The application of deep learning (DL) technology to the identification of crop growth processes will become the trend of smart agriculture. However, using DL to identify wheat growth stages on mobile devices requires high battery energy consumption, significantly reducing the device’s operating time. However, implementing a DL framework on a remote server may result in low-quality service and delays in the wireless network. Thus, the DL method should be suitable for detecting wheat growth stages and implementable on mobile devices. A lightweight DL-based wheat growth stage detection model with low computational complexity and a computing time delay is proposed; aiming at the shortcomings of high energy consumption and a long computing time, a wheat growth period recognition model and dynamic migration algorithm based on deep reinforcement learning is proposed. The experimental results show that the proposed dynamic migration algorithm has 128.4% lower energy consumption and 121.2% higher efficiency than the local implementation at a wireless network data transmission rate of 0–8 MB/s.

Autonomous driving technology for agricultural machinery can maximise crop yield, reduce labour costs, and alleviate labour intensity. In response to the current low degree of automation and low tracking accuracy of driving paths in agricultural equipment, this research proposes an unmanned agricultural machinery operating system based on an improved fuzzy adaptive PD control algorithm. Firstly, mechanical kinematic models and fuzzy adaptive control algorithms are introduced to achieve autonomous driving, and parameter settings and speed adjustments are made while considering errors. Secondly, in the autonomous driving operation system, taking a certain rice machine as an example, perception information, trajectory design, dynamic control, operation supervision, and remote control design are carried out. The experimental results show that the improved fuzzy algorithm exhibits smaller deviation results in driving path tracking, with an average error between the actual path and the expected path of less than 0.001 m. In different testing scenarios, compared with the actual control results, the maximum deviation of the control system platform in straight sections is less than 2.8 m, which is more stable. More than 95% of the lateral deviation results in the road sections are within 0.11 m. And the tracking distance error of the proposed method in the straight and curved segments is relatively small, far smaller than other comparative algorithms. The unmanned agricultural machinery operation system proposed in this study can significantly improve the efficiency and accuracy of agricultural machinery work, promote the development of intelligent and modern agricultural machinery, and provide reference value and important contributions to social and economic development as well as the progress and promotion of related technologies.

The certification information of wheat varieties contains valuable breeding knowledge and plays an important role in germplasm resource management and breeding research. However, most certification information is stored in unstructured text form, making it difficult to support efficient knowledge acquisition and utilization. To address this issue, this paper investigates joint entity-relation extraction from wheat variety certification texts and its application to knowledge graph construction. Specifically, building on existing character-word fusion methods, we propose the Joint Entity-Relation Extraction for Wheat Variety Certification Texts (JERE-WVCT) to address indiscriminate incorporation of candidate features in character-word fusion that obscures key features, a lack of differentiated weight assignment for features, and severe imbalance across relation types. Within JERE-WVCT, a deep character-word fusion mechanism based on hierarchical filtering and ranking is designed to enhance the representation of domain-specific entities. In addition, relation labels are incorporated as prior knowledge to alleviate the impact of relation type imbalance and improve the model’s triple extraction capability. Experimental results show that JERE-WVCT achieves an F1 score of 96.78% on the wheat variety certification corpus, outperforming all baseline models. Based on the extracted triples, a wheat variety knowledge graph is constructed, and exploratory variety relationship analysis is conducted as a downstream application. The results demonstrate the effectiveness of the proposed model for structured knowledge acquisition and support graph-based exploration of wheat variety information.