Leaf chlorophyll content (LCC) is one of the most important factors affecting photosynthetic capacity and nitrogen status, both of which influence crop harvest. However, the development of rapid and nondestructive methods for leaf chlorophyll estimation is a topic of much interest. Hence, this study explored the use of the machine learning approach to enhance the estimation of leaf chlorophyll from spectral reflectance data. The objective of this study was to evaluate four different approaches for estimating the LCC of apple tree leaves at five growth stages (the 1st, 2nd, 3rd, 4th and 5th growth stages): (1) univariate linear regression (ULR); (2) multivariate linear regression (MLR); (3) support vector regression (SVR); and (4) random forest (RF) regression. Samples were collected from the leaves on the eastern, western, southern and northern sides of apple trees five times (1st, 2nd, 3rd, 4th and 5th growth stages) over three consecutive years (2016–2018), and experiments were conducted in 10–20-year-old apple tree orchards. Correlation analysis results showed that LCC and ST, LCC and vegetation indices (VIs), and LCC and three edge parameters (TEP) had high correlations with the first-order differential spectrum (FODS) (0.86), leaf chlorophyll index (LCI) (0.87), and (SD_r − SD_b)/ (SD_r + SD_b) (0.88) at the 3rd, 3rd, and 4th growth stages, respectively. The prediction models of different growth stages were relatively good. The MLR and SVR models in the LCC assessment of different growth stages only reached the highest R² values of 0.79 and 0.82, and the lowest RMSEs were 2.27 and 2.02, respectively. However, the RF model evaluation was significantly better than above models. The R² value was greater than 0.94 and RMSE was less than 1.37 at different growth stages. The prediction accuracy of the 1st growth stage (R² = 0.96, RMSE = 0.95) was best with the RF model. This result could provide a theoretical basis for orchard management. In the future, more models based on machine learning techniques should be developed using the growth information and physiological parameters of orchards that provide technical support for intelligent orchard management.

The use of a fast and accurate unmanned aerial vehicle (UAV) digital camera platform to estimate leaf area index (LAI) of kiwifruit orchard is of great significance for growth, yield estimation, and field management. LAI, as an ideal parameter for estimating vegetation growth, plays a significant role in reflecting crop physiological process and ecosystem function. At present, LAI estimation mainly focuses on winter wheat, corn, soybean, and other food crops; in addition, LAI on forest research is also predominant, but there are few studies on the application of orchards such as kiwifruit. Concerning this study, high-resolution UAV images of three growth stages of kiwifruit orchard were acquired from May to July 2021. The extracted significantly correlated spectral and textural parameters were used to construct univariate and multivariate regression models with LAI measured for corresponding growth stages. The optimal model was selected for LAI estimation and mapping by comparing the stepwise regression (SWR) and random forest regression (RFR). Results showed the model combining texture features was superior to that only based on spectral indices for the prediction accuracy of the modeling set, with the R² of 0.947 and 0.765, RMSE of 0.048 and 0.102, and nRMSE of 7.99% and 16.81%, respectively. Moreover, the RFR model (R² = 0.972, RMSE = 0.035, nRMSE = 5.80%) exhibited the best accuracy in estimating LAI, followed by the SWR model (R² = 0.765, RMSE = 0.102, nRMSE = 16.81%) and univariate linear regression model (R² = 0.736, RMSE = 0.108, nRMSE = 17.84%). It was concluded that the estimation method based on UAV spectral parameters combined with texture features can provide an effective method for kiwifruit growth process monitoring. It is expected to provide scientific guidance and practical methods for the kiwifruit management in the field for low-cost UAV remote sensing technology to realize large area and high-quality monitoring of kiwifruit growth, thus providing a theoretical basis for kiwifruit growth investigation.

Detecting irrigation dynamics at the field scale is essential for regulating agricultural water resources. Irrigation, as an episodic management behavior embedded in the soil-plant-atmosphere continuum (SPAC), is governed by complex biophysical factors, making it challenging to detect discrete events using traditional remote sensing or empirical modeling. We propose a Knowledge-Guided Stacked-differential-window Neural-network (KGSN) framework to address these challenges. By utilizing a process-based WOFOST-SAR model as a mechanism generator, the framework overcomes the critical bottleneck of field-scale label scarcity. The method integrates daily soil moisture dynamics, precipitation, and irregular radar backscatter to simultaneously resolve irrigation occurrence, timing (Day of Year, DOY), frequency, and water volume. Independent validation across the North China Plain (NCP) from 2015 to 2020 demonstrates high detection reliability, with an overall accuracy (OA) of 0.90 and a root mean square error (RMSE) for timing of 3.6 days. Feature attribution analysis reveals that the continuous soil moisture background and sudden radar pulses form an information-level compensation mechanism, enabling the model to maintain robust detection even under irregular satellite observations. Regional mapping at 30-m resolution reveals that irrigation intensity is primarily regulated through event frequency, exhibiting a structured spatial compensation for precipitation deficits. This physically consistent approach provides a scalable tool for precision irrigation monitoring and regional water resource assessment.

Detecting irrigation dynamics at the field scale is essential for regulating agricultural water resources. Irrigation, as an episodic management behavior embedded in the soil-plant-atmosphere continuum (SPAC), is governed by complex biophysical factors, making it challenging to detect discrete events using traditional remote sensing or empirical modeling. We propose a Knowledge-Guided Stacked-differential-window Neural-network (KGSN) framework to address these challenges. By utilizing a process-based WOFOST-SAR model as a mechanism generator, the framework overcomes the critical bottleneck of field-scale label scarcity. The method integrates daily soil moisture dynamics, precipitation, and irregular radar backscatter to simultaneously resolve irrigation occurrence, timing (Day of Year, DOY), frequency, and water volume. Independent validation across the North China Plain (NCP) from 2015 to 2020 demonstrates high detection reliability, with an overall accuracy (OA) of 0.90 and a root mean square error (RMSE) for timing of 3.6 days. Feature attribution analysis reveals that the continuous soil moisture background and sudden radar pulses form an information-level compensation mechanism, enabling the model to maintain robust detection even under irregular satellite observations. Regional mapping at 30-m resolution reveals that irrigation intensity is primarily regulated through event frequency, exhibiting a structured spatial compensation for precipitation deficits. This physically consistent approach provides a scalable tool for precision irrigation monitoring and regional water resource assessment.