Search Results (33)

To effectively address the issues of poor ventilation, light deficiency, increased pest and disease pressure, and declining fruit quality in closed-canopy walnut orchards, this study was conducted in a standard, densely planted ‘Xinwen 185’ walnut orchard. Three treatments were established: an unthinned control (CK), a 1-year thinning treatment (T1), and a 2-year thinning treatment (T2). All parameters were uniformly investigated during the 2023 growing season to analyze the effects of thinning on orchard population structure, microenvironment, leaf physiological characteristics, fruit quality, and yield. The results demonstrated that tree thinning significantly optimized the population structure: crown width expanded by 6.22–6.76 m, light transmittance increased to 27.74–33.64%, and orchard coverage decreased from 100% to 75.94–80.51%. The microenvironment was improved: inter-row temperature increased by 2.34–4.08 °C, light intensity increased by 5.38–25.29%, and relative humidity decreased by 2.15–3.30%. Furthermore, leaf physiological functions were activated: in the T2 treatment, the chlorophyll content in outer-canopy leaves increased by 15.23% and 12.45% at the kernel-hardening and maturity stages, respectively; the leaf carbon-to-nitrogen ratio increased by 18.67%; the net photosynthetic rate (Pn) during fruit expansion increased by 34.21–46.10%; and the intercellular CO₂ concentration (Ci) decreased by 10.18–10.31%. Fruit quality and yield were synergistically enhanced: single fruit weight increased by 23.39~37.94%, and kernel weight increased by 26.79–41.13%. The total sugar content in inner-canopy fruits increased by 16.50–16.67%, while the protein and fat content in outer-canopy fruits increased by 0.69–12.50% and 0.60–2.18%, respectively. Yield exhibited a “short-term adjustment and long-term gain” pattern: the T2 treatment (after 2 years of thinning) achieved a yield of 5.26 t·ha⁻¹, which was 20.38% higher than the CK. The rates of diseased fruit and empty shells decreased by 65.71% and 93.22%, respectively, and the premium fruit rate reached 90.60%. This study confirms that tree thinning is an effective measure for improving the growing environment and enhancing overall productivity in closed-canopy walnut orchards, providing a scientific basis for sustainable orchard management and increased orchard profitability. Full article

Orchard air-assisted sprayers have become key equipment for the prevention and control of fruit tree diseases and pests. However, centrifugal fans are rarely used in orchard air-assisted sprayers. To address the issue that the airflow generated by single-duct centrifugal air-assisted sprayers is insufficient to cover the lower canopy, a flow-guiding device for the lower canopy of fruit trees was designed. The Flow Simulation software of SOLIDWORKS 2021 was used to simulate the airflow field, and various structural parameters of the air outlet were analyzed to determine the optimal configuration of the upper edge inclination angle, the position of the upper air outlet, and the length of the upper air outlet. The results showed that the position of the upper air outlet had the most significant impact on the uniformity of the external flow field, followed by the upper edge inclination angle and the length of the upper air outlet. The optimal parameter settings for the air supply guiding device were determined as follows: upper edge inclination angle of 79°, upper air outlet position of 307 mm, and upper air outlet length of 190 mm. The verification test showed that the relative error between the simulated and actual airflow velocity measurements did not exceed 10%, confirming the accuracy of the simulation. The orchard field test showed that the average deposition density in the inner canopy of fruit trees was 78 particles/cm², indicating strong penetration ability; the distribution of spray droplets in the vertical direction of the canopy was uniform, meeting the requirements of fruit tree pesticide application operations. This technology provides a new approach for the application of centrifugal fans in fruit tree pesticide spraying. Full article

