Search Results (1,598)

The use of UAVs to detect litchi in natural environments is imperative for rapid litchi yield estimation and automated harvesting systems. However, UAV-based lychee fruit detection bottlenecks arise from complex canopy architecture and leaf occlusion. This study proposed a collaborative optimization strategy integrating agronomic technique with deep learning. Three leaf thinning intensities (0, 6, and 12 compound leaves) were applied at the early stage of fruit to systematically evaluate their effects on fruit growth, canopy structure, and detection performance. Results indicated that moderate defoliation (six leaves) significantly enhanced canopy openness and light penetration without adversely impacting on yield and fruit quality. Subsequent UAV-based detection under moderate versus no defoliation treatment revealed that the YOLOv8-based model achieved significant performance gains: mean average precision (mAP) increased from 0.818 to 0.884, and the F1-score improved from 0.796 to 0.842. The study contributes a novel collaborative optimization strategy that effectively mitigates occlusion issues in fruit detection. This approach demonstrates that agronomic techniques can be strategically used to enhance AI perception, offering a significant step forward in the integration of agricultural machinery and agronomy for intelligent orchard systems. Full article

Despite the extensive cultivation of Moringa trees in tropical regions, understanding of accession-specific performance across diverse agroecological zones remains inadequate. Thus, this study evaluated the growth, adaptability, and leaf yield performance of 12 Moringa accessions (11 M. oleifera and 1 M. stenopetala) over three years in a subtropical climate (Pretoria, South Africa). Seeds were planted in seedling trays in the glasshouse at the University of Pretoria’s experimental farm. Vigorous seedlings were transplanted to the field at the Roodeplaat experimental site of the Agricultural Research Council two months after establishment, following a randomized complete block design (RCBD). Data were measured on establishment (emergence, survival), growth and yield parameters, and monitored plant health via leaf greenness, vigour, chlorosis, and pest and disease incidence. Accessions exhibited substantial variation for most traits, except for stem diameter. Moringa stenopetala showed the highest initial emergence rate but later displayed lower survival rates than most M. oleifera accessions. Survival rates, morphological features (plant height, canopy diameter, and branching), visual scores for leaf greenness and plant vigour, and leaf yield (fresh and dry) varied considerably among the accessions. Moringa oleifera A2 consistently performed well, exhibiting vigorous growth, the maximum survival rate (78%), and fresh leaf production (6206 kg ha⁻¹). Accessions A3 and A8 showed intermediate yield and longevity, indicating potential for cultivation or breeding. Conversely, M. oleifera A10 and M. stenopetala markedly underperformed in most traits, limiting their cultivation potential. Based on multi-year performance, A2 is suggested for large-scale cultivation due to its vigour, yield, and stress tolerance, while A3 and A8 hold breeding potential. The study emphasizes the critical role of genetic variation and selection in enhancing Moringa productivity under subtropical environments. Future work should focus on genetic characterization and agronomic practices optimization of superior accessions. Full article

This study examines the impact of canopy structural characteristics on pedestrians’ visual perception and psychophysiological responses along four roads in the subtropical city of Guangzhou: Huadi Avenue, Jixiang Road, Yuejiang Middle Road, and Huan Dao Road. A Canopy Structural Index (CSI) was innovatively developed by integrating tree height, crown width, diffuse non-interceptance, and leaf area index, establishing a five-tier quantitative grading system. The study used multimodal data fusion techniques combined with heart rate variability (HRV) analysis and eye-tracking experiments to quantitatively decipher the patterns of autonomic nervous regulation and visual attention allocation under different levels of CSI. The results demonstrate that CSI levels are significantly correlated with psychological relaxation states: as CSI levels increase, time-domain HRV metrics (SDNN and RMSSD) rise by 15%–43%, while the frequency-domain metric (LF/HF) decreases by 31%, indicating enhanced parasympathetic activity and a transition from stress to relaxation. Concurrently, the allocation of visual attention toward canopies intensifies. The proportion of fixation duration increases to nearly 50%, and the duration of the first fixation extends by 0.3–0.8 s. The study proposes CSI ≤ 0.15 as an optimization threshold, offering scientific guidance for designing and pruning subtropical urban street tree canopies. Full article

