Leaf area index (LAI) is one of the key parameters to describe canopy development and growth status in apple orchards. In this study, a multimodal unmanned aerial vehicle (UAV) framework was developed for LAI estimation in the three phenological stages of apple trees, including the end-flowering, spring shoot growth, and fruit expansion. Multispectral imagery and light detection and ranging (LiDAR) point cloud data were synchronously acquired during ground measurements. A feature dataset was then fused with spectral, texture, and three-dimensional canopy structural information. Specifically, 15 spectral indices, 40 texture features, and 27 structural descriptors were extracted to characterize canopy reflectance, spatial heterogeneity, and geometric architecture, respectively. Seven regression models were evaluated, including Backpropagation neural network (BPNN), Deep neural network (DNN), Gaussian process regression (GPR), Random forest (RF), Ridge regression (RIDGE), and Support vector regression (SVR). Model performance was assessed at each phenological stage after repeated random validation. The coefficient of determination, root mean square error, and mean absolute error were used as evaluation metrics. In addition, ablation experiments were conducted to quantify the individual and combined contributions of spectral, textural, and structural information. Shapley additive explanations were used to interpret feature importance at the growth stages. The results showed that the deep neural network achieved the best overall performance and the most stable accuracy among all models. The coefficient of determination reached 0.86, 0.87, and 0.89, respectively, at the end-flowering, spring shoot growth, and fruit expansion stage after validation. Deep Neural Network was highly effective for LAI estimation under different canopy conditions and phenological stages. Furthermore, the better performance was achieved in the lower fluctuations and higher robustness to the stage-dependent canopy. Ablation analysis further demonstrated that canopy structural information was the dominant source for LAI estimation in apple trees. When used alone, structural features consistently outperformed or matched the spectral and textural feature groups. Especially, there was a strong sensitivity of canopy geometry to leaf area changes during rapid vegetative development at the new shoot growth stage, with the coefficient of determination of 0.79. Meanwhile, the structural and textural information was fused for the best dual-source performance at the new shoot growth stage. The coefficient of determination increased to 0.84, whereas the root mean square error was reduced to 0.16. A similar pattern was observed during the fruit expansion stage. Specifically, the combination of structural and textural information also produced the strongest two-source performance, with a coefficient of determination of 0.85 and a root mean square error of 0.13. Once spectral, textural, and structural information were fully integrated, the estimation accuracy further improved, with the coefficients of determination of 0.86, 0.87, and 0.89 at the three stages, respectively, while the root mean square errors were 0.14, 0.14, and 0.12, respectively. The interpretation analysis also confirmed the dominant role of canopy structural information in all growth stages. Structural descriptors with canopy density and height distribution contributed most to prediction, whereas spectral and texture information served as complementary inputs to enhance model discrimination under complex canopy conditions. Overall, multimodal feature fusion with deep learning can also provide accurate and interpretable LAI estimation for apple orchards at phenological stages. The findings can offer strong support to accurately monitor the orchard canopy.

Climate change, resource limitations, and increasing global food demand are accelerating the need for efficient and sustainable agricultural management practices. Unmanned aerial vehicles (UAVs) have emerged as a transformative technology in precision agriculture (PA) because of their capability to provide high-resolution, real-time, and site-specific crop monitoring. This review critically examines recent advancements (2016–2025) in UAV-assisted PA, focusing on UAV platforms, sensing technologies, data acquisition systems, information fusion methods, and artificial intelligence (AI)-driven analytical frameworks. Particular emphasis is placed on applications including crop monitoring, disease and pest detection, weed mapping, irrigation management, soil assessment, yield estimation, phenotyping, and precision spraying. The review highlights that integrating RGB, multispectral, hyperspectral, thermal, and LiDAR sensors with machine learning (ML) and deep learning (DL) algorithms substantially improves monitoring accuracy, operational efficiency, and agricultural decision-making compared with conventional practices. Algorithms such as Random Forest (RF), Support Vector Machine (SVM), convolutional neural networks (CNNs), and YOLO-based models have demonstrated strong effectiveness in yield prediction, disease recognition, and weed discrimination. Despite these advancements, several challenges continue to limit large-scale implementation, including restricted flight endurance, payload limitations, environmental sensitivity, data-processing complexity, interoperability issues, and limited AI model transferability across different agricultural environments. Furthermore, model performance remains highly dependent on sensor configuration, dataset quality, and field-specific environmental conditions. Recent developments indicate rapid commercialization of UAV technologies together with emerging trends in edge AI, explainable AI (XAI), UAV–IoT integration, cloud-based analytics, and autonomous multi-UAV systems. Overall, this review identifies major technological advancements, key operational limitations, and future research directions required to support scalable, reliable, and climate-resilient UAV-assisted agricultural systems.