (KBs) to further improve the BERT model of MRC. They introduced a self-adaptive KT-NET structure to select the desired knowledge from KBs, then fused it with BERT to achieve contextual and knowledge-aware prediction. The KT-NET provided consistent and significant improvements over BERT, outperforming competing benchmarks on the ReCoRD and squadron 1.1 benchmarks. [PERSON] et al. (2019) applied syntax to instruct the text modeling by blending syntactic constraints into the attention mechanism. They introduced syntactic dependency of interest into the self-attention network to establish an SDOI-SAN with syntax-guided self-attention. This design can possess better linguistically motivated word representations. Ample experiments demonstrated that the proposed SG-Net facilitates powerful performance. These high-performance machine reading comprehension techniques have also been used in all kinds of domains to meet different needs. [PERSON] et al. (2019) suggested using MEC in state tracking from datasets and model architectures. This innovation solved the challenge that existing methods require conversation data with state information and have limited ability to generalize to unknown domains. [PERSON] et al. (2019) introduced a new visual machine reading comprehension dataset called VisualMRC. In this dataset, the machine will read and understand the given text in the image to answer the question in natural language. VisualMRC focuses more on developing natural language comprehension and generation capabilities. Experiments show that VisualMRC outperforms the basic sequence-to-sequence model and the state-of-the-art VQA model. However, there are problems, such as a small corpus, a narrow range of corpus contents, or the need for manual annotation, in the study of agricultural question classification. Agricultural practitioners prefer the web to the traditional way of consulting experts on agricultural production techniques.