The prolonged retention of senescent branches and needles (canopy litter) in Cunninghamia lanceolata canopies is an evolutionary adaptation, yet its impacts on stand microenvironment and understory succession remain poorly quantified. To address this gap, we conducted a 5-year field experiment across six planting densities (1800, 2400, 3000, 3600, 4200, and 4800 trees·ha⁻¹), aiming to evaluate the effects of canopy litter removal on canopy structure, forest light environment, and understory biodiversity. Results demonstrated that leaf area index (LAI) and mean tilt angle of the leaf (MTA) significantly increased with density (p < 0.05), leading to marked reductions in photosynthetic photon flux density (PPFD) and light transmittance (T). Canopy litter removal significantly reduced LAI across all densities after 4–5 years (p < 0.05) and consistently enhanced PPFD and transmittance (p < 0.01). MTA and light quality parameters (red:blue and red:far-red ratios) both exhibited variable responses to litter removal, driven by density and time interactions, with effects diminishing over time. Understory vegetation diversity exhibited pronounced temporal dynamics and density-dependent responses to canopy litter removal, with increases in species richness (S), Simpson diversity (D), and Shannon–Wiener diversity (H), while Pielou Evenness (J) responded more variably. The most notable increase in species richness occurred in the 4th year, when 21 new species were recorded, largely due to the expansion of light-demanding bamboos (e.g., Indocalamus tessellatus and Pleioblastus amarus), heliophilic grasses (e.g., Lophatherum gracile) and pioneer ferns (e.g., Pteris dispar and Microlepia hancei). Correlation analyses confirmed PPFD as a key positive driver of all diversity indices (p < 0.01), whereas LAI was significantly negatively correlated with PPFD, light transmittance, and understory diversity (p < 0.01). These findings demonstrate that strategic management of canopy litter incorporating stand density regulation can improve understory light availability, thereby facilitating heliophilic species recruitment and biodiversity enhancement in subtropical coniferous plantations. Full article

As a staple cereal worldwide, winter wheat plays a pivotal role in food security. Leaf chlorophyll serves as a direct indicator of photosynthetic performance and nitrogen nutrition, making it critical for precision management and yield gains. Consequently, rapid, nondestructive, and high-accuracy remote-sensing retrievals are urgently needed to underpin field operations and precision fertilization. In this study, canopy hyperspectral reflectance together with destructive chlorophyll assays were systematically acquired from Yangling field trials conducted during 2018–2020. Three families of spectral indices were devised: classical empirical indices; two-dimensional optimal spectral indices (2D OSI) selected by correlation-matrix screening; and novel three-dimensional optimal spectral indices (3D OSI). The main contribution lies in devising novel 3D OSIs that combine three spectral bands and demonstrating how their fusion with classic two-band indices can improve chlorophyll quantification. Correlation analysis showed that most empirical vegetation indices were significantly associated with chlorophyll (p < 0.05), with the new double difference index (NDDI) giving the strongest relationship (R = 0.637). Within the optimal-index sets, the difference three-dimensional spectral index (DTSI; 680, 807, and 1822 nm) achieved a correlation coefficient of 0.703 (p < 0.05). Among all multi-input fusion schemes, fusing empirical indices with 3D OSI and training with RF delivered the best validation performance (R² = 0.816, RMSE = 0.307 mg g⁻¹, MRE = 11.472%), and external data further corroborated its feasibility. Altogether, integrating 3D spectral indices with classical vegetation indices and deploying RF enabled accurate, nondestructive estimation of winter wheat chlorophyll, offering a new hyperspectral pathway for monitoring crop physiological status and advancing precision agricultural management and fertilization, can guide in-season fertilization to optimize nitrogen use, thereby advancing precision agriculture. Full article

