Search Results (219)

Tomato (Solanum lycopersicum L.) is one of the most preferred hosts of polyphagous stink bugs (Hemiptera: Pentatomidae) and leaf-footed bugs (Hemiptera: Coreidae). These hemipterans can infest tomato fruits at all stages of fruit ripening. However, it is unclear whether there is any feeding preference for these true bugs among different ripening stages of tomato (green, breaker, pink, and red stages). Feeding and behavioral assays were performed to determine the feeding preference and damage potential of two common stink bugs—the brown marmorated stink bug (Halyomorpha halys (Stål)) and the southern green stink bug (Nezara viridula L.)—and a leaf-footed bug (Leptoglossus zonatus (Dallas)) among the various ripening stages of tomato. The results indicated that green is the most preferred ripening stage for N. viridula and L. zonatus, while pink tomatoes were found to be a more preferred feeding site for H. halys. Fully ripe red tomatoes were found to be the least preferred feeding site for all three insects. The findings of this study will be useful for developing fruit damage symptom-based monitoring programs and establishing economic threshold levels for these pests in tomatoes, as well as informing harvesting regimes. Full article

Tomato (Solanum lycopersicum) is a globally important crop, and enhancing its fruit quality and phenotypic traits is a key objective in modern breeding. This study investigates the role of the LEAFY-COTYLEDON1-LIKE4 (L1L4), an NF-YB subunit of the nuclear factor Y (NF-Y) transcription factor, in tomato fruit development using RNA-sequencing data from zinc-finger nuclease (ZFN)-targeted disruption lines. Differential gene expression (DEG) analyses of two independent l1l4 mutant lines compared to the wild-type line revealed significant alterations in key metabolic pathways and regulatory networks that are implicated in fruit ripening. Specifically, L1L4 disruption impacted the genes and pathways related to the fruit’s color development (carotenoid and flavonoids), texture (cell wall modification), flavor (sugar and volatile organic compound metabolism), and ripening-related hormone signaling. The analyses also revealed multiple differentially expressed histones, histone modifiers, and transcription factors (ERFs, MYBs, bHLHs, WRKYs, C2H2s, NACs, GRAS, MADs, and bZIPs), indicating that L1L4 participates in a complex regulatory network. These findings provide valuable insights into the role of L1L4 in orchestrating tomato fruit development and highlight it as a potential target for genetically improving the fruit quality. Full article

During fruit ripening in Capsicum species, substantial amounts of carotenoids accumulate in the pericarp. While the carotenoid biosynthesis pathway in Capsicum species has been extensively investigated from various angles, the transcriptional regulation of genes encoding carotenoid biosynthetic enzymes remains less understood in this non-climacteric horticultural crop compared to tomato, a climacteric fruit. In the present study, we investigated the function of the NAM, ATAF1/2 or CUC2 81 (CaNAC81) transcription factor gene. This gene was selected through RNA-Seq co-expression analysis based on the correlation between expressed transcription factor gene profiles and those of carotenoid structural genes. To determine its role in regulating the expression of biosynthetic-related carotenogenic genes, we performed Virus-Induced Gene Silencing (VIGS) assays in the Serrano-type C. annuum ‘Tampiqueño 74’. Fruits from plants infected with a pTRV2:CaNAC81 construct (silenced fruits) exhibited altered carotenoid pigmentation accumulation, manifested as yellow-orange spots, in contrast to fruits from non-agroinfected controls (NTC) and fruits from plants infected with the empty TRV2 construct (red fruits). Quantitative real-time PCR (qPCR) assays confirmed decreased transcript levels of CaNAC81 in fruits displaying altered pigmentation, along with reduced transcription of the PSY gene, which encodes the carotenoid biosynthetic enzyme phytoene synthase (PSY). High-performance liquid chromatography (HPLC) analysis revealed a distinct carotenoid pigment accumulation pattern in fruits from plants showing silencing symptoms, characterized by low concentrations of capsanthin and zeaxanthin and trace amounts of capsorubin, compared to control plants (NTC). These findings suggest the involvement of CaNAC81 in the regulatory network of the carotenoid biosynthetic pathway in chili pepper fruits. Full article

