Accurately detecting and identifying granary pests is important in effectively controlling damage to a granary, ensuring food security scientifically and efficiently. In this paper, multi-scale images of seven common granary pests were collected. The dataset had 5231 images acquired with DSLR-shot, microscope, cell phone and online crawler. Each image contains different species of granary pests in a different background. In this paper, we designed a multi-scale granary pest recognition model, using the YOLOv5 (You Look Only Once version 5) object detection algorithm incorporating bidirectional feature pyramid network (BiFPN) with distance intersection over union, non-maximum suppression (DIOU_NMS) and efficient channel attention (ECA) modules. In addition, we compared the performance of the different models established with Efficientdet, Faster rcnn, Retinanet, SSD, YOLOx, YOLOv3, YOLOv4 and YOLOv5s, and we designed improved YOLOv5s on this dataset. The results show that the average accuracy of the model we designed for seven common pests reached 98.2%, which is the most accurate model among those identified in this paper. For further detecting the robustness of the proposed model, ablation analysis was conducted. Furthermore, the results show that the average accuracy of models established using the YOLOv5s network model combined with the attention mechanism was 96.9%. When replacing the model of PANet with BiFPN, the average accuracy reached 97.2%. At the same time, feature visualization was analyzed. The results show that the proposed model is good for capturing features of pests. The results of the model have good practical significance for the recognition of multi-scale granary pests.

Sap flow is widely used to estimate the transpiration and water consumption of canopies and to manage water resources. In this paper, an improved time series prediction model was proposed by integrating three basic networks—CNN, GRU and BiLSTM—to assess sap flow with historical environment variables. A dataset with 17,569 records of each, including 9 environment variables and 1 sap flow, was applied from a public database of SAPFLUXNET. After normalization, the environment variables were analyzed and composed with the factor analysis method. After the CNN-GRU-BiLSTM structure was designed, N records of three main factors were computed from environment variables, which were measured at N previous moments, and the sap flow was measured at the current moment, and they were applied for each training, validation, and testing cycle. To improve and compare the CNN-GRU-BiLSTM-based model, nine other models, using the methods of multiple linear regression, support vector regression, random forest, LSTM, GRU, BiLSTM, CNN-GRU, CNN-BiLSTM, and CNN-GRU-LSTM, were constructed in this study, respectively. Results show that the performance of the CNN-GRU-BiLSTM-based model has more accuracy than the other nine models we built in this paper, with the mean absolute error, mean squared error, mean absolute percentage error, and coefficient of determination (R²) being 0.0410, 0.0029, 0.2708 and 0.9329, respectively. Furthermore, for a comparison of the descending dimension method of factor analysis, principal component analysis (PCA) and singular value decomposition (SVD) methods were applied and compared, respectively. Results show that the performance of the factor analysis-based model is better than the PCA- or SVD-based model, with the R² results of the factor analysis-based model being higher than the PCA- and SVD-based models by 5.06% and 10.63%, respectively. This study indicates that the CNN-GRU-BiLSTM-based sap flow prediction model established with a factor analysis of historical environmental variables has optimistic applications for analyzing the transpiration of trees and evaluating water consumption.

The ‘Kyoho’ (Vitis labruscana) grape is one of the mainly fresh fruits; it is important to accurately segment the grape bunch and to detect its maturity level for the construction of an intelligent grape orchard. Grapes in the natural environment have different shapes, occlusion, complex backgrounds, and varying illumination; this leads to poor accuracy in grape maturity detection. In this paper, an improved Mask RCNN-based algorithm was proposed by adding attention mechanism modules to establish a grape bunch segmentation and maturity level detection model. The dataset had 656 grape bunches of different backgrounds, acquired from a grape growing environment of natural conditions. This dataset was divided into four groups according to maturity level. In this study, we first compared different grape bunch segmentation and maturity level detection models established with YoloV3, Solov2, Yolact, and Mask RCNN to select the backbone network. By comparing the performances of the different models established with these methods, Mask RCNN was selected as the backbone network. Then, three different attention mechanism modules, including squeeze-and-excitation attention (SE), the convolutional block attention module (CBAM), and coordinate attention (CA), were introduced to the backbone network of the ResNet50/101 in Mask RCNN, respectively. The results showed that the mean average precision (mAP) and mAP_0.75 and the average accuracy of the model established with ResNet101 + CA reached 0.934, 0.891, and 0.944, which were 6.1%, 4.4%, and 9.4% higher than the ResNet101-based model, respectively. The error rate of this model was 5.6%, which was less than the ResNet101-based model. In addition, we compared the performances of the models established with MASK RCNN, adding different attention mechanism modules. The results showed that the mAP and mAP_0.75 and the accuracy for the Mask RCNN50/101 + CA-based model were higher than those of the Mask RCNN50/101 + SE- and Mask RCNN50/101 + CBAM-based models. Furthermore, the performances of the models constructed with different network layers of ResNet50- and ResNet101-based attention mechanism modules in a combination method were compared. The results showed that the performance of the ResNet101-based combination with CA model was better than the ResNet50-based combination with CA model. The results showed that the proposed model of Mask RCNN ResNet101 + CA was good for capturing the features of a grape bunch. The proposed model has practical significance for the segmentation of grape bunches and the evaluation of the grape maturity level, which contributes to the construction of intelligent vineyards.