Bird–plant interactions are critical for maintaining biodiversity and ecosystem function, and represent a key research focus in modern ecology. Using the line transect method, we surveyed bird diversity and collected plant trait data in four habitat types in the southern zone of Fujian’s Meihuashan National Nature Reserve during October–December 2021 and July–August 2022. This study investigated how plant traits (tree height, diameter at breast height (DBH), canopy density fruit amount) influence the diversity of frugivorous birds (species richness, abundance, Shannon–Wiener, Pielou, Simpson) across four disturbed habitats—villages (residential areas), bamboo forests (economic plantations), unguarded broad-leafed forests (wild forests), and nurtured broad-leafed forests (managed forests)—during both summer (breeding season) and autumn–winter (fruiting season). The key findings revealed that (1) significant correlations between plant traits and bird diversity were exclusive to the fruiting season, with no associations found in summer; (2) during autumn–winter, the key plant traits driving bird diversity varied distinctively by habitat: tree height and canopy density were paramount in villages; both habitat structure (canopy density) and fruit amount were important in bamboo forests, whereas in both broad-leafed forests, a combination of tree structure (height, DBH, canopy density) and fruit amount determined bird abundance; (3) a significant interaction between season and habitat was detected for community evenness, indicating that habitat type modulates the seasonal effects on community composition. This study underscores that in human-modified landscapes, conserving habitat structural complexity and key resource plants is crucial for sustaining frugivorous bird diversity and its ecological functions. Conservation strategies must account for seasonal dynamics to be effective. Full article

As a globally significant oil crop, precise ripeness identification of palm fruits directly impacts harvesting efficiency and oil quality. However, the progress and application of identifying the ripeness of palm fruits have been impeded by the computational limitations of agricultural hardware and the insufficient robustness in accurately identifying palm fruits in complex on-tree environments. To address these challenges, this paper proposes an efficient recognition network tailored for complex canopy-level palm fruit ripeness assessment. Progressive combination optimization enhances the baseline network, which utilizes the YOLOv8 architecture. This study has individually enhanced the backbone network, neck, detection head, and loss function. Specifically, the backbone integrates the StarNet framework, while the detection head incorporates the lightweight LSCD structure. To enhance recognition precision, StarNet-derived Star Blocks replace standard bottleneck modules in the neck, forming optimized C2F-Star components, complemented by DIoU loss implementation to accelerate convergence. The resultant on-tree model for recognizing palm fruit ripeness achieves substantial efficiency gains. While simultaneously elevating detection precision to 76.0% mAP@0.5, our method’s GFLOPs, parameters, and model size are only 4.5 G, 1.37 M, and 2.85 MB, which are 56.0%, 46.0%, and 48.0% of the original model. The effectiveness of the model in recognizing palm fruit ripeness in complex environments, such as uneven lighting, motion blur, and occlusion, validates its robustness. Full article

Traditional rainfed fig orchards intended for fresh consumption tend to have low yields and cultural practices difficulties due to wide plant spacing and large canopies. This study investigates whether the espalier training system, commonly employed in other fruit species, can be applied to fig cultivation to improve productivity and fruit quality under high-density irrigated plantations. For the first time, four fig varieties (‘San Antonio’, ‘Dalmatie’, ‘Albacor’, and ‘De Rey’) were evaluated in a high-density system (625 trees/ha) using espalier training over four consecutive years (2018–2021) in southwestern Spain. Among the varieties, ‘Dalmatie’ demonstrated the highest suitability to the system, combining low vegetative vigour with superior yield performance, reaching a cumulative yield of 103.15 kg/tree and yield efficiency of 1.94 kg/cm². ‘San Antonio’ was the earliest to ripen and exhibited the longest harvest duration (81 days), enabling early and extended market availability. In terms of fruit quality, ‘Albacor’ stood out for its high total soluble solids content (24.97 °Brix), while ‘De Rey’ exhibited the best sugar–acid balance, with a maturity index of 384.58. The present work demonstrates that intensive fig cultivation on espalier structures offers an innovative alternative to traditional systems, thereby enhancing orchard efficiency, management, and fruit quality. Full article