Yellow shoulder disorder (YSD) is characterized by discolored regions beneath the fruit’s epidermis, impacting the ripening process and rendering tomatoes unsuitable for marketing. YSD poses a significant challenge in high-tunnel (HT) tomato production, a system that has gained prominence for its ability to extend growing seasons and enhance crop quality. This review delves into the various factors influencing YSD occurrence, including soil nutritional status, weather, plant variety, and the interactions between these factors, contributing to the occurrence of YSD in HT microclimate. The severity of YSD symptoms, ranging from minor to significant discoloration, highlights the complexity of this disorder. This review highlights research gaps on the effects of temperature, relative humidity, nutrient imbalance, soil water management, clay minerals, and how their interactions influence YSD in HT microclimates, emphasizing the need for comprehensive studies to understand the complex relationships between soil health, nutrient management, and tomato quality in HT microclimates and the need for further research to sustain high-quality tomato production in HTs. Full article

Background: γ-Aminobutyric acid (GABA) is a non-proteinogenic amino acid with key roles in plant metabolism, stress responses, and fruit nutritional quality. In tomato (Solanum lycopersicum), GABA levels are dynamically regulated during fruit development but decline in the late ripening stages. Methods: To enhance GABA accumulation, we used CRISPR/Cas9 to edit the calmodulin-binding domain (CaMBD) of SlGAD2 and SlGAD3, which encode glutamate decarboxylases (GADs). The resulting truncated enzymes were expected to be constitutively active. We quantified GABA content in leaves and fruits and performed transcriptomic analysis on edited lines at the BR+7 fruit stage. Results: CaMBD truncation significantly increased GABA levels in both leaves and fruits. In gad2 sg1 lines, GABA levels increased by 3.5-fold in leaves and 3.2-fold in BR+10 fruits; in gad3 sg3 lines, increases of 2.8- and 2.5-fold were observed, respectively. RNA-seq analysis identified 1383 DEGs in gad2 #1−5 and 808 DEGs in gad3 #3−8, with 434 DEGs shared across both lines. These shared DEGs showed upregulation of GAD, GABA-T, and SSADH, and downregulation of stress-responsive transcription factors including WRKY46, ERF, and NAC. Notably, total free amino acid content and fruit morphology remained unchanged despite elevated GABA. Conclusions: CRISPR/Cas9-mediated editing of the CaMBD in SlGAD genes selectively enhances GABA biosynthesis in tomato without adverse effects on development or fruit quality. These lines offer a useful platform for GABA-centered metabolic engineering and provide insights into GABA’s role in transcriptional regulation during ripening. Full article

Current tomato harvesters rely primarily on external color as the sole indicator of ripeness. However, this approach often results in premature harvesting, leading to insufficient lycopene accumulation and a suboptimal nutritional content for human consumption. Such limitations are especially critical in controlled-environment agriculture (CEA) systems, where maximizing fruit quality and nutrient density is essential for both the yield and consumer health. To address that challenge, this study introduces a novel, multimodal harvest readiness framework tailored to nutrient film technology (NFT)-based smart farms. The proposed approach integrates plant-level stress diagnostics and fruit-level phenotyping using wearable biosensors, AI-assisted computer vision, and non-invasive physiological sensing. Key physiological markers—including the volatile organic compound (VOC) methanol, phytohormones salicylic acid (SA) and indole-3-acetic acid (IAA), and nutrients nitrate and ammonium concentrations—are combined with phenomic traits such as fruit color (a*), size, chlorophyll index (rGb), and water status. The innovation lies in a four-stage decision-making pipeline that filters physiologically stressed plants before selecting ripened fruits based on internal and external quality indicators. Experimental validation across four plant conditions (control, water-stressed, light-stressed, and wounded) demonstrated the efficacy of VOC and hormone sensors in identifying optimal harvest candidates. Additionally, the integration of low-cost electrochemical ion sensors provides scalable nutrient monitoring within NFT systems. This research delivers a robust, sensor-driven framework for autonomous, data-informed harvesting decisions in smart indoor agriculture. By fusing real-time physiological feedback with AI-enhanced phenotyping, the system advances precision harvest timing, improves fruit nutritional quality, and sets the foundation for resilient, feedback-controlled farming platforms suited to meeting global food security and sustainability demands. Full article

