Smart agricultural machinery is built upon traditional agricultural equipment, further integrating modern information technologies to achieve automation, precision, and intelligence in agricultural production. Currently, significant progress has been made in the autonomous operation and monitoring technologies of smart agricultural machinery in China. However, challenges remain, including poor adaptability to complex environments, high equipment costs, and issues with system implementation and standardization integration. To help industry professionals quickly understand the current state and promote the rapid development of smart agricultural machinery, this paper provides an overview of the key technologies related to autonomous operation and monitoring in China’s smart agricultural equipment. These technologies include environmental perception, positioning and navigation, autonomous operation and path planning, agricultural machinery status monitoring and fault diagnosis, and field operation monitoring. Each of these key technologies is discussed in depth with examples and analyses. On this basis, the paper analyzes the main challenges faced by the development of autonomous operation and monitoring technologies in China’s smart agricultural machinery sector. Furthermore, it explores the future directions for the development of autonomous operation and monitoring technologies in smart agricultural machinery. This research is of great importance for promoting the transition of China’s agricultural production towards automation and intelligence, improving agricultural production efficiency, and reducing reliance on human labor.

The kale crop is an important bulk vegetable, and automatic segmentation to recognize kale is fundamental for effective field management. However, complex backgrounds and texture-rich edge details make fine segmentation of kale difficult. To this end, we constructed a kale dataset in a real field scenario and proposed an UperNet semantic segmentation model with a Swin transformer as the backbone network and improved the model according to the growth characteristics of kale. Firstly, a channel attention module (CAM) is introduced into the Swin transformer module to improve the representation ability of the network and enhance the extraction of kale outer leaf and leaf bulb information; secondly, the extraction accuracy of kale target edges is improved in the decoding part by designing an attention refinement module (ARM); lastly, the uneven distribution of classes is solved by modifying the optimizer and loss function to solve the class distribution problem. The experimental results show that the improved model in this paper has excellent performance in feature extraction, and the average intersection and merger ratio (mIOU) of the improved kale segmentation can be up to 91.2%, and the average pixel accuracy (mPA) can be up to 95.2%, which is 2.1 percentage points and 4.7 percentage points higher than the original UperNet model, respectively, and it effectively improves the segmentation recognition of kale.

Scientific and accurate agronomic knowledge is key to ensuring efficient wheat production. China’s vast agricultural land spans a wide range of longitudes and latitudes, and agronomic practices are closely tied to temporal factors such as wheat growth stages. So agronomic knowledge exhibits significant spatiotemporal variability. Constructing a spatiotemporal knowledge graph of wheat production can offer multi-dimensional data support and enabling deeper knowledge services. Wheat agronomic knowledge is often fragmented and unstructured and efficiently extracting text segments of agronomic knowledge and agronomic knowledge triples are two key challenges. Because of the high proportion and significant production service value of attribute values in agronomic knowledge, an attribute-rich agronomic knowledge graph schema was created. According to the characteristics of agronomic texts, a keyword attention mechanism (KAM) was proposed and integrated with an improved BERT model for sentence-level feature extraction to create an extraction model AgronomicCorpusExtraction for agronomic knowledge text corpora. The agronomic knowledge of wheat production is characterized by non-standard syntax, complex multi-layer structures, diverse entity expression methods, and a wide span of scope, and existing extraction methods cannot achieve satisfactory results. To address the issue, a joint extraction model AgronomicTripleExtraction was proposed to extract entities, attributes, and relations in different phrases, firstly the BERT and BiGRU were used jointly to extract the long and short distance features, and the CRF was used by global normalization joint modeling to extract attributes, then intermediate features between the same type of attributes extracted by average pooling to segment different entities. At last, a relation-aware relation feature enhancement (RAFE) method was created and a MLP was used to extract relations based on the relation matrix constructed from the knowledge graph schema. Ablation experiments were conducted to evaluate the performance for AgronomicCorpusExtraction with and without KAM and that for AgronomicTripleExtraction under four conditions, the model with BiGRU, RAFE, and entity segment, without BiGRU, without RAFF, and without entity segment. The results indicate that the use of KAM improves F1-score by 0.128 and AgronomicTripleExtraction achieves F1 of 0.897, 0.875, 0.871 for attribute, entity and relation extraction when using the three modules simultaneously, and removing any single module leads to a certain degree of performance degradation. Comparative experiments were conducted between AgronomicTripleExtraction and some related state-of-the-art models published recently.