Tomato blight significantly threatened tomato yield and quality, making precise disease detection essential for modern agricultural practices. Traditional segmentation models often struggle with over-segmentation and missed segmentation, particularly in complex backgrounds and with diverse lesion morphologies. To address these challenges, we proposed Unet with Vision Mamba and ConvNeXt (VMC-Unet), an asymmetric segmentation model for quantitative analysis of tomato blight. Built on the Unet framework, VMC-Unet integrated a parallel feature-aware backbone combining ConvNeXt, Vision Mamba, and Atrous Spatial Pyramid Pooling (ASPP) modules to enhance spatial feature focusing and multi-scale information processing. During decoding, Vision Mamba was hierarchically embedded to accurately recover complex lesion morphologies through refined feature processing and efficient up-sampling. A joint loss function was designed to optimize the model’s performance. Extensive experiments on both tomato epidemic and public datasets demonstrated VMC-Unet superior performance, achieving 97.82% pixel accuracy, 87.94% F1 score, and 86.75% mIoU. These results surpassed those of classical segmentation models, underscoring the effectiveness of VMC-Unet in mitigating over-segmentation and under-segmentation while maintaining high segmentation accuracy in complex backgrounds. The consistent performance of the model across various datasets further validated its robustness and generalization potential, highlighting its applicability in broader agricultural settings.

Facility agriculture cultivation is the main production form of the vegetable industry in the world. As an important vegetable crop, hot peppers are easily threatened by many diseases in a facility microclimate environment. Traditional disease detection methods are time-consuming and allow the disease to proliferate, so timely detection and inhibition of disease development have become the focus of global agricultural practice. This article proposed a generalizable and explainable machine learning model for hot pepper damping-off in intensive seedling production under the condition of ensuring the high accuracy of the model. Through Kalman filter smoothing, SMOTE-ENN unbalanced sample processing, feature selection and other data preprocessing methods, 19 baseline models were developed for prediction in this article. After statistical testing of the results, Bayesian Optimization algorithm was used to perform hyperparameter tuning for the best five models with performance, and the Extreme Random Trees model (ET) most suitable for this research scenario was determined. The F₁-score of this model is 0.9734, and the AUC value is 0.9969 for predicting the severity of hot pepper damping-off, and the explainable analysis is carried out by SHAP (SHapley Additive exPlanations). According to the results, the hierarchical management strategies under different severities are interpreted. Combined with the front-end visualization interface deployed by the model, it is helpful for farmers to know the development trend of the disease in advance and accurately regulate the environmental factors of seedling raising, and this is of great significance for disease prevention and control and to reduce the impact of diseases on hot pepper growth and development.

This study addresses the limitations of current non-destructive techniques for assessing tomato quality, such as their high cost, strong dependence on spectroscopic instruments, and difficulty in dynamic monitoring. The study proposes an integrated tomato quality prediction model that combines a Long Short-Term Memory (LSTM)-based environmental predictor, a Gated Recurrent Unit with attention mechanism (GRU-AT) for dynamic maturity prediction, and a Deep Neural Network (DNN)-based quality evaluation module. The LSTM model demonstrated high accuracy in environmental prediction (R² > 0.9559). The GRU-AT model excelled in color ratio prediction (R² > 0.86), and the DNN model achieved R² values exceeding 0.811 for lycopene (LYC), firmness (FI), and soluble solids content (SSC). Experimental results demonstrate that this approach can accurately predict multiple quality parameters using only standard RGB images. In summary, this study provides a low-cost, low-complexity solution enabling real-time, non-destructive monitoring of greenhouse tomato quality, offering a viable pathway for crop quality management in precision agriculture.

Cucumber target spot disease, caused by Corynespora cassiicola, is a major constraint on greenhouse cucumber production. Accurate pixel-level segmentation and automated severity assessment remain challenging because early lesions are small, lesion boundaries are gradual, and field images often contain complex illumination and background interference. To address these issues, this study proposes a small-lesion and boundary-aware Mask2Former framework with a ResNet-50 (R50) backbone for cucumber target spot segmentation. Specifically, a Small Target Enhancement Module (ST) is designed by integrating atrous spatial pyramid pooling (ASPP), a high-resolution feature retention branch, and a small-target-weighted loss to improve sensitivity to early micro-lesions. In addition, a Boundary Segmentation Module (BS) is introduced to enhance boundary localization through boundary attention and explicit supervision with Dice and Focal losses. A field dataset containing 2559 cucumber leaf images was annotated at the pixel level and split into training, validation, and test sets at a ratio of 7:1:2. Disease severity was categorized into four grades based on the lesion-to-leaf-area ratio. On the test set, the proposed model achieved an mIoU of 85.05%, a Dice coefficient of 91.94%, and a precision of 92.30%, outperforming the Mask2Former baseline by 4.95, 4.72, and 4.58 percentage points, respectively. Moreover, severity assessment based on segmentation results reached an overall grading accuracy of 91.6% (Cohen’s Kappa = 0.89), with misclassifications predominantly confined to adjacent severity grades and no large cross-category errors observed. These results indicate that the proposed method has practical potential for automated disease monitoring and precision management in greenhouse cucumber production.