Several pressing issues have been revealed by deep learning-based tomato ripening detection technology in intricate environmental applications: The ripening transition stage distinction is not accurate enough, small target tomato detection is likely to miss, and the detection technology is more susceptible to variations in light. Based on the YOLOv11 model, a YOLOv11-SLBA tomato ripeness detection model was presented in this study. First, SPPF-LSKA is used in place of SPPF in the backbone section, greatly improving the model’s feature discrimination performance in challenging scenarios including dense occlusion and uneven illumination. Second, a new BiAttFPN hierarchical progressive fusion is added in the neck area to increase the feature retention of small targets during occlusion. Lastly, the feature separability of comparable categories is significantly enhanced by the addition of the auxiliary detection head DetectAux. In this study, comparative experiments are carried out to confirm the model performance. Under identical settings, the YOLOv11-SLBA model is compared to other target detection networks, including Faster R-CNN, SSD, RT-DETR, YOLOv7, YOLOv8, and YOLOv11. With 2.7 million parameters and 10.9 MB of model memory, the YOLOv11-SLBA model achieves 92% P, 83.5% R, 91.3% mAP50, 64.6% mAP50-95, and 87.5% F1-score. This is a 3.4% improvement in accuracy, a 1.5% improvement in average precision, and a 1.6% improvement in F1-score when compared to the baseline model YOLOv11. It outperformed the other comparison models in every indication and saw a 1.6% improvement in score. Furthermore, the tomato-ripeness1public dataset was used to test the YOLOv11-SLBA model, yielding model p values of 78.6%, R values of 91.5%, mAP50 values of 93.7%, and F1-scores of 84.6%. This demonstrates that the model can perform well across a variety of datasets, greatly enhances the detection generalization capability in intricate settings, and serves as a guide for the algorithm design of the picking robot vision system. Full article

Blackseed oil supplemented with caseinate (CA)–carboxymethyl chitosan (CMCH) composite membranes were evaluated for their functional properties and as edible coating for extending the shelf life of grape tomatoes. Composite films were prepared from equal parts of (CaCa or NaCa) and (CMCH) with or without supplemented 3% blackseed oil (BO) and evaluated for their functional properties. Subsequently, the edible membrane coating was evaluated to extend the shelf life of grape tomatoes (Solanum lycopersicum L.). The water vapor permeability (WVP) of the films was the lowest for the calcium caseinate–carboxymethyl chitosan–blackseed oil (CaCa-CMCH-BO) film (3.01 g kPa⁻¹ h⁻¹ m⁻²). Adding blackseed oil to the edible film matrix also led to a significant increase in its mechanical properties, resulting in tensile strength values of 12.5 MPa and 10.2 MPa and elongation at break values of 90.5% and 100% for NaCa-CMCH-BO and CaCa-CMCH-BO, respectively. The composite films also exhibited good compatibility through hydrogen bonding and hydrophobic interactions, as confirmed by FTIR spectroscopy. The particle size and zeta potential of CaCa-CMCM-BO were 117 nm and −40.73 mV, respectively, while for NaCa-CMCH-BO, they were 294.70 nm and −25.10 mV, respectively. The incorporation of BO into the films resulted in greater antioxidant activity. When applied as an edible film membrane on grape tomatoes, the coating effectively delayed the deterioration of tomatoes by reducing weight loss, microbial spoilage, and oxidative degradation. Compared to the control, the coated fruits had delayed ripening, with a shelf life of up to 30 days, and reduced microbial growth over the entire storage period. Full article

Accurate identification of tomato ripeness and precise detection of picking points is the key to realizing automated picking. Aiming at the problems faced in practical applications, such as low accuracy of tomato ripeness and picking points detection in complex greenhouse environments, which leads to wrong picking, missed picking, and fruit damage by robots, this study proposes the YOLO-TMPPD (Tomato Maturity and Picking Point Detection) model. YOLO-TMPPD is structurally improved and algorithmically optimized based on the YOLOv8 baseline architecture. Firstly, the Depthwise Convolution (DWConv) module is utilized to substitute the C2f module within the backbone network. This substitution not only cuts down the model’s computational load but also simultaneously enhances the detection precision. Secondly, the Content-Aware ReAssembly of FEatures (CARAFE) operator is utilized to enhance the up-sampling operation, enabling precise content-aware processing of tomatoes and picking keypoints to improve accuracy and recall. Finally, the Convolutional Attention Mechanism (CBAM) module is incorporated to enhance the model’s ability to detect tomato-picking key regions in a large field of view in both channel and spatial dimensions. Ablation experiments were conducted to validate the effectiveness of each proposed module (DWConv, CARAFE, CBAM), and the architecture was compared with YOLOv3, v5, v6, v8, v9, and v10. The experimental results reveal that, when juxtaposed with the original network model, the YOLO-TMPPD model brings about remarkable improvements. Specifically, it improves the object detection F1 score by 4.48% and enhances the keypoint detection accuracy by 4.43%. Furthermore, the model’s size is reduced by 8.6%. This study holds substantial theoretical and practical value. In the complex environment of a greenhouse, it contributes significantly to computer-vision-enabled detection of tomato ripening. It can also help robots accurately locate picking points and estimate posture, which is crucial for efficient and precise tomato-picking operations without damage. Full article

