Accurately and effectively detecting the growth position and contour size of apple fruits is crucial for achieving intelligent picking and yield predictions. Thus, an effective fruit edge detection algorithm is necessary. In this study, a fusion edge detection model (RED) based on a convolutional neural network and rough sets was proposed. The Faster-RCNN was used to segment multiple apple images into a single apple image for edge detection, greatly reducing the surrounding noise of the target. Moreover, the K-means clustering algorithm was used to segment the target of a single apple image for further noise reduction. Considering the influence of illumination, complex backgrounds and dense occlusions, rough set was applied to obtain the edge image of the target for the upper and lower approximation images, and the results were compared with those of relevant algorithms in this field. The experimental results showed that the RED model in this paper had high accuracy and robustness, and its detection accuracy and stability were significantly improved compared to those of traditional operators, especially under the influence of illumination and complex backgrounds. The RED model is expected to provide a promising basis for intelligent fruit picking and yield prediction.

Tomato is a globally significant horticultural crop with substantial economic and nutritional value. High-precision phenotypic analysis of tomato fruit characteristics, enabled by computer vision and image-based phenotyping technologies, is essential for varietal selection and automated quality evaluation. An intelligent detection framework for phenomics analysis of tomato fruits was developed in this study, which combines image processing techniques with deep learning algorithms to automate the extraction and quantitative analysis of 12 phenotypic traits, including fruit morphology, structure, color and so on. First, a dataset of tomato fruit section images was developed using a depth camera. Second, the SegFormer model was improved by incorporating the MLLA linear attention mechanism, and a lightweight SegFormer-MLLA model for tomato fruit phenotype segmentation was proposed. Accurate segmentation of tomato fruit stem scars and locular structures was achieved, with significantly reduced computational cost by the proposed model. Finally, a Hybrid Depth Regression Model was designed to optimize the estimation of optimal depth. By fusing RGB and depth information, the framework enabled efficient detection of key phenotypic traits, including fruit longitudinal diameter, transverse diameter, mesocarp thickness, and depth and width of stem scar. Experimental results demonstrated a high correlation between the phenotypic parameters detected by the proposed model and the manually measured values, effectively validating the accuracy and feasibility of the model. Hence, we developed an equipment automatically phenotyping tomato fruits and the corresponding software system, providing reliable data support for precision tomato breeding and intelligent cultivation, as well as a reference methodology for phenotyping other fruit crops.

With the explosive growth of information on the internet, personalized recommendation technology has become an important tool for helping users efficiently acquire information. However, existing spreading-based recommendation algorithms only consider user choices and fail to fully leverage the potential relationships between users and items. Additionally, the incomplete utilization of user and item information limits their application potential and applicable scenarios, resulting in suboptimal recommendation performance in practical applications. To address this issue, we propose a Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading (LGCNHS). This algorithm first optimizes the embeddings of users and items using their respective feature matrix, then learns the latent embedding representations of users and items through a lightweight graph convolutional network. Finally, the latent embedding representations are incorporated as key parameters into the hybrid spreading recommendation algorithm to generate recommendations. Comparative experiments on two publicly available datasets, MovieLens and Douban, demonstrate that LGCNHS achieves improved accuracy and diversity in recommendations compared to related methods. The algorithm code is available on github.

The hemibiotrophic bacterial pathogen Pseudomonas syringae (Pst) infects a range of plant species and causes enormous economic losses. Despite its agronomic significance, the molecular mechanisms underlying tomato–Pst interactions remain largely uncharacterized. To elucidate these mechanisms, we conducted a comprehensive transcriptomic analysis using infected tomato leaves inoculated with virulent strains Pst DC3000 at relatively early time points. RNA-sequencing of nine libraries identified stage-specific expression patterns, with DEG counts ranging from 484 to 1267 upregulated and from 560 to 844 downregulated genes. Enrichment analysis highlighted significant alterations in metabolic pathways, plant–pathogen interaction networks, and hormone signaling cascades, with marked transcriptional reprogramming observed between the pre- and post-infection stages. A longitudinal analysis of gene expression dynamics identified 15 consistently upregulated and 9 downregulated genes across all post-inoculation time points. Notably, in several candidate genes, a homologous gene of AtNSP2, SlNSP-Like was confirmed to be involved in disease resistance in tomato leaves. SlNSP-Like is localized in the cytoplasm and nucleus, and the transient overexpression of SlNSP-Like tomato plant exhibits significant resistance to Pst DC3000. This study provides valuable insights into the molecular dialogue between tomato and Pst, and the identified regulatory genes and pathways serve as promising targets for breeding disease-resistant tomato cultivars and developing management strategies against bacterial spot disease.