The tree tomato (Solanum betaceum Cav.) is a Solanaceae fruit native to South America with nutritional, functional, and economic value. However, assessing fruit maturity and making harvest-related decisions continue to rely primarily on manual visual inspection, which can be subjective and inconsistent under field conditions. The objective of this study was to evaluate the performance of YOLO11-based object detection models for the automatic detection of the ripeness of Solanum betaceum fruits in a real-world agricultural setting. To this end, a dataset of 200 images captured with smartphones was used, in which 1,361 fruits were annotated with bounding boxes corresponding to green and ripe fruits. The YOLO11n, YOLO11s, and YOLO11m models were trained with an input resolution of 640. For each setting, cross-validation (k = 5) was used, and data augmentation was appliedss-validation (k=5) for each setting and applying data augmentation to the training subsets. YOLO11m achieved the best overall performance, as it combined high values fr precision, recall, and mAP. On the test set, YOLO11m achieved an precision of 0.9878 ± 0.0055, a recall of 0.9750 ± 0.148, and an mAP50 of 0.9618 ± 0.0188 for green fruit, as well as a precision of 0.9950 ± 0.0041, a recall of 0.9958 ± 0.0039, and a mAP50 of 0.9920 ± 0.0047 for ripe fruits. YOLO11s also demonstrated competitive performance, particularly in the detection of ripe fruits, with an mAP50 of 0.9894 ± 0.0070 and an mAP50–95 of 0.9754 ± 0.0081, positioning it as an efficient alternative given its substantially lower computational cost. In contrast, YOLO11n, the lightest architecture, showed the lowest performance for green fruits (mAP50-95: 0.8557 ± 0.0611), reflecting the limitations of its 7representational capacity.
IntroductionFig rust caused by Phakopsora nishidana is a destructive foliar disease that impairs leaf photosynthetic capacity and seriously endangers fig yield and production stability. Traditional visual diagnosis of fig rust severity relies on manual observation, which is labor-intensive, highly subjective, and incapable of large-scale, quantitative disease evaluation. Although hyperspectral remote sensing has been widely adopted for plant disease monitoring, few studies have integrated hyperspectral reflectance features and leaf physiological parameters for quantitative estimation of fig rust severity, restricting the accurate and refined monitoring of this disease.MethodsThis study took leaf lesion coverage as a continuous quantitative indicator of fig rust severity, and collected hyperspectral reflectance data (400–1000 nm) and leaf physiological parameters from 336 fig leaf samples covering five disease severity levels. We systematically analyzed the variation characteristics of leaf physiological indexes under different disease degrees, and comprehensively compared the performance of different spectral preprocessing methods, feature wavelength selection algorithms, and machine learning regression models for fig rust severity prediction.ResultsWith the aggravation of fig rust infection, leaf chlorophyll and water content decreased significantly, while anthocyanin content increased remarkably; flavonol content fluctuated across different disease levels and only presented a weak correlation with lesion coverage. The combination of Savitzky–Golay smoothing and first derivative (SG+FD) was identified as the optimal spectral preprocessing method, and the competitive adaptive reweighted sampling (CARS) algorithm outperformed the successive projections algorithm (SPA) in feature wavelength screening. The pure spectral CARS-SVR model yielded an R² of 0.849 and an RMSE of 4.393, while the SVR model based solely on physiological parameters achieved an R² of 0.778 and an RMSE of 5.372. The fusion SVR model integrating spectral and physiological information obtained the best prediction performance, with R², RMSE, and RPD reaching 0.924, 3.067, and 3.641, respectively. Ablation analysis confirmed that chlorophyll content was the dominant factor improving model prediction accuracy. Additionally, model prediction reliability declined under high disease severity, with the severity of heavily infected leaves generally underestimated, showing uneven model performance across different infection levels.DiscussionThe fusion of hyperspectral and leaf physiological data can effectively improve the accuracy and stability of quantitative fig rust severity estimation. The proposed method provides a reliable leaf-level technical solution for precise monitoring of fig rust, and offers valuable theoretical and methodological references for the intelligent quantitative evaluation of crop foliar diseases.
