Evapotranspiration (ET) is a key component of the water cycle, and accurate estimation of reference crop evapotranspiration (ET_o) is essential for irrigation management. To build a precise, lightweight ET_o estimation model, this study takes key meteorological factors as inputs and applies machine learning models and hybrid models Crested Porcupine Optimizer kernel extreme learning machine (CPO-KELM), the Dung Beetle Optimizer Algorithm KELM (DBO-KELM), and Particle Swarm Optimization KELM (PSO-KELM) to estimate ET_o at 38 meteorological stations across China’s seven major geographical regions. The results indicate that maximum temperature (T_max), average temperature (T_ave), and relative humidity (RH) are the primary factors affecting ET_o and were therefore used as model inputs. The standalone kernel extreme learning machine (KELM) model shows acceptable ET_o estimation performance, with R², RMSE, MAE, and NSE ranging from 0.802-0.885, 0.512-0.911, 0.464-0.970, and 0.802-0.885, respectively. Hybrid models outperform the standalone KELM, among which CPO-KELM is the most accurate: its R², RMSE, MAE, and NSE range from 0.881-0.942, 0.413-1.147, 0.284-0.763, and 0.881-0.942. At the regional scale, the CPO-KELM model exhibits its best performance in the Northeast and North China regions, with R², RMSE, MAE, and NSE ranging from 0.923-0.936, 0.413-0.511, 0.284-0.358, and 0.923-0.936, respectively. In contrast, its weakest performance is observed in parts of South China and Northwest China, with R², RMSE, MAE, and NSE ranging from 0.881-0.905, 0.675-1.147, 0.506-0.763, and 0.881-0.905. Compared to standalone KELM, CPO-KELM improves accuracy significantly: R² and NSE rise by 6.4%-9.9%, while MAE drops by 21.3%-38.8%. Thus, the hybrid CPO-KELM model effectively enhances ET_o estimation accuracy across China’s regions. Therefore, the proposed CPO-KELM hybrid model provides a high-accuracy and lightweight alternative for ET_o estimation in data-scarce regions, and offers reliable technical support for intelligent water resources management and irrigation optimization across diverse climatic zones in China.

Accurate detection of tomato leaf diseases is crucial for early prevention and ensuring agricultural production. This study addresses six tomato leaf diseases: bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, and yellow leaf curl virus. A lightweight detection model, YOLO-LGS, is proposed to achieve efficient and automated disease detection. The dataset of tomato leaf diseases was first augmented to enrich the disease features, thereby improving the model’s detection performance. The YOLO-LGS model is built on the YOLOv11 architecture, incorporating lightweight group attention net (LWGANet) to reconstruct the backbone network, replacing the convolutional block with parallel spatial attention mechanism with the grouped channel-wise self-attention (GCSA) mechanism, and introducing separated and enhanced attention module (SEAM) into the detection head to balance performance and efficiency. Experimental results show that the YOLO-LGS model achieves an mAP50 of 0.693 and an F1 score of 0.677, outperforming other YOLO models (YOLOv8s, YOLOv9s, YOLOv10s, and YOLOv11s). Additionally, the model’s parameter size is only 6.333 M, and its GFLOPs is 13.4, representing reductions of 32.739% and 37.089%, respectively, compared to YOLOv11s, significantly lowering computational cost while maintaining detection performance. The results demonstrate the effectiveness of LWGANet, GCSA, and SEAM. The development of the YOLO-LGS model provides an efficient, lightweight solution for tomato leaf disease detection in resource-constrained environments.

