Lulu Ma

Introduction: Safeguarding the yield and quality of field crops against abiotic stresses is critical for large-scale agricultural production and precision agronomy. This study addresses the challenges of detecting concealed fruit stress and overcoming label scarcity in unmanned aerial vehicle (UAV) multispectral monitoring, using blossom-end rot (BER) in processing tomatoes as a case study. Methods: We developed a leaf–fruit synergistic Roll and Color Disease Index (RCDI), integrating fruit incidence with visible canopy phenotypic features to characterize the combined canopy–fruit stress status associated with BER. A three-level screening strategy was used to identify the optimal spectral feature set. A Multi-model Collaborative Cyclic Self-Training (MCC-ST) framework was subsequently developed to address the limited availability of severity-labeled samples. Results: The combination of GRVI, NDVI, and SAVI was identified as the optimal spectral feature set, achieving stable within-dataset binary classification performance of approximately 97% in repeated cross-validation. Under 30 random-seed repeated stratified three-fold cross-validations, MCC-ST + DT and MCC-ST + RF achieved RCDI-based BER severity-grading accuracies of 85.00% ± 0.31% and 85.07% ± 0.42%, respectively. Compared with the corresponding original DT and RF models, MCC-ST improved repeated-validation accuracy by 16.19–4.09 percentage points. Discussion: The RCDI helps bridge the observational gap between canopy signals and concealed fruit stress, while MCC-ST alleviates the bottleneck associated with label scarcity. The proposed approach provides a promising framework for crop abiotic-stress monitoring under limited-label conditions.

Quality grading of greenhouse lettuce requires rapid external appearance screening and nondestructive internal quality assessment. However, existing detection methods struggle to simultaneously evaluate both external and internal quality while maintaining efficiency, resulting in a lack of scientific and comprehensive integrated evaluation standards for current crop grading. To address this issue, this study leveraged the technical strengths of different sensors to construct separate models: an RGB image-based monitoring model for external quality and a hyperspectral-based estimation model for internal quality. Using a combined objective–subjective weighting method, this approach scientifically integrated external and internal quality monitoring indicators to establish a comprehensive evaluation method for greenhouse lettuce quality. The results demonstrate that features such as canopy projection area, compactness, and color components can be extracted from RGB images. Combined with Ridge regression, this approach achieves high-accuracy estimation of lettuce fresh weight and leaf area (R² ≥ 0.880). For intrinsic quality, by combining hyperspectral data with the CARS and SPA band selection algorithms, a Random Forest (RF)-based inversion model for chlorophyll, soluble sugar, protein, and vitamin C content was developed. The AHP-CRITIC method effectively resolved the weight imbalance caused by an excessive coefficient of variation in appearance indicators, thereby achieving the scientific integration of appearance and internal quality data. The grading outcomes of this integrated evaluation method were highly consistent with industry standards (kappa coefficient: 0.788). This approach establishes an effective link between the rapid monitoring of external and internal quality for comprehensive evaluation, providing a novel technical pathway and scientific basis for nondestructive post-harvest detection and automated grading of greenhouse vegetables.

To address the limitations of traditional cotton leaf nitrogen content estimation methods, which include low efficiency, high cost, poor portability, and challenges in vegetation index acquisition owing to environmental interference, this study focused on emerging non-destructive nutrient estimation technologies. This study proposed an innovative method that integrates multi-color space fusion with deep and machine learning to estimate cotton leaf nitrogen content using smartphone-captured digital images. A dataset comprising smartphone-acquired cotton leaf images was processed through threshold segmentation and preprocessing, then converted into RGB, HSV, and Lab color spaces. The models were developed using deep-learning architectures including AlexNet, VGGNet-11, and ResNet-50. The conclusions of this study are as follows: (1) The optimal single-color-space nitrogen estimation model achieved a validation set R² of 0.776. (2) Feature-level fusion by concatenation of multidimensional feature vectors extracted from three color spaces using the optimal model, combined with an attention learning mechanism, improved the validation R² to 0.827. (3) Decision-level fusion by concatenating nitrogen estimation values from optimal models of different color spaces into a multi-source decision dataset, followed by machine learning regression modeling, increased the final validation R² to 0.830. The dual fusion method effectively enabled rapid and accurate nitrogen estimation in cotton crops using smartphone images, achieving an accuracy 5–7% higher than that of single-color-space models. The proposed method provides scientific support for efficient cotton production and promotes sustainable development in the cotton industry.