Apple yield estimation is a critical task in precision agriculture, challenged by complex tree canopy structures, growth stage variability, and orchard heterogeneity. In this study, we apply multi-source feature fusion by combining vegetation indices from UAV remote sensing imagery, structural feature ratios from ground-based fruit tree images, and leaf chlorophyll content (SPAD) to improve apple yield estimation accuracy. The DeepLabv3+ network, optimized with Convolutional Block Attention Module (CBAM) and Efficient Channel Attention (ECA), improved fruit tree image segmentation accuracy. Four structural feature ratios were extracted, visible-light and multispectral vegetation indices were calculated, and feature selection was performed using Pearson’s correlation coefficient analysis. Yield estimation models were constructed using k-nearest neighbors (KNN), partial least squares (PLS), random forest (RF), and support vector machine (SVM) algorithms under both single feature sets and combined feature sets (including vegetation indices, structural feature ratios, SPAD, vegetation indices + SPAD, vegetation indices + structural feature ratios, structural feature ratios + SPAD, and the combination of all three). The optimized CBAM-ECA-DeepLabv3+ model achieved a mean Intersection over Union (mIoU) of 0.89, an 8% improvement over the baseline DeepLabv3+, and outperformed U2Net and PSPNet. The SVM model based on multi-source feature fusion achieved the highest apple yield estimation accuracy in small-scale orchard sample plots (R² = 0.942, RMSE = 12.980 kg). This study establishes a reliable framework for precise fruit tree image segmentation and early yield estimation, advancing precision agriculture applications. Full article

To address uneven airflow distribution and pesticide deposition coverage in orchard pesticide application, we developed a double-flexible duct spraying device. Utilizing FLUENT 2022 software for airflow field simulation, we analyzed various structural parameters to identify optimal configurations for the air duct type, diameter, and nozzle outlet diameter. The results indicated that the nozzle outlet diameter most significantly influences wind field uniformity, followed by the air duct diameter and type. The optimal settings were identified as follows: C-Type air duct, 100 mm duct diameter, and 50 mm nozzle outlet diameter. Validation tests confirmed these settings, with simulated and actual wind speed measurements, showing no more than a 10% relative error, affirming the simulation’s accuracy. Field tests demonstrated an average droplet density of 35.38 droplets/cm² within tree canopies, indicating strong penetration ability. Droplet distribution followed a lower > middle > upper pattern in the canopy’s vertical direction, fulfilling technical requirements for high spindle-shaped fruit trees and providing a foundation for achieving a uniform canopy coverage. Full article

Autonomous navigation is key to improving efficiency and addressing labor shortages in the fruit industry. Semi-structured orchards, with straight tree rows, dense weeds, thick canopies, and varying light conditions, pose challenges for tree identification and navigation line extraction. Traditional 3D lidars suffer from a narrow vertical FoV, sparse point clouds, and high costs. Furthermore, most lidar-based tree-row-detection algorithms struggle to extract high-quality navigation lines in scenarios with thin trunks and dense foliage occlusion. To address these challenges, we developed a 3D perception system using a servo motor to control the rolling motion of a 2D lidar, constructing 3D point clouds with a wide vertical FoV and high resolution. In addition, a method for trunk feature point extraction and tree row line detection for autonomous navigation has been proposed, based on trunk geometric features and RANSAC. Outdoor tests demonstrate the system’s effectiveness. At speeds of 0.2 m/s and 0.5 m/s, the average distance errors are 0.023 m and 0.016 m, respectively, while the average angular errors are 0.272° and 0.146°. This low-cost solution overcomes traditional lidar-based navigation method limitations, making it promising for autonomous navigation in semi-structured orchards. Full article

