The knowledge within wheat production chain data has multiple levels and complex semantic relationships, making it difficult to extract knowledge from them. Therefore, this paper proposes a fine-grained knowledge extraction method for the wheat production chain based on ontology. For the first time, the conceptual layers of ploughing, planting, managing, and harvesting were defined around the main agricultural activities of the wheat production chain. Based on this, the entities, relationships, and attributes in the conceptual layers were defined at a fine-grained level, and a spatial–temporal association pattern layer with four conceptual layers, twenty-eight entities, and forty-two relationships was constructed. Then, based on the characteristics of the self-constructed dataset, the Word2vec-BiLSTM-CRF model was designed for extracting the knowledge within it, i.e., the entity–relationship–attribute model and the Word2vec-BiLSTM-CRF model in this paper were compared with the four SOTA models. The results show that the accuracy and F1 value improved by 8.44% and 8.89%, respectively, compared with the BiLSTM-CRF model. Furthermore, the entities of the pest and disease dataset were divided into two different granularities for the comparison experiment; the results show that for entities with “disease names” and “pest names”, the recognition accuracy at the fine-grained level is improved by 32.71% and 31.58%, respectively, compared to the coarse-grained level, and the recognition performance of various fine-grained entities has been improved.

The extraction of entities and relationships is a crucial task in the field of natural language processing (NLP). However, existing models for this task often rely heavily on a substantial amount of labeled data, which not only consumes time and labor but also hinders the development of downstream tasks. Therefore, with a focus on enhancing the model’s ability to learn from small samples, this paper proposes an entity and relationship extraction method based on the Universal Information Extraction (UIE) model. The core of the approach is the design of a specialized prompt template and schema on cotton pests and diseases as one of the main inputs to the UIE, which, under its guided fine-tuning, enables the model to subdivide the entity and relationship in the corpus. As a result, the UIE-base model achieves an accuracy of 86.5% with only 40 labeled training samples, which really solves the problem of the existing models that require a large amount of manually labeled training data for knowledge extraction. To verify the generalization ability of the model in this paper, experiments are designed to compare the model with four classical models, such as the Bert-BiLSTM-CRF. The experimental results show that the F1 value on the self-built cotton data set is 1.4% higher than that of the Bert-BiLSTM-CRF model, and the F1 value on the public data set is 2.5% higher than that of the Bert-BiLSTM-CRF model. Furthermore, experiments are designed to verify that the UIE-base model has the best small-sample learning performance when the number of samples is 40. This paper provides an effective method for small-sample knowledge extraction.

The free-living infective juveniles of entomopathogenic nematodes (EPNs) are critical biological control agents against insect pests. The field efficacy of EPNs is largely determined by their tolerance to low-humidity stress, a trait closely linked to the mobilization of their energy reserves. This study aims to investigate how varying levels of humidity stress influence energy reserve dynamics in two EPNs, Steinernema kraussei 0657L and Heterorhabditis brevicaudis 0641TY, and their relationship with the survival rate and pathogenicity. The results demonstrated that lipids were the predominant energy reserve, followed by proteins and sugars. Notably, neutral lipid constituted approximately 3% of the total lipid content. Among sugars, soluble sugar levels were the highest, followed by glycogen and trehalose. Exposure to low-humidity stress resulted in increased levels of protein, total lipid, glycogen, and trehalose in nematodes. These increases were more pronounced in S. kraussei 0657L, a highly drought-resistant strain, compared to H. brevicaudis 0641TY. Furthermore, the accumulation of protein, total lipid, and trehalose was negatively correlated with survival rate and pathogenicity. However, positive correlations were observed between trehalose and both total lipids and soluble sugars. Furthermore, transcriptome analysis revealed that under low-humidity stress, S. kraussei 0657L exhibited an enrichment of differentially expressed genes (DEGs) involved in glycolysis/gluconeogenesis, fatty acid metabolism, and glycerophospholipid metabolism pathways. This indicates that S. kraussei 0657L regulated energy metabolism to adapt to low-humidity stress. These findings provide insights into the mechanisms underlying drought resistance in EPNs and offer an experimental basis for their application in arid environments.

Entomopathogenic nematodes (EPNs) are among the most promising biocontrol agents; however, their short shelf life constrains commercial application. This study evaluated the effects of storage temperature (6 °C and 25 °C) and sponge substrate moisture content (42%, 48%, and 55%) on the 18-week survival and post-storage virulence of two indigenous EPN species from Gansu Province, China: Heterorhabditis megidis 0627M and Steinernema feltiae 0619HT. A Generalized Linear Mixed Model (GLMM) revealed that storage duration, temperature, moisture content, and species all significantly affected survival of H. megidis 0627M and S. feltiae 0619HT (all p < 0.001). Low-temperature storage (6 °C) reduced mortality odds by 82.4% compared with room temperature (OR = 0.176, 95% CI: 0.117–0.263, p < 0.001). High moisture content (55%) increased mortality odds by 10.1-fold relative to moderate moisture (48%; OR = 10.114, 95% CI: 6.155–16.618, p < 0.001), whereas low moisture (42%) showed no significant difference from 48% (OR = 0.942, p = 0.810). The two species exhibited distinct adaptation strategies: S. feltiae 0619HT achieved the highest survival under low-temperature storage at 48% moisture content (56.48% at week 18), whereas H. megidis 0627M demonstrated a delayed competitive advantage under room-temperature, low-moisture conditions. Virulence assays revealed that low-temperature storage better preserved infectivity under most conditions. Notably, survival and virulence were not always concordant, necessitating their evaluation as complementary metrics. Species-specific storage protocols are proposed, providing a scientific basis for the future development of regionally targeted and cost-effective native EPN formulations, as well as for regionally targeted biocontrol applications.

The cotton bollworm, Helicoverpa armigera, is a major polyphagous agricultural pest that is notoriously difficult to control due to its broad host range, scattered oviposition, larval boring behavior, and strong resistance to both chemical insecticides and Bt proteins. Melatonin, an endogenous hormone ubiquitously present in living organisms, plays critical regulatory roles in various physiological processes, including metabolism, immunity, stress responses, and reproduction. Moreover, numerous studies have demonstrated that exogenous melatonin plays an important role in helping plants defend against diverse environmental stresses. In the present study, an artificial diet supplementation method was employed to investigate the effects of different concentrations of exogenous melatonin on the growth, development, and reproduction of H. armigera, with the aim of evaluating its potential application in integrated pest management (IPM). Larvae were treated with melatonin at concentrations of 0, 0.02, 0.2, 2, and 20 μg/g. The results showed that with increasing melatonin concentration, larval developmental duration was significantly prolonged, whereas pupal duration was significantly shortened (p < 0.05). At 20 μg/g, the longest larval duration (16.90 d) was recorded, representing an extension of 2.69 d compared with the control, while pupal duration (8.04 d) was reduced by 2.16 d relative to the control. Furthermore, higher melatonin concentrations significantly decreased larval head capsule width, larval body and pupal weight, and also significantly shortened adult longevity and oviposition period, while markedly reducing both total fecundity and daily fecundity per female. At 20 μg/g, the mean total fecundity per female reached its minimum (224.13 eggs), representing a reduction of 62.81% compared with the control. Collectively, these findings demonstrate that exogenous melatonin can significantly inhibit the growth, development, and reproduction of H. armigera, highlighting its potential application as a biorational tool in crop protection and integrated pest management.