IntroductionSoybean productivity is highly sensitive to heat and insufficient nitrogen supply, stresses that can occur simultaneously. However, organ-specific lipid responses to this stress combination remain poorly resolved.MethodsWe exposed hydroponically grown soybean plants to nitrogen deficiency, heat stress (HS) and their combination and compared physiological, lipidomic and gene-expression responses in roots and leaves.ResultsCombined stress caused the largest reductions in biomass and photosynthetic performance and the highest H₂O₂ and MDA accumulation and relative total abundance of candidate oxylipin features. Untargeted LC–MS lipid profiling coupled with WGCNA revealed distinct organ-specific lipid abundance patterns. HS produced the strongest root lipidomic response, characterized by increased abundances of features putatively annotated as polyunsaturated diacylglycerols, lysophospholipids and a gibberellin A12-related compound. Leaves showed their largest lipidomic shift under combined stress, with decreased abundances of photosynthetic membrane-lipid features, increased linoleic-acid- and digalactosylmonoacylglycerol-related features and divergent changes among jasmonate-related features, including cis-jasmone. RT-qPCR showed higher PLD1 and lower GA20ox expression in roots under HS. In leaves, FAD2, LOX2, OPR3 and MYC2 expression increased under all stress treatments, whereas PLA2 expression increased only under HS and combined stress.DiscussionThese findings reveal pronounced tissue specificity in soybean lipid responses to combined nitrogen deficiency and HS and highlight candidate membrane-lipid- and oxylipin-associated responses for further investigation. However, they do not establish direct root–shoot lipid transport or metabolic coordination.
Plants must continuously adapt to complex dynamic environmental conditions throughout their entire life cycle. Abiotic stresses, such as drought, salinity, heavy metal toxicity, extreme temperatures significantly impair plant growth and cause substantial reductions in crop yield. Iron (Fe), an essential micronutrient, serves as a cofactor for numerous metalloenzymes and a critical component of the mitochondrial and chloroplast electron transport chains. It plays an indispensable role in fundamental physiological processes such as photosynthesis, respiration, antioxidant defense, and nitrogen metabolism. However, abiotic stress frequently disrupts iron homeostasis in plants, resulting in either iron deficiency or toxic accumulation, a dual imbalance that compromises cellular function. Such dysregulation exacerbates oxidative damage via Fenton chemistry and severely inhibits growth and development by impairing photosynthetic efficiency and enzymatic activity. Emerging evidence indicates that iron acts not merely as a nutritional element but also as a key modulator of intracellular signaling, metabolic reprogramming, and ion homeostasis, thereby contributing critically to abiotic stress resilience. This review comprehensively synthesizes current understanding of iron’s regulatory roles in plant responses to major abiotic stresses, with particular emphasis on the crosstalk between iron homeostasis networks and stress activated signaling pathways. We further elucidate how perturbations in iron homeostasis exert bidirectional effects on stress tolerance, both sensitizing and, under certain contexts, potentiating adaptive responses, thereby offering conceptual and mechanistic insights to advance research on the integration of iron nutrition and stress adaptation, and to inform strategies for enhancing crop resilience.
IntroductionThis paper proposes an improved detection framework, PLD-YOLO, to address the challenges of small object scale, strong background interference, and loss of shallow feature information during feature extraction in pest and disease detection under complex natural environments for intelligent plant protection.MethodsThe proposed framework enhances small object representation by introducing a high-resolution feature branch. It further improves contextual modeling capability and suppresses complex background noise through multi-scale convolutions and an adaptive feature mechanism. In addition, a dynamic small object weighting strategy is introduced to improve small object learning under scale imbalance conditions.ResultsThis study systematically evaluates the proposed method on the Tomato-Village dataset constructed under natural environments and compares it with representative two-stage object detection methods, YOLO series models, and transformer-based detection models. Ablation studies, cross-dataset validation, and robustness evaluations under different field conditions further demonstrate the effectiveness and robustness of the proposed approach. The experimental results show that PLD-YOLO achieves excellent performance in terms of precision, recall, mAP@50, and mAP@50–95 while maintaining high inference efficiency.DiscussionThe proposed method provides a potential technical solution for intelligent plant protection and contributes to the advancement of precision agriculture.