Pruning is labor-intensive and increases production costs, while mechanical pruning offers a promising alternative. However, research on its effectiveness remains limited. To address this gap, we evaluated the effects of mechanical pruning over two consecutive years (2023 and 2024) on tree growth, yield, and fruit quality of ‘Arisoo’ apple trees. The treatment included hand (manual) pruning (HP), mechanical pruning (MP), and combined mechanical and hand pruning (MP + HP) applied during winter pruning in a super-spindle-slender-shaped apple orchard. MP significantly reduced pruning time; however, the amount of plant biomass removed was lower in the MP treatment than in the HP and MP + HP treatments. Canopy volume was higher in the HP treatment than in MP and MP + HP treatments; however, the pruning treatments did not affect trunk cross-sectional area or tree yield. Leaf chlorophyll and nitrogen contents were slightly lower in the MP treatment than in the HP treatment in 2023 but were not affected in 2024. The MP treatment also noticeably reduced light penetration within the canopy and produced smaller fruits with lower soluble solids content and poorer coloration at harvest compared to the HP and MP + HP treatments. In contrast, the HP and MP + HP treatments showed similar effects on light penetration, yield, fruit size, and fruit quality; however, the MP + HP treatment significantly reduced the pruning time compared with the HP treatment. Overall, this study found that MP reduced light penetration and produced smaller and poorly colored fruits, whereas a follow-up combination of HP after MP improved pruning efficiency, light penetration, fruit size, and fruit quality. Full article

Detecting citrus Huanglongbing (HLB) in orchard environments is particularly challenging due to multi-scale targets and occlusions due to clustering, which manifest as complex and variable backgrounds, targets ranging from distant single leaves to nearby full canopies, and frequent instances where symptomatic leaves are hidden behind others, all significantly hindering accurate detection. To overcome these challenges, this study introduces a novel citrus object detection model, Multi-Scale Huanglongbing DETR (MSHLB-DETR), developed on the basis of an improved Real-Time DEtection TRansformer (RT-DETR). The model significantly enhances detection accuracy and efficiency for HLB under complex orchard conditions. To address the issue of small target feature loss in leaf detection, a new efficient transformer module called Smart Disease Recognition for Citrus Huanglongbing with Multi-scale (SDRM) is introduced. SDRM includes a space-to-depth (SPD) module and inverted residual mobile block (IRMB), which facilitate deep interaction between local and global features and significantly improve the computational efficiency of the transformer. Additionally, the transformer encoder incorporates a Context-Guided Block (CGBlock) for contextual feature learning. To evaluate the proposed model under complex background conditions, a dataset of 4367 images was collected from diverse orchard scenes, preprocessed, and divided into training, validation, and testing subsets. The experimental results demonstrate that the proposed MSHLB-DETR achieved the best detection performance on the test set, with an ${\mathrm{mAP}}_{50}$ of 96.0%, surpassing other state-of-the-art models of similar scale. Compared to the original RT-DETR, the proposed model increased ${\mathrm{mAP}}_{50}$ by 15.8%, reduced Params by 7.5%, and decreased GFLOPs by 5.2%. This study reveals the critical importance of developing efficient multi-scale detection techniques for the accurate identification of citrus Huanglongbing in complex real-time monitoring scenarios. The proposed algorithm is expected to provide valuable references and new insights for the precise and timely detection of citrus Huanglongbing. Full article

Canopy chlorophyll content (CCC) is an important index for monitoring the growth and estimating the productivity of citrus fruit trees. This study optimized the PROSAIL model by adjusting the average leaf angle (ALA) parameter. A hybrid inversion model was then developed by combining the simulated data with UAV multispectral measurements using machine learning to determine the optimal data fusion ratio for improved citrus CCC prediction. The results show that (1) the most pragmatic accommodation for the hybrid inversion model in this study is the 1:4 ratio of measured data to simulated data; (2) the adjusted ALA (ALA_adj) value of citrus fruit trees is 42°, and the spectral response region of the adjusted PROSAIL parameters is more conducive to leaf chlorophyll content (LCC) and the leaf area index (LAI) for CCC modeling; and (3) the ALA_adj hybrid inversion model showed significantly better performance than the ALA-unadjusted model under all four machine learning methods, with the peak prediction accuracy, measured by R², rising from 0.723 to 0.823—a 13.8% increase. The proposed method effectively improves the prediction accuracy of citrus CCCs, demonstrating the strong potential of the ALA_adj-based PROSAIL model for UAV-scale CCC monitoring. Full article