Mango (Mangifera Indica L.), a key horticultural crop, particularly in Pakistan, has been primarily studied locally using low- to medium-resolution satellite imagery, usually focusing on a particular phenological stage. The large canopy size, complex tree structure, and unique phenology of mango trees further accentuate intrinsic challenges posed by low-spatiotemporal-resolution data. The absence of mango-specific vegetation indices compounds the problem of accurate health classification and yield estimation at the tree level. To overcome these issues, this study utilizes high-resolution multi-spectral UAV imagery collected from two mango orchards in Multan, Pakistan, throughout the annual phenological cycle. It introduces MangiSpectra, an integrated two-staged framework based on Long Short-Term Memory (LSTM) networks. In the first stage, nine conventional and three mango-specific vegetation indices derived from UAV imagery were processed through fine-tuned LSTM networks to classify the health of individual mango trees. In the second stage, associated data such as the trees’ age, variety, canopy volume, height, and weather data were combined with predicted health classes for yield estimation through a decision tree algorithm. Three mango-specific indices, namely the Mango Tree Yellowness Index (MTYI), Weighted Yellowness Index (WYI), and Normalized Automatic Flowering Detection Index (NAFDI), were developed to measure the degree of canopy covered by flowers to enhance the robustness of the framework. In addition, a Cumulative Health Index (CHI) derived from imagery analysis after every flight is also proposed for proactive orchard management. MangiSpectra outperformed the comparative benchmarks of AdaBoost and Random Forest in health classification by achieving 93% accuracy and AUC scores of 0.85, 0.96, and 0.92 for the healthy, moderate and weak classes, respectively. Yield estimation accuracy was reasonable with $R^2=0.21$, and $\mathrm{RMSE}=50.18$. Results underscore MangiSpectra’s potential as a scalable precision agriculture tool for sustainable mango orchard management, which can be improved further by fine-tuning algorithms using ground-based spectrometry, IoT-based orchard monitoring systems, computer vision-based counting of fruit on control trees, and smartphone-based data collection and insight dissemination applications. Full article

Accurately predicting grapevine yield and quality is critical for optimising vineyard management and ensuring economic viability. Numerous studies have reported the complexity in modelling grapevine yield and quality due to variability in the canopy structure, challenges in incorporating soil and microclimatic factors, and management practices throughout the growing season. The use of multimodal data and machine learning (ML) algorithms could overcome these challenges. Our study aimed to assess the potential of multimodal data (hyperspectral vegetation indices (VIs), thermal indices, and canopy state variables) and ML algorithms to predict grapevine yield components and berry composition parameters. The study was conducted during the 2019/20 and 2020/21 grapevine growing seasons in two South Australian vineyards. Hyperspectral and thermal data of the canopy were collected at several growth stages. Simultaneously, grapevine canopy state variables, including the fractional intercepted photosynthetically active radiation (fiPAR), stem water potential (Ψ_stem), leaf chlorophyll content (LCC), and leaf gas exchange, were collected. Yield components were recorded at harvest. Berry composition parameters, such as total soluble solids (TSSs), titratable acidity (TA), pH, and the maturation index (IMAD), were measured at harvest. A total of 24 hyperspectral VIs and 3 thermal indices were derived from the proximal hyperspectral and thermal data. These data, together with the canopy state variable data, were then used as inputs for the modelling. Both linear and non-linear regression models, such as ridge (RR), Bayesian ridge (BRR), random forest (RF), gradient boosting (GB), K-Nearest Neighbour (KNN), and decision trees (DTs), were employed to model grape yield components and berry composition parameters. The results indicated that the GB model consistently outperformed the other models. The GB model had the best performance for the total number of clusters per vine (R² = 0.77; RMSE = 0.56), average cluster weight (R² = 0.93; RMSE = 0.00), average berry weight (R² = 0.95; RMSE = 0.00), cluster weight (R² = 0.95; RMSE = 0.13), and average berries per bunch (R² = 0.93; RMSE = 0.83). For the yield, the RF model performed the best (R² = 0.97; RMSE = 0.55). The GB model performed the best for the TSSs (R² = 0.83; RMSE = 0.34), pH (R² = 0.93; RMSE = 0.02), and IMAD (R² = 0.88; RMSE = 0.19). However, the RF model performed best for the TA (R² = 0.83; RMSE = 0.33). Our results also revealed the top 10 predictor variables for grapevine yield components and quality parameters, namely, the canopy temperature depression, LCC, fiPAR, normalised difference infrared index, Ψ_stem, stomatal conductance (g_s), net photosynthesis (P_n), modified triangular vegetation index, modified red-edge simple ratio, and ANT_gitelson index. These predictors significantly influence the grapevine growth, berry quality, and yield. The identification of these predictors of the grapevine yield and fruit composition can assist growers in improving vineyard management decisions and ultimately increase profitability. Full article

