The Leaf Area Index (LAI) is a crucial structural parameter linked to the photosynthetic capacity and biomass of crops. While integrating machine learning algorithms with spectral variables has improved LAI estimation over large areas, excessive input parameters can lead to data redundancy and reduced generalizability across different crop species. To address these challenges, we propose a novel framework based on Bayesian-Optimized Random Forest Regression (Bayes-RFR) for enhanced LAI estimation. This framework employs a tree model-based feature selection method to identify critical features, reducing redundancy and improving model interpretability. A Gaussian process serves as a prior model to optimize the hyperparameters of the Random Forest Regression. The field experiments conducted over two years on maize and wheat involved collecting LAI, hyperspectral, multispectral, and RGB data. The results indicate that the tree model-based feature selection outperformed the traditional correlation analysis and Recursive Feature Elimination (RFE). The Bayes-RFR model demonstrated a superior validation accuracy compared to the standard Random Forest Regression and Pso-optimized models, with the R² values increasing by 27% for the maize hyperspectral data, 12% for the maize multispectral data, and 47% for the wheat hyperspectral data. These findings suggest that the proposed Bayes-RFR framework significantly enhances the stability and predictive capability of LAI estimation across various crop types, offering valuable insights for precision agriculture and crop monitoring.

Plantation distribution information is of great significance to the government’s macro-control, optimization of planting layout, and realization of efficient agricultural production. Existing studies primarily relied on high spatiotemporal resolution remote sensing data to address same-spectrum, different-object classification by extracting phenological information from temporal imagery. However, the classification problem of orchard or artificial forest, where the spectral and textural features are similar and their phenological characteristics are alike, still presents a substantial challenge. To address this challenge, we innovatively proposed a multi-index entropy weighting DTW method (ETW-DTW), building upon the traditional DTW method with single-feature inputs. In contrast to previous DTW classification approaches, this method introduces multi-band information and utilizes entropy weighting to increase the inter-class distances. This allowed for accurate classification of orchard categories, even in scenarios where the spectral textures were similar and the phenology was alike. We also investigated the impact of fusing optical and Synthetic Aperture Radar (SAR) data on the classification accuracy. By combining Sentinel-1 and Sentinel-2 time series imagery, we validated the enhanced classification effectiveness with the inclusion of SAR data. The experimental results demonstrated a noticeable improvement in orchard classification accuracy under conditions of similar spectral characteristics and phenological patterns, providing comprehensive information for orchard mapping. Additionally, we further explored the improvement in results based on two different parcel-based classification strategies compared to pixel-based classification methods. By comparing the classification results, we found that the parcel-based averaging method has advantages in clearly defining orchard boundaries and reducing noise interference. In conclusion, the introduction of the ETW-DTW method is of significant practical importance in addressing the challenge of same-spectrum, different-object classification. The obtained orchard distribution can provide valuable information for the government to optimize the planting structure and layout and regulate the macroeconomic benefits of the fruit industry.

This article mainly uses the data provided by Wordle player to predict the distribution of score results for a certain word in the future. However, the scoring results have high uncertainty. There are many evaluation indicators and impact factors. In order to predict the results more accurately, this paper proposes a BP neural network data prediction model based on genetic algorithm optimization(GA BP algorithm). We introduce three factors: time, prevalence of each word and number of repeated letters. We determine the BP neural network topology, each parameter of BP and each parameter of GA, then train it. The data correlation between the training set, validation set, test set and overall results after training is derived and the result is good. The predicted result for the term EERIE on March 1, 2023 is: 0.00%, 2.06%, 16.21%, 32.95%, 28.50%, 15.78%, 4.48% from 1 to X in that order.

Drought–flood abrupt alternation is a frequent compound stress under climate change, threatening crop production. However, how crops integrate soil, root and leaf signals for systemic adaptation remains unclear. Maize was subjected to different drought intensities (mild, moderate and severe) followed by flooding durations (3 and 6 days), with three biological replicates per treatment. Soil properties, enzyme activities, rhizosphere microbiomes, root metabolites, leaf hormones and organ C/N were measured and integrated using multivariate analyses. The stress significantly inhibited aboveground biomass, with drought and flooding exhibiting strong interactions. At the soil level, moderate drought pretreatment buffered nitrate depletion and redox decline, whereas prolonged flooding promoted ammonium accumulation. Under moderate drought followed by prolonged flooding, soil organic carbon and acid phosphatase were synergistically enhanced. At the plant level, short‐term flooding after mild drought activated root fermentation. Carbon and nitrogen allocation decoupled, with prolonged flooding after moderate drought producing “root high‐C/low‐N” versus “leaf high‐N” patterns. Hormone responses formed stress‐specific codes: prolonged flooding activated salicylic acid, whereas mild drought followed by prolonged flooding induced abscisic acid. At the system level, network and path analyses identified leaf‐derived anthranilic acid as a node linking soil redox status with leaf carbon metabolism. Rhizosphere communities underwent selection by prior drought, exhibiting succession from aerobic decomposers to anaerobic respirers and stress‐tolerant actinomycetes. Crop responses constitute a multi‐level coupled process initiated by soil conditions, modulated by rhizosphere microbiota, and integrated through plant metabolic and signalling networks. This study suggests several potential regulatory targets—moderate drought priming, microbial legacy effects, organ‐specific C/N allocation and anthranilic acid‐mediated signalling—providing a hypothesis‐generating framework for further investigation into optimizing irrigation scheduling and designing water management strategies to enhance crop resilience under climate fluctuations.