Understory vegetation, particularly dwarf bamboo, plays a crucial role in regulating forest nutrient cycles by intercepting litter and altering decomposition processes, yet its overall impacts remain understudied and insufficiently quantified. This study employs a combination of field surveys and decomposition bag experiments to investigate how understory dwarf bamboo (Fargesia decurvata) alters the spatial–temporal patterns of leaf litter production and decomposition. We found that the dwarf bamboo intercepted more than 25% of canopy litterfall, altering its spatial distribution and reducing decomposition efficiency in the bamboo crown (BC). Leaf trait-decomposition relationships differed strongly across habitats, being positive for saturated fresh weight (SFW), leaf thickness (LFT), and leaf area (LA) and dry weight (DW) in bamboo habitats but weaker in the bamboo-free habitat (NB). Potassium release was significantly higher in the BC treatment, whereas carbon release showed the opposite trend. In contrast, nitrogen and phosphorus exhibited net enrichment across all treatments, with phosphorus enrichment being slower in BC than in bamboo-covered ground surface (BG) and NB. Our results demonstrate that the understory dwarf bamboo reshapes the spatial distribution of litter and nutrient release dynamics during decomposition, resulting in element-specific nutrient release patterns. These findings provide mechanistic insights into how understory dwarf bamboo mediates nutrient cycling dynamics in forest communities. Full article

The traditional continuous, quantitative spraying technology ignores the severity of pests, diseases and grasses, spatial distribution and other differences, resulting in low effective utilization of pesticides, environmental pollution and other problems. Variable-rate spray technology has become an important development direction in the field of precision agriculture by dynamically sensing crop canopy morphology, pest and disease distribution, and environmental parameters, adjusting the application amount in real time, and significantly improving pesticide utilization. In this study, we systematically review the core progress of variable-rate spray technology; focus on the technical system of information detection, spray volume model, and control system; analyze the current bottlenecks; and propose an optimization path to adapt to the complex agricultural conditions. At the level of information perception, LiDAR, machine vision, and multi-source sensor fusion technology constitute the main perception architecture, and infrared and ultrasonic sensors assist target recognition in complex scenes. In the construction of the spray volume model, models based on canopy volume, leaf area density, etc., are used to realize dynamic application decision by fusing equipment operating parameters, pest and disease levels, meteorological conditions, and so on. The control system takes the solenoid valve + PID control as the core program, and improves the response speed through PWM regulation and closed-loop feedback. The current technical bottlenecks are mainly concentrated in the sensor dynamic detection accuracy, model environmental adaptability, and the reliability of the execution parts. In the future, it is necessary to further promote anti-jamming multi-source heterogeneous sensor data fusion, multi-factor adaptive spray model development, lightweight edge computing deployment, and solenoid valve structural parameter optimization and other technical research, with a view to promoting the application of variable-rate spray technology to the field on a large scale and providing a theoretical reference and technological support for the green transformation of agriculture. Full article