Accurate prediction of regional water evapotranspiration can greatly contribute to the rational utilization of regional water resources for the water resources saving. Crop evapotranspiration can be one of the most important indicators for the water evapotranspiration status of crops, in order to evaluate the soil water balance of farmland and the water management of farmland. However, the calculation of crop evapotranspiration requires a large amount of meteorological factor data. There is often redundant data with the low correlation with the evapotranspiration in meteorological data, which seriously affects the prediction accuracy and efficiency of the model. It is a high demand to extract the core environmental factors for the less-factor prediction model. The classification and regression tree (CART) can be used to make feasible for the large data sources in a short time. Then, Less factors can be extracted for the predictive analysis in a more scientific way. In this study, a less-factor water evapotranspiration model was constructed to efficiently and accurately predict the evapotranspiration data. The core factors were selected from multiple meteorological factors. 23 typical stations were selected in the nine agricultural regions, and then collected data on eight meteorological factors, such as precipitation, and sunshine hours. The meteorological factors were ranked in the order of importance using CART. The top 3-5 meteorological factors were then selected to predict the evapotranspiration using the extreme learning machine (ELM) model. At the same time, the ELM model was optimized using genetic algorithm (GA), particle swarm optimization (PSO), sparrow search algorithm (SSA). Three optimization algorithms (GA-ELM, PSO-ELM, SSA-ELM) were used to construct a less-factor hybrid optimization water evapotranspiration prediction model. Relative root mean square error (RMSE), coefficient of determination (R²), mean absolute error (MAE), and Nash-Sutcliffe coefficient (NSE) were used to evaluate the performance of the ELM and optimization model. The results showed that: 1) The main influencing factors were ranked in the order of the precipitation, sunshine duration, mean station pressure, daily maximum temperature, and average relative humidity using the importance ranking of the CART algorithm. 2) The PSO-ELM model presented the highest prediction accuracy among the three optimization algorithms. Specifically, the RMSE of evapotranspiration prediction for the 23 stations was ranged from 6.608 to 22.077 mm/d, the NSE of 0.824-0.998, R² of 0.908-0.995, and MAE of 5.075-16.677 mm/d. In addition, the RF and SVR model with the strong generalization performance were selected to compare with the PSO-ELM model. The prediction performance of three models was slightly improved with the increase of the input factors, indicating the slight overall improvement. The three models shared the strong generalization and robustness. The meteorological factors were input into the prediction model, according to the order of importance. The meteorological factors ranked the 4th and 5th were relatively less important, where the prediction accuracy was slightly improved with the increase of the input parameters. The PSO-ELM model presented the highest prediction accuracy among the three models. 3) The ELM model performed better applicability in the Yunnan-Guizhou Plateau region, the Sichuan Basin, and the surrounding areas. The three optimization algorithms showed the better applicability in the South China and the Yunnan-Guizhou Plateau region, with the highest applicability of the PSO-ELM model. The findings can provide an important reference for the crop water demand calculation in nine major agricultural regions in China.

Soil moisture is a critical component of the soil-plant-atmosphere continuum (SPAC) in fruit trees. However, high-precision monitoring of orchard soil moisture at the regional scale still remains a challenge. This study presents a two-stage feature space model to estimate root zone soil moisture using UAV remote sensing data. The results indicate that the temperature-leaf area index (TLDI) is negatively correlated with soil water content. The upper triangular space performs highly effectively for deep soil moisture inversion, with R² values ranging from 0.56 to 0.66, RMSE between 0.20 and 0.27, and RPD from 1.25 to 1.50. Conversely, the lower triangular space yields superior results for shallow soil moisture inversion, with R² values between 0.67 and 0.82, RMSE from 0.15 to 0.19, and RPD between 1.67 and 2.09. The results suggest that the lower triangular space is optimal for shallow soil moisture inversion, while the upper triangular space is more suited for deep soil moisture inversion. This study presents a novel approach for estimating deep soil moisture in orchards, providing a theoretical basis for improving soil moisture management.