Anthocyanins, which accumulate in fruits, flowers, and vegetative organs, play a critical role in plant reproduction, disease resistance, stress tolerance, and promoting human health. Although light significantly influences the development of various fruit pigments, the specific mechanisms through which it regulates anthocyanin accumulation during fruit ripening are not yet fully understood. This study aimed to investigate the role of light in anthocyanin biosynthesis using Aft tomato fruits, which accumulate pigments in the epidermis. To explore the effects of light on anthocyanin biosynthesis, half of each fruit was covered with aluminum foil to establish light-exposed and bagged conditions for comparative analysis. The results showed that the bagged treatment led to a significant decrease in the total anthocyanin content of the fruits. Transcriptome analysis revealed a notable upregulation of several structural genes involved in the anthocyanin biosynthetic pathway, specifically Sl4CL, SlCHS, SlCHI, SlF3H, SlDFR, and Sl3GT in the light-exposed fruits. Additionally, the expression levels of light-responsive genes and transcription factors, such as SlCRY1, SlSPA, SlUVR3, SlHY5, SlBBX24, SlMYB11, MADS-box transcription factor 23, SlHD-ZIP I/II, SlAN2-like, SlbHLH and SlWD40 proteins, were significantly higher in the light-exposed samples compared to those subjected to the bagged treatment. Weighted Gene Co-Expression Network Analysis (WGCNA) demonstrated a strong association between light-induced gene expression such as SlPAL, SlCHS1, SlDFR, SlF3H, SlF3′5′H, SlANS, SlHY5, and SlAN2-like quantified by qRT-PCR analysis and anthocyanin biosynthesis. Moreover, as the fruit matured, both anthocyanin accumulation and the expression of genes related to its biosynthetic pathway increased. These findings contribute to a foundational understanding of the regulatory network that influences light-induced processes and fruit development impacting anthocyanin accumulation, which will facilitate in-depth study of the functions of these identified genes and provide a foundation for breeding anthocyanin-rich tomato varieties. Full article

This study proposes an improved YOLOv11 model to address the limitations of traditional tomato recognition algorithms in complex agricultural environments, such as lighting changes, occlusion, scale variations, and complex backgrounds. These factors often hinder accurate feature extraction, leading to recognition errors and reduced computational efficiency. To overcome these challenges, the model integrates several architectural enhancements. First, the UniRepLKNet block replaces the C3k2 module in the standard network, improving computational efficiency, expanding the receptive field, and enhancing multi-scale target recognition. Second, the RFCBAMConv module in the neck integrates channel and spatial attention mechanisms, boosting small-object detection and robustness under varying lighting conditions. Finally, the TADDH module optimizes the detection head by balancing classification and regression tasks through task alignment strategies, further improving detection accuracy across different target scales. Ablation experiments confirm the contribution of each module to overall performance improvement. Our experimental results demonstrate that the proposed model exhibits enhanced stability under special conditions, such as similar backgrounds, lighting variations, and object occlusion, while significantly improving both accuracy and computational efficiency. The model achieves an accuracy of 85.4%, recall of 80.3%, and $mAP@50$ of 87.3%. Compared to the baseline YOLOv11, the improved model increases $mAP@50$ by 2.2% while reducing parameters to 2.16 M, making it well-suited for real-time applications in resource-constrained environments. This study provides an efficient and practical solution for intelligent agriculture, enhancing real-time tomato detection and laying a solid foundation for future crop monitoring systems. Full article

Accurate tomato maturity detection represents a critical challenge in precision agriculture. A YOLOv11-based algorithm named YOLO-PGC is proposed in this study for tomato maturity detection. Its three innovative components are denoted by “PGC”, respectively representing the Polarization State Space Strategy with Dynamic Weight Allocation, the Global Horizontal–Vertical Context Module, and the Convolutional–Inductive Feature Fusion Module. The Polarization Strategy enhances robustness against occlusion through adaptive feature importance modulation, he Global Context Module integrates cross-dimensional attention mechanisms with hierarchical feature extraction, and the Convolutional–Inductive Feature Fusion Module employs multimodal integration for improved object discrimination in complex scenes. Experimental results demonstrate that YOLO-PGC achieves superior precision and mean average precision compared to state-of-the-art methods. Validation on the COCO benchmark confirms the framework’s generalization capabilities, maintaining computational efficiency for real-time deployment. YOLO-PGC establishes new performance standards for agricultural object detection with potential applications in similar computer vision challenges. Overall, these components and strategies are integrated into YOLO-PGC to achieve robust object detection in complex scenarios. Full article

