The efficient and precise identification of rice growth stages through remote sensing technology holds critical significance for varietal breeding optimization and production management enhancement. Remote sensing, characterized by high spatial-temporal resolution and automated monitoring capabilities, provides transformative solutions for large-scale dynamic phenology monitoring, offering essential technical support to address climate change impacts and food security challenges in complex agroecosystems where precise monitoring of growth stage transitions enables yield prediction and stress-resilient cultivation management.

In recent years, the technical system for monitoring rice growth stages has achieved systematic breakthroughs in the perception layer, decision-making layer, and execution layer, forming a technological ecosystem covering the entire chain of "data acquisition-feature analysis-intelligent decision-making-precise operation". At the perception layer, a "space-air-ground" three-dimensional monitoring network has been constructed: High-altitude satellites (Sentinel-2, Landsat) realize regional-scale phenological dynamic tracking through wide-spectrum multi-temporal observations; low-altitude unmanned aerial vehicle (UAV) equipped with hyperspectral and light detection and ranging (LiDAR) sensors analyze the heterogeneity of canopy three-dimensional structure; near-ground sensor networks real-timely capture leaf-scale photosynthetic efficiency and nitrogen metabolism parameters. Radiometric calibration and temporal interpolation algorithms eliminate the spatio-temporal heterogeneity of multi-source data, forming continuous and stable monitoring capabilities. Innovations in technical methods show three integration trends: Firstly, multimodal data collaboration mechanisms break through the physical characteristic barriers between optical and radar data; secondly, deep integration of mechanistic models and data-driven approaches embeds the scattering by arbitrarily inclined leaves by arbitrary inclined leaves (PROSPECT + SAIL, PROSAIL) radiative transfer model into the long short-term memory (LSTM) network architecture; thirdly, cross-scale feature analysis technology breaks through by constructing organ-population association models based on dynamic attention mechanisms, realizing multi-granularity mapping between panicle texture features and canopy leaf area index (LAI) fluctuations. The current technical system has completed three-dimensional leaps: From discrete manual observations to full-cycle continuous perception, with monitoring frequency upgraded from weekly to hourly; from empirical threshold-based judgment to mechanism-data hybrid-driven, the cross-regional generalization ability of the model can be significantly improved; from independent link operations to full-chain collaboration of "perception-decision-execution", constructing a digital management closed-loop covering rice sowing to harvest, providing core technical support for smart farm construction.

Current technologies face three-tiered challenges in data heterogeneity, feature limitations and algorithmic constraints. Future research should focus on three aspects: 1) Multi-source data assimilation systems to reconcile spatiotemporal heterogeneity through UAV-assisted satellite calibration and GAN-based cloud-contaminated data reconstruction; 2) Cross-scale physiological-spectral models integrating 3D canopy architecture with adaptive soil-adjusted indices to overcome spectral saturation; 3) Mechanism-data hybrid paradigms embedding thermal-time models into LSTM networks for environmental adaptation, developing lightweight CNNs with multi-scale attention for occlusion-resistant panicle detection, and implementing transfer learning for cross-regional model generalization. The convergence of multi-source remote sensing, intelligent algorithms, and physiological mechanisms will establish a full-cycle dynamic monitoring system based on agricultural big data.

Rice leaf area is a critical physiological metric that indicates photosynthetic efficiency, energy conversion, and dry matter accumulation capacity. This study aimed to develop a simple and efficient rice leaf area imaging system and prediction method, so as to provide a theoretical foundation and technical support for rapid and accurate leaf area measurement.

The study utilized representative rice varieties—Xiushui 134 (indica), Huanghuazhan (japonica), and Yongyou 1540 (indica-japonica hybrid)—as experimental materials. Leaf area data were collected from the aboveground parts during critical growth periods, and both flat-overhead-view and side-view images were captured. Using the PlantScreen high-throughput modular plant phenotyping platform, morphological and color feature information was extracted. Based on these data, various feature selection methods (Pearson correlation coefficient, maximal information coefficient (MIC), and recursive feature elimination (RFE)) combined with machine learning models (support vector regression (SVR), random forest regression (RFR), and XGBoost) and deep learning models (ResNet50, AlexNet, VGG, and SeNet) were employed to develop a simplified and efficient rice leaf area prediction model.

(1) An imaging approach that integrated flat-overhead and multi-angle side views significantly outperformed single-view methods for leaf area prediction, with R² values of 0.76–0.82 and coefficients of variation (CV) of 5.5%–13.7%, compared with R² values of 0.51–0.78 and CVs of 9.7%–27.5% for single views. The optimal system used one flat-overhead-view and one side-view image, achieving R² = 0.79, root mean square error (RMSE) = 95.3, mean absolute error (MAE) = 77.02, and CV = 6.5%. (2) Using MIC algorithm for key feature selection combined with the random forest regression model achieved excellent results (R² = 0.84, RMSE = 81.8, and MAE = 63.3), noticeably outperforming other machine learning models. The deep learning model SeNet (R² = 0.80, RMSE = 98.1, and MAE = 74.7) outperformed traditional ResNet50 and AlexNet models but showed no significant advantage over the MIC-RFR model. (3) Feature analysis indicated that the projected area and plant height from side-view images, as well as leaf perimeter and green-yellow characteristics from flat-overhead-view images, significantly contributed to leaf area prediction. The contribution of the side-view projected area (+117.4) was substantially greater than that of other features (ranging from 1.48 to 18.87).

This study employed a simple and efficient leaf area prediction imaging system (one flat-overhead-view combined with one side-view image), integrated with the MIC-RFR model, to meet the high-precision and stable prediction requirements for individual rice leaf area. This method provided a powerful tool and technical support for precision agriculture and crop breeding.