To enhance the precision and efficiency of young apple fruit recognition in agricultural settings, where timely identification is crucial for effective crop management and yield optimization, this paper introduces an innovative approach utilizing the YOLOv8n model, bolstered by transfer learning techniques. The recognition of young apple fruits presents unique challenges due to the variability in size, shape, and maturity stages, as well as the dynamic environmental conditions within orchards, such as varying lighting and occlusion by leaves or branches. To tackle these challenges, the study embarked on constructing a comprehensive and diverse dataset that captures the essence of young apple fruits under a wide range of scenarios. High-resolution digital cameras were employed to gather images of young apple fruits at different times of the day and throughout various stages of growth. Special attention was given to incorporating a spectrum of lighting conditions, from bright sunlight to shaded areas, to ensure the model's robustness against illumination changes. Furthermore, fruits of different sizes and maturity levels were included to reflect the natural heterogeneity within an orchard. These images underwent rigorous preprocessing, which involved meticulous cropping to focus solely on the fruits and augmentation techniques like rotation, flipping, and color adjustment to artificially diversify the dataset and prevent overfitting. The study then compared the efficacy of the transfer learning approach against the conventional method of random weight initialization across several state-of-the-art object detection models, including RetinaNet, EfficientDet, YOLOv5n, YOLOv8n, and YOLOv10n. The objective was to assess which model, when equipped with pre-trained weights from a related domain, could best adapt to the task of identifying young apple fruits with high accuracy and efficiency. Notably, the YOLOv8n model, known for its balance between performance and computational efficiency, emerged as the top performer when enhanced with transfer learning. It achieved remarkable detection precision of 99.3%, indicating a high degree of correctness in identifying young apple fruits, coupled with a recall rate of 94.2%, which underscores its ability to capture most of the relevant instances in the dataset. The average precision (AP) of 97.0% and extended average precision (AP) across multiple IoU thresholds (83.1%) further solidified its superiority in this specific detection task.To push the boundaries of detection accuracy even further, this research introduced the EMCA (Efficient Multiscale Channel Attention) module, integrating it into the YOLOv8n framework to create the YOLOv8n-EMCA model. The EMCA module is designed to enhance the model's capability to extract and process multi-scale features, enabling it to attend more effectively to critical details across different spatial resolutions. This refinement led to slight improvements in precision (99.6%) and a notable jump in recall to 95.6%, indicating fewer missed detections. Additionally, the mean Average Precision at 50% IoU (mAP50) reached 97.3%, and the more stringent mAP50-95 metric improved to 88.2%, demonstrating the model's robustness across a range of IoU thresholds. This research not only contributes a novel methodology tailored for the identification of young apple fruits but also serves as a valuable reference for the development of similar systems for other fruit trees. The findings underscore the transformative potential of transfer learning and advanced attention mechanisms in bolstering object detection capabilities, with profound implications for advancing agricultural automation and machine vision applications. By leveraging these techniques, the agriculture sector can move closer to achieving precision farming, where real-time, accurate monitoring and decision-making become the norm.

Intelligent and automation technologies have been widely applied to agricultural production in recent years. A green apricot is one of the most significant economic fruits in Asian areas. However, traditional identification cannot fully meet large-scale production under complex environmental conditions in precision management. Fruit recognition and detection technologies can be required to enhance the accuracy and efficiency of large-scale production at present. In this study, an efficient and lightweight target recognition model (MCBPnet) was developed to automatically detect the greet apricot fruits using advanced deep learning. The CBAM (convolutional block attention module) was first integrated into the MobileNetV3 architecture. The precision of the model was significantly enhanced to identify the apricot fruits with the most rich image areas. The regions of an image were then prioritized with the most relevant to fruit detection. More computational resources were effectively allocated to the features closely associated with the task. As a result, the model structure, IRCBAM (inverted residual convolutional block attention module) was then developed to incorporate as the backbone network of MCBPnet. BiFPN (Bi-directional feature pyramid network) was also introduced into the neck network, in order to further enhance the performance of the model. A feature pyramid was constructed to capture the multi-scale features from images using the BiFPN model. The fruits with varying sizes were then detected within a single frame. A high detection rate was also maintained in a wide range of fruit sizes and orientations. Moreover, some occlusions were also removed during detection, where the fruits were partially obscured by leaves or other fruits. The PConv (partial convolution) structure was utilized in the detection head. The high accuracy of detection was achieved, even when only a portion of a fruit was visible. As such, reliable recognition was obtained to increase its accuracy and robustness under conditions of partial visibility. The results demonstrated that the MCBPnet model achieved significant advantages across key performance metrics, including precision and mAP₅₀. Specifically, the high accuracy of detection was obtained with a precision of 0.988, an mAP₅₀ of 0.994. Furthermore, the detection speed of the MCBPnet model was 109.890 frames/s, which was 70.33% higher than that of the YOLOv8n model, and the model arithmetic was 6.1 G, which was 75.31% of that of the original one. This lightweight model was also deployed on edge devices, such as drones or handheld scanners, without any energy-intensive computational resources. In summary, the high-performance metrics were achieved by the MCBPnet model. The effectiveness of the integrated techniques was validated to maintain high accuracy during agricultural automation. Various indicators represented the significant advancement in the MCBPnet model. Advanced deep learning techniques and lightweight can be expected to improve the efficiency and accuracy of apricot detection during harvesting. The findings can provide technological support to the automated recognition and picking of green apricots. This MCBPnet model can greatly contribute to modern, intelligent, and sustainable crop management, thus driving the transition toward more efficient and intelligent agricultural practices.