The canopy of perennial evergreen fruit trees in southern China has a unique Bidirectional Reflectance Factor (BRF) due to its complex multi-branch structure and density changes. This study aimed to address the lack of clarity regarding the changes in BRF of evergreen fruit trees in southern China. Litchi, a typical fruit tree in this region, was chosen as the subject for establishing a three-dimensional (3D) real structure model. The canopy BRF of litchi was simulated under different leaf components, illumination geometry, observed geometry, and leaf area index (LAI) using a 3D radiation transfer model. The corresponding changes in characteristics were subsequently analyzed. The findings indicate that the chlorophyll content and equivalent water thickness of leaves exert significant influences on canopy BRF, whereas the protein content exhibit relatively weak effects. Variation in illumination and observation geometry results in the displacement of hotspots, with the solar zenith angle and view zenith angle exerting significant influence on the BRF. As the LAI of the litchi orchard increases, the distribution of hotspots becomes more concentrated, and the differences in angle information are relatively smaller when observed from multiple angles. With the increase in LAI in litchi orchards, the BRF on the principal plane would be saturated, but observation at hotspots could alleviate this phenomenon. The above analysis provides a reference for quantitative inversion of vegetation parameters using remote sensing monitoring information of typical perennial evergreen fruit trees. Full article

There is a consensus that fire should be actively managed in tropical savannas to decrease wildfire risks, firefighting costs, and social conflicts as well as to promote ecosystem conservation. Selection and participatory monitoring of the effects of fire on cultural keystone species may be an efficient way to involve local stakeholders and inform management decisions. In this study, we investigated the effects of different fire regimes on a cultural keystone species in Central Brazil. With the support of diverse multiethnic groups of local fire brigades, we sampled Hancornia speciosa (Apocynaceae) populations across a vast regional range of 18 traditional territories (Indigenous Lands and Quilombola Territories) as well as four restricted Protected Areas. We considered areas under wildfires (WF), prescribed burns (PB) and fire exclusion (FE) and quantified tree mortality, canopy damage, loss of reproductive structures and fruit production following a simplified field protocol. Areas with H. speciosa populations were identified and classified according to their fire history, and in each sampled area, adult plants were evaluated. We hypothesized that WF would have larger negative impact on the population parameters measured, while FE would increase plant survival and fruit production. We found that tree mortality, canopy damage, and loss of reproductive structures were higher in areas affected by wildfires, which also had the lowest fruit production per plant compared to PB and FE areas, corroborating our hypotheses. However, we also found higher mortality in FE areas compared to PB ones, probably due to plant diseases in areas with longer FE. Considering these results and that the attempts to exclude fire from fire-prone ecosystems commonly lead to periodic wildfires, we argue that the Integrated Fire Management program in course in federal Protected Areas in Brazil—based on early dry season prescribed fires—is a good management option for this, and likely other, cultural keystone species in the Brazilian savanna. Full article

This article addresses the challenges of measuring the 3D architecture traits, such as height and volume, of fruit tree canopies, constituting information that is essential for assessing tree growth and informing orchard management. The traditional methods are time-consuming, prompting the need for efficient alternatives. Recent advancements in unmanned aerial vehicle (UAV) technology, particularly using Light Detection and Ranging (LiDAR) and RGB cameras, have emerged as promising solutions. LiDAR offers precise 3D data but is costly and computationally intensive. RGB and photogrammetry techniques like Structure from Motion and Multi-View Stereo (SfM-MVS) can be a cost-effective alternative to LiDAR, but the computational demands still exist. This paper introduces an innovative approach using UAV-based single-lens stereoscopic photography to overcome these limitations. This method utilizes color variations in canopies and a dual-image-input network to generate a detailed canopy height map (CHM). Additionally, a block structure similarity method is presented to enhance height estimation accuracy in single-lens UAV photography. As a result, the average rates of growth in canopy height (CH), canopy volume (CV), canopy width (CW), and canopy project area (CPA) were 3.296%, 9.067%, 2.772%, and 5.541%, respectively. The r² values of CH, CV, CW, and CPA were 0.9039, 0.9081, 0.9228, and 0.9303, respectively. In addition, compared to the commonly used SFM-MVS approach, the proposed method reduces the time cost of canopy reconstruction by 95.2% and of the cost of images needed for canopy reconstruction by 88.2%. This approach allows growers and researchers to utilize UAV-based approaches in actual orchard environments without incurring high computation costs. Full article