Pecan (Carya cathayensis) is an important economic crop, and its hydrolyzed peptides have been evidenced to reduce the effect of oxidative stress due to their antioxidant capacity. Hence, the protocols of ultrafiltration and gel filtration chromatography were established to obtain bioactive peptides from by-products of C. cathayensis (pecan cake). As measured by DPPH/ABTS radical scavenging, the peptides with less molecular weight (MW) possess higher antioxidant capacity. PCPH-III (MW < 3 kDa) presented higher radical scavenging capacity than PCPH-II (3 kDa < MW < 10 kDa) and PCPH-I (MW > 10 kDa) measured by DPPH (IC₅₀: 111.0 μg/ mL) and measured by ABTs (IC₅₀: 402.9 μg/mL). The secondary structure and amino acid composition varied by their MW, in which PCPH-II contained more α-helices (26.71%) and β-sheets (36.96%), PCPH-III contained higher ratios of β-turns (36.87%), while the composition of different secondary of PCPH-I was even 25 ± 5.76%. The variation trend of α-helix and random experienced slightly varied from PCPH-I to PCPH-II, while significantly decreased from PCPH-II to PCPH-III. The increasing antioxidant capacity is followed by the content of proline, and PCPH-III had the highest composition (8.03%). With regard to the six peptides identified by LC-MS/MS, two of them (VYGYADK and VLFSNY) showed stronger antioxidant capacity than others. In silico molecular docking demonstrated their combining abilities with a transcription factor Kelch-like ECH-associated protein 1 (Keap1) and speculated that they inhibit oxidative stress through activating the Keap1-Nrf2-ARE pathway. Meanwhile, increased activity of SOD and CAT—antioxidant markers—were found in H₂O₂-induced cells. The residue of tyrosine was demonstrated to contribute the most antioxidant capacity of VYGYADK and its position affected less. This study provided a novel peptide screening and by-product utilization process that can be applied in natural product developments.

Grapes are an important cash crop that contributes to the rapid development of the agricultural economy. The harvesting of ripe fruits is one of the crucial steps in the grape production process. However, at present, the picking methods are mainly manual, resulting in wasted time and high costs. Therefore, it is particularly important to implement intelligent grape picking, in which the accurate detection of grape stems is a key step to achieve intelligent harvesting. In this study, a trellis grape stem detection model, YOLOv8n-GP, was proposed by combining the SENetV2 attention module and CARAFE upsampling operator with YOLOv8n-pose. Specifically, this study first embedded the SENetV2 attention module at the bottom of the backbone network to enhance the model’s ability to extract key feature information. Then, we utilized the CARAFE upsampling operator to replace the upsampling modules in the neck network, expanding the sensory field of the model without increasing its parameters. Finally, to validate the detection performance of YOLOv8n-GP, we examined the effectiveness of the various keypoint detection models constructed with YOLOv8n-pose, YOLOv5-pose, YOLOv7-pose, and YOLOv7-Tiny-pose. Experimental results show that the precision, recall, mAP, and mAP-kp of YOLOv8n-GP reached 91.6%, 91.3%, 97.1%, and 95.4%, which improved by 3.7%, 3.6%, 4.6%, and 4.0%, respectively, compared to YOLOv8n-pose. Furthermore, YOLOv8n-GP exhibits superior detection performance compared with the other keypoint detection models in terms of each evaluation indicator. The experimental results demonstrate that YOLOv8n-GP can detect trellis grape stems efficiently and accurately, providing technical support for advancing intelligent grape harvesting.

Protein–protein interaction (PPI) plays an important role in the biological process. While, there are limitations of long spend time and high labor cost in traditional lab based PPIs detection approaches. Although many computation-based methods have been proposed for prediction of PPIs, achieving high predictive performance and overcoming low generalization performance remain challenging issues. This study developed a novel PPIs prediction method by combining protein language model (PLM) of ESM2 and deep neural network, which show high predictive and generalization performance. Specifically, after protein-coding of protein sequence, the PLM of ESM2 is embedded. Then pre-training weight which trained on human dataset was transferred to other species dataset. The generalization of the model we established is tested on four independent datasets. The results show that values of area under precision–recall curve (AUPR) and area under the receiver operating characteristic (ROC) curve achieved 93.06% and 98.69% on human dataset respectively. AUPR values achieved 87.54%, 84.95%, 81.99%, and 66.23% on datasets of Mus musculus, Drosophila melanogaster, Caenorhabditis elegans and Saccharomyces cerevisiae, which are higher of 13.61%–78.78%, 11.35%–75.71%, 12.76%–73.1% and 11.77%–56.94% than multilayer perceptron + convolutional neural network (MLP + CNN), MLP + gated recurrent unit (GRU) and MLP + CNN + GRU based models on these four dataset respectively. The results indicate that this PPIs prediction method we developed can extract features that better characterize the nature of PPIs with protein sequence, and achieving a high predictive and generalization performance for predicting PPIs.