Tomato fruit ripening is a complex process that determines the formation of fruit quality. Transcription factors (TFs) play key roles in regulating fruit ripening and quality formation. MADS-box genes, a crucial class of genes involved in virtually all aspects of plant development, are regarded as important candidate members among them. In this study, we present a detailed overview of the phylogeny and expression of 32 tomato MIKC-type MADS-box genes. Moreover, 20 genes contained many phytohormone-related elements. In combination with higher expression in fruit, eight genes are suggested to be involved in plant hormone pathways that regulate fruit ripening. A virus-induced gene silencing (VIGS) experiment revealed that TM4, TAGL11, SlMADS6, SlMADS99, TAGL1, SlMADS1, RIN, and MC may positively regulate fruit ripening. Measurements of the endogenous phytohormones in silenced TM4, TAGL11, SlMADS6, SlMADS99, TAGL1, SlMADS1, RIN, or MC fruit suggest that eight MIKC-type MADS-box genes, as well as medicated abscisic acid (ABA), salicylic acid (SA), gibberellin (GA₃), indole-3-acetic acid (IAA), and/or methyl jasmonate (MeJA) pathways, positively regulate fruit ripening in tomatoes. Full article

To enhance the efficiency of automatic cherry tomato harvesting in precision agriculture, an improved YOLOv8n algorithm was proposed for fast and accurate recognition in natural environments. The improvements are as follows: first, the ADown down-sampling module replaces part of the original network backbone’s standard convolution, enabling the model to capture higher-level image features for more accurate target detection, while also reducing model complexity by cutting the number of parameters. Secondly, the model’s neck adopts a Slim-Neck (GSConv+VoV-GSCSP) instead of traditional convolution with C2f. It replaces this combination with the more efficient CSConv and swaps the C2f module for VoV-GSCSP. Finally, the model also introduces the EMA attention mechanism, implemented at the P5 layer, which enhances the feature representation capability, enabling the network to extract detailed target features more accurately. This study trained the object-detection algorithm on a self-built cherry tomato dataset before and after improvement and compared it with early deep learning models and YOLO series algorithms. The experimental results show that the improved model increases accuracy by 3.18%, recall by 1.43%, the F1 score by 2.30%, mAP50 by 1.57%, and mAP50-95 by 1.37%. Additionally, the number of parameters is reduced to 2.52 M, and the model size is reduced to 5.08 MB, which outperforms other related models compared to the previous version. The experiment demonstrates the technology’s broad potential for embedded systems and mobile devices. The improved model offers efficient, accurate support for automated cherry tomato harvesting. Full article

The ripeness of tomatoes is a critical factor influencing both their quality and yield. Currently, the accurate and efficient detection of tomato ripeness in greenhouse environments, along with the implementation of selective harvesting, has become a topic of significant research interest. In response to the current challenges, including the unclear segmentation of tomato ripeness stages, low recognition accuracy, and the limited deployment of mobile applications, this study provided a detailed classification of tomato ripeness stages. Through image processing techniques, the issue of class imbalance was addressed. Based on this, a model named GCSS-YOLO was proposed. Feature extraction was refined by introducing the RepNCSPELAN module, which is a lightweight alternative that reduces model size. A multi-dimensional feature neck network was integrated to enhance feature fusion, and three Semantic Feature Learning modules (SGE) were added before the detection head to minimize environmental interference. Further, Shape_IoU replaced CIoU as the loss function, prioritizing bounding box shape and size for improved detection accuracy. Experiments demonstrated GCSS-YOLO’s superiority, achieving an average mean average precision mAP50 of 85.3% and F1 score of 82.4%, outperforming the SSD, RT-DETR, and YOLO variants and advanced models like YOLO-TGI and SAG-YOLO. For practical deployment, this study deployed a mobile application developed using the NCNN framework on the Android platform. Upon evaluation, the model achieved an RMSE of 0.9045, an MAE of 0.4545, and an R² value of 0.9426, indicating strong performance. Full article