Introduction: Rapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status.Methods: In this study, 20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods extreme learning machine (ELM), random forest (RF), back propagation (BP), multivariable linear regression (MLR), support vector machine (SVM)], and the optimal modeling strategy (RF) was selected. Finally, the vegetation indices with a high correlation with LAI were fused to construct the VI-fusion RF model, to explore the potential of multi-vegetation index fusion in the estimation of cotton LAI.Results: The RF model had the highest estimation accuracy among the LAI estimation models, and the estimation accuracy of models constructed by fusing multiple VIs was higher than that of models constructed based on single VIs. Among the multi-VI fusion models, the RF model constructed based on the fusion of seven vegetation indices (MNDSI, SRI, GRVI, REP, CIred-edge, MSR, and NVI) had the highest estimation accuracy, with coefficient of determination (R2), rootmean square error (RMSE), normalized rootmean square error (NRMSE), and mean absolute error (MAE) of 0.90, 0.50, 0.14, and 0.26, respectively. Discussion: Appropriate fusion of vegetation indices can include more spectral features in modeling and significantly improve the cotton LAI estimation accuracy. This study will provide a technical reference for improving the cotton LAI estimation accuracy, and the proposed method has great potential for crop growth monitoring applications.

Nitrogen fertilizer levels significantly affect crop growth and development, necessitating precision management. Most studies focus on nitrogen nutrient estimation using vegetation indices and textural features, overlooking the diagnostic potential of color features. Hence, we investigated cotton nitrogen nutrition status using unmanned aerial vehicle (UAV) image features and the nitrogen nutrient index (NNI). Random frog algorithm - and random forest-screened image feature sets significantly correlated with the NNI, which were substituted into four machine learning algorithms for NNI estimation modeling. The composite scores (F) of optimal image feature sets were calculated using the coefficient of variation method for comprehensive cotton nitrogen nutrient diagnosis. Validation of the model for determining the critical nitrogen concentration in cotton yielded a coefficient of determination R² = 0.89, root mean square error RMSE = 0.50 g (100 g)^-1, and mean absolute error MAE = 0.44, demonstrating improved performance. Additionally, our novel NNI estimation model constructed based on the optimal image feature sets exhibited R²_c = 0.97, RMSE_c = 0.02, MAE_c = 0.02, R²_v = 0.85, RMSE_v = 0.05, and MAE_v = 0.04. Polynomial fitting of the composite index with NNI indicated that the model was reliable and yielded the following diagnostic criterion: 0.48 < F2 < 0.67 indicated nitrogen overapplication, whereas F2 < 0.48 or F2 > 0.67 indicated nitrogen deficiency. This study demonstrates the superior effectiveness of using UAV RGB image feature sets for NNI estimation and the quick, accurate diagnosis of cotton nitrogen levels, which will help guide nitrogen fertilizer application.

Cotton yield estimation is of great practical significance to producers, allowing them to make rational management decisions. At present, crop yield estimation methods mainly comprise traditional agricultural yield estimation methods, which have many shortcomings. As an ideal “probe” for detecting crop photosynthesis, sun-induced chlorophyll fluorescence (SIF) can directly reflect the dynamics of actual crop photosynthesis and has the potential to predict crop yield, in order to realize cotton yield estimation based on canopy SIF. In this study, we set up field trials with different nitrogen fertilizer gradients. The changes of canopy SIF and the physiological parameters of cotton in different growth periods were analyzed. To investigate the effects of LAI and AGB on canopy SIF estimation of cotton yield, four algorithms, Ada Boost (Adaptive Boosting), Bagging (Bootstrap Aggregating), RF (Random Forest), and BPNN (Backpropagation Neural Network), were used to construct cotton yield estimation models based on the SIF and SIFy (the normalization of SIF by incident photosynthetically active radiation) for different time and growth periods. The results include the following: (1) The effects of the leaf area index (LAI) and aboveground biomass (AGB) on cotton canopy SIF and cotton yield were similar. The correlation coefficients of LAI and AGB with cotton yield and SIF were significantly positively correlated with each other starting from the budding period, reaching the maximum at the flowering and boll period, and decreasing at the boll period; (2) In different monitoring time periods, the R² of the cotton yield estimation model established based on SIF and SIFy showed a gradual increase from 10:00 to 14:00 and a gradual decrease from 15:00 to 19:00, while the optimal observation time was from 14:00 to 15:00. The R² increased with the progression of growth from the budding period to the flowering and boll period and decreased at the boll period, while the optimum growth period was the flowering and boll period; (3) Compared to SIF, SIFy has a superior estimation of yield. The best yield estimation model based on the RF algorithm (R² = 0.9612, RMSE = 66.27 kg·ha⁻¹, RPD = 4.264) was found in the canopy SIFy of the flowering and boll period at 14:00–15:00, followed by the model utilizing the Bagging algorithm (R² = 0.8898) and Ada Boost algorithm (R² = 0.8796). In summary, SIFy eliminates the effect of PAR (photosynthetically active radiation) on SIF and can further improve the estimation of SIF production. This study provides empirical support for SIF estimation of cotton yield and methodological and modeling support for the accurate estimation of cotton yield.