Identification of Pepper Leaf Diseases Based on TPSAO-AMWNet.

Pepper is a high-economic-value agricultural crop that faces diverse disease challenges such as blight and anthracnose. These diseases not only reduce the yield of pepper but, in severe cases, can also cause significant economic losses and threaten food security. The timely and accurate identification of pepper diseases is crucial. Image recognition technology plays a key role in this aspect by automating and efficiently identifying pepper diseases, helping agricultural workers to adopt and implement effective control strategies, alleviating the impact of diseases, and being of great importance for improving agricultural production efficiency and promoting sustainable agricultural development. In response to issues such as edge-blurring and the extraction of minute features in pepper disease image recognition, as well as the difficulty in determining the optimal learning rate during the training process of traditional pepper disease identification networks, a new pepper disease recognition model based on the TPSAO-AMWNet is proposed. First, an Adaptive Residual Pyramid Convolution (ARPC) structure combined with a Squeeze-and-Excitation (SE) module is proposed to solve the problem of edge-blurring by utilizing adaptivity and channel attention; secondly, to address the issue of micro-feature extraction, Minor Triplet Disease Focus Attention (MTDFA) is proposed to enhance the capture of local details of pepper leaf disease features while maintaining attention to global features, reducing interference from irrelevant regions; then, a mixed loss function combining Weighted Focal Loss and L2 regularization (WfrLoss) is introduced to refine the learning strategy during dataset processing, enhancing the model's performance and generalization capabilities while preventing overfitting. Subsequently, to tackle the challenge of determining the optimal learning rate, the tent particle snow ablation optimizer (TPSAO) is developed to accurately identify the most effective learning rate. The TPSAO-AMWNet model, trained on our custom datasets, is evaluated against other existing methods. The model attains an average accuracy of 93.52% and an F1 score of 93.15%, demonstrating robust effectiveness and practicality in classifying pepper diseases. These results also offer valuable insights for disease detection in various other crops.

Characterization of rhizobia isolated from legume species within the genera Astragalus and Lespedeza grown in the Loess Plateau of China and description of Rhizobium loessense sp. nov.

Twenty-nine rhizobial isolates from root nodules of Astragalus and Lespedeza spp. growing in the Loess Plateau of China were characterized by numerical taxonomy, RFLP and sequencing of PCR-amplified 16S rRNA genes, measurement of DNA G+C content, DNA-DNA relatedness and cross-nodulation with selected legume species. Based on the results of numerical taxonomy, the isolates formed two clusters (1 and 2) with some single isolates at a similarity level of 82 %. Cluster 1 contained six isolates from Astragalus and Lespedeza spp. Cluster 2 consisted of nine isolates from Astragalus spp. DNA relatedness was greater than 80 % among isolates within cluster 2. Phylogenetic analysis based on 16S rRNA gene sequences showed that CCBAU 7190B(T), representing cluster 2, was closely related to Rhizobium galegae and Rhizobium huautlense. DNA-DNA relatedness between CCBAU 7190B(T) and reference strains of R. galegae, R. huautlense and other related species ranged from 0 to 48.6 %. The cluster 2 isolates could also be differentiated phenotypically from related species. Based on these data, a novel species, Rhizobium loessense sp. nov., is proposed for cluster 2, with the type strain CCBAU 7190B(T) (=AS1.3401(T)=LMG 21975(T)).

Rhizobium indigoferae sp. nov. and Sinorhizobium kummerowiae sp. nov., respectively isolated from Indigofera spp. and Kummerowia stipulacea.

Forty-eight rhizobial isolates from root nodules of Indigofera and Kummerowia, two genera of annual or perennial wild legumes growing in the Loess Plateau in north-western China, were characterized by a polyphasic approach. Two main groups, cluster 1 and cluster 2, were defined based upon the results of numerical taxonomy, SDS-PAGE of whole-cell proteins and DNA relatedness. All the isolates within cluster 1 were isolated from Indigofera and they were identified as Rhizobium strains by 16S rRNA gene analysis. DNA relatedness of 29.5-48.9% was obtained among the cluster 1 isolates and the reference strains for defined Rhizobium species. Cluster 2 consisted of isolates from Kummerowia stipulacea and was identified as belonging to Sinorhizobium by 16S rRNA gene analyses. DNA relatedness varied from 5.2 to 41.7% among the isolates of cluster 2 and reference strains for Sinorhizobium species. Considering the existence of distinctive features among these two groups and related species within the genera Rhizobium and Sinorhizobium, we propose two novel species, Rhizobium indigoferae sp. nov. for cluster 1, with isolate CCBAU 71714(T) (= AS 1.3046(T)) as the type strain, and Sinorhizobium kummerowiae sp. nov. for cluster 2, with isolate CCBAU 71042(T) (= AS 1.3045(T)) as the type strain.