As a crucial fruit tree crop, the health and yield of apple trees are intricately linked to soil moisture conditions. This study aimed to integrate the enhanced WOFOST model with the HYDRUS model to simulate the growth and development of apple trees, as well as the dynamics of soil moisture under varying degrees of water deficit. The outputs of evapotranspiration (ET₀) and leaf area index (LAI) from the WOFOST model during the apple growth phase were specifically integrated with HYDRUS-1D. These parameters served as intermediaries to assess the impact of different water deficit scenarios on apple tree growth and soil moisture content. The experimental design included three levels of water deficit treatments in addition to control, with irrigation volumes for the deficit treatments set at 85%, 70%, and 55% of the control’s volume, respectively. The model-predicted LAI across all irrigation treatments exhibited an R² range of 0.89-0.95, a normalized root mean square error (NRMSE) between 8.02% and 14.57%, and yield prediction errors ranging from 6.27% to 9.61%, closely aligned with empirical data. The accuracy of simulated soil moisture content was enhanced in the 0-30 cm layer, with a slight decrease in accuracy observed in the 30-60 cm layer. For each irrigation treatment, the R² values for simulated soil moisture content ranged from 0.77 to 0.89 in the 0-30 cm layer and from 0.75 to 0.81 in the 30-60 cm layer. This study validated the capability of the WOFOST-HYDRUS model to accurately simulate the effects of varied water deficit treatments on soil moisture, LAI, and apple tree yield, providing valuable insights for developing optimal irrigation strategies.

Accurate estimation of reference crop evapotranspiration (ET₀) is essential for water resource management and irrigation scheduling. A multitude of empirical models have been employed to estimate ET₀, yielding satisfactory outcomes. However, the performance of each model is contingent upon the empirical parameters utilized. This study examines the applicability of four empirical ET₀ models, namely the Makkink (Mak), Irmark-Allen (IA), improved Baier-Robertson (MBR), and Brutsaert-Stricker (BS) models. Meteorological data from 24 weather stations across various regions in China were procured and employed to assess the ET₀ simulation results. The study employed the Differential Evolution (DE) optimization algorithm, Grey Wolf Optimizer (GWO) algorithm, and a hybrid algorithm that combines DE and GWO algorithms (DE-GWO algorithm) to optimize the parameters of the four empirical models. The findings revealed that the optimization algorithms significantly enhanced the regional adaptability of the four models, particularly the BS model. The DE-GWO algorithm demonstrated superior optimization performance (RMSE=0.055-0.372, R²=0.912-0.998, MAE=0.037-0.311, and FS=0.864-0.982) compared to the DE (RMSE=0.101-2.015, R²=0.529-0.997, MAE=0.075-1.695, and FS=0.383-0.967) and GWO (RMSE=0.158-0.915, R²=0.694-0.987, MAE=0.111-0.701, and FS=0.688-0.947) algorithms. The DE-GWO-optimized BS model was the most accurate and improved, followed by the MBR model. The IA and Mak models also showed slightly better performance after optimization with the DE-GWO algorithm. The DE-GWO-optimized BS model performed better in the southern agricultural region than in other regions. It is recommended to utilize the DE-GWO to enhance the accurate prediction of empirical ET₀ models across the nine agricultural regions of China.