Mechanical weed management is a drudging task that requires manpower and has risks when conducted within rows of orchards. However, intrarow weeding must still be conducted by manual labor due to the restricted movements of riding mowers within the rows of orchards due to their confined structures with nets and poles. However, autonomous robotic weeders still face challenges identifying uncut weeds due to the obstruction of Global Navigation Satellite System (GNSS) signals caused by poles and tree canopies. A properly designed intelligent vision system would have the potential to achieve the desired outcome by utilizing an autonomous weeder to perform operations in uncut sections. Therefore, the objective of this study is to develop a vision module using a custom-trained dataset on YOLO instance segmentation algorithms to support autonomous robotic weeders in recognizing uncut weeds and obstacles (i.e., fruit tree trunks, fixed poles) within rows. The training dataset was acquired from a pear orchard located at the Tsukuba Plant Innovation Research Center (T-PIRC) at the University of Tsukuba, Japan. In total, 5000 images were preprocessed and labeled for training and testing using YOLO models. Four versions of edge-device-dedicated YOLO instance segmentation were utilized in this research—YOLOv5n-seg, YOLOv5s-seg, YOLOv8n-seg, and YOLOv8s-seg—for real-time application with an autonomous weeder. A comparison study was conducted to evaluate all YOLO models in terms of detection accuracy, model complexity, and inference speed. The smaller YOLOv5-based and YOLOv8-based models were found to be more efficient than the larger models, and YOLOv8n-seg was selected as the vision module for the autonomous weeder. In the evaluation process, YOLOv8n-seg had better segmentation accuracy than YOLOv5n-seg, while the latter had the fastest inference time. The performance of YOLOv8n-seg was also acceptable when it was deployed on a resource-constrained device that is appropriate for robotic weeders. The results indicated that the proposed deep learning-based detection accuracy and inference speed can be used for object recognition via edge devices for robotic operation during intrarow weeding operations in orchards. Full article

Orchard thinning can avoid biennial bearing and improve fruit quality, which is a necessary agronomic section in orchard management. The existing methods of artificial fruit thinning and chemical spraying are no longer suitable for the development of modern agriculture. With the continuous acceleration of the construction process of modern orchards, blossom thinning mechanization has become an inevitable trend in the development of the orchard flower and fruit management. Based on relevant reports in the past 20 years, the paper discusses the current level of development of mechanized blossom thinning technologies and equipment in orchards from three aspects: mechanism research, machine development, and intelligent upgrading. Firstly, for thinning mechanism research, three directions were investigated: the rope flexible hitting force, thinning agronomic requirements, and the fruit tree growth model between thinning and fruit yields. Secondly, for marketable machine developments, two types of machines were investigated: the hand-held thinner and tractor-mounted thinner. The hand-held thinner is mainly suitable for traditional old orchards with a messy canopy structure, especially in the interior and top of the canopy. The tractor-mounted thinner is mainly suitable for orchards with the same crown structure, such as the hedge type, trunk type, and V-type. Thirdly, for equipment intelligent upgrading, the research of the intelligent detection algorithm for inflorescence on the fruit tree was investigated, for species including the apple, pear, citrus, grape, litchi, mango, and apricot. Finally, combining the advantages and disadvantages of the research, the authors propose thoughts and prospects, which can provide a reference for the design and applications of orchard mechanized blossom thinning. Full article