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

Imaging in controlled agriculture helps maximize plant growth by saving labor and optimizing resources. By monitoring specific plant traits, growers can prevent crop losses by correcting environmental conditions that lead to physiological disorders like leaf tipburn. This study aimed to identify morphometric and spectral markers for the early detection of tipburn in two Romaine lettuce (Lactuca sativa) cultivars (‘Chicarita’ and ‘Dragoon’) using an image-based system with color and multispectral cameras. By monitoring tipburn in treatments using melatonin, lettuce cultivars, and with and without supplemental lighting, we enhanced our system’s accuracy for high-resolution tipburn symptom identification. Canopy geometrical features varied between cultivars, with the more susceptible cultivar exhibiting higher compactness and extent values across time, regardless of lighting conditions. These traits were further used to compare simple linear, logistic, least absolute shrinkage and selection operator (LASSO) regression, and random forest models for predicting leaf fresh and dry weight. Random forest regression outperformed simpler models, reducing the percentage error for leaf fresh weight from ~34% (LASSO) to ~13% (RMSE: 34.14 g to 17.32 g). For leaf dry weight, the percentage error decreased from ~20% to ~12%, with an explained variance increase to 94%. Vegetation indices exhibited cultivar-specific responses to supplemental lighting. ‘Dragoon’ consistently had higher red-edge chlorophyll index (CIrededge), enhanced vegetation index, and normalized difference vegetation index values than ‘Chicarita’. Additionally, ‘Dragoon’ showed a distinct temporal trend in the photochemical reflectance index, which increased under supplemental lighting. This study highlights the potential of morphometric and spectral traits for early detection of tipburn susceptibility, optimizing cultivar-specific environmental management, and improving the accuracy of predictive modeling strategies. Full article

Climate change poses a significant threat to crop production, particularly in tropical and semi-arid regions. Sorghum (Sorghum bicolor (L.) Moench), a resilient C₄ cereal, has high photosynthetic efficiency and abiotic stress tolerance, making it a key crop for food, fodder, and feed security. This study evaluated agronomic and physiological traits influencing the yield performance of 20 sorghum varieties under field conditions in Kerala, India. The data were analyzed using a randomized block design (RBD) in GRAPES software, and a principal component analysis was performed in R. Variety CSV 17 exhibited the highest grain yield (GY) (3760 kg ha⁻¹) and harvest index (HI) (43), with early flowering, early maturity, a high chlorophyll content (CHL), and minimal nitrogen (N), phosphorus (P), and potassium uptake. Conversely, CSV 20 produced the highest stover yield (22.5 t ha⁻¹), associated with greater leaf thickness (LT), lower canopy temperature, taller plant height (PH), increased leaf number (LN), and extended maturity. Leaf temperature (T_leaf) was negatively correlated with the quantum yield of photosystem II (ΦPSII) and panicle length (PL), which were strong predictors of grain weight. The principal component analysis revealed that PC1 and PC2 explained 21% and 19% of the variation in the grain and stover yield, respectively. Hierarchical partitioning identified the potassium content (K%), CHL, T_leaf, leaf area index (LAI), ΦPSII, and LT as key contributors to the GY, while the SY was primarily influenced by the LN, nitrogen content (N%), maturity duration, PH, and ΦPSII. These findings highlight the potential of exploiting physiological traits for enhancing sorghum productivity under summer conditions in Kerala and similar environments. Full article

This study investigates the spatial heterogeneity and species diversity of grassland vegetation in the agro-pastoral ecotone of China’s Loess Plateau, integrating Taylor’s power law model with the minimum area concept to address scale-dependent ecological patterns. Field surveys were conducted across four vegetation types: small-leaf poplar forest (SP), pine–caragana mixed forest (PC), caragana shrubland (RC), and saline grassland (SG). Nested quadrats (0.25–8 m²) were used to establish species–area relationships (SARs), while binary occurrence frequency data fitted to Taylor’s power law quantified spatial heterogeneity parameters (δ_i, δ_c, CACD) and derived diversity indices (H′, J′, D). the results showed that species composition differed significantly among vegetation types, with RC exhibiting the highest richness (25 species) and SG the lowest (12 species). SAR analysis showed distinct z-values: SP had the lowest z (0.14), indicating minimal area effects and high homogeneity, while SG had the highest area sensitivity. Spatial heterogeneity (δ_c) was highest in RC and lowest in SP. Over 82.5% of herb-layer species exhibited aggregated distributions (δ_i > 0). The dominant species Leymus secalinus (Georgi) Tzvelev shifted from regular (δ_i < 0) under SP/SG to aggregated (δ_i > 0) under PC/RC. Diversity metrics peaked in PC plots (highest H′ and richness, lowest dominance), whereas SP showed high dominance but low diversity. CACD values (critical aggregation diversity) were maximized under SG. The integration of power law modeling and minimum area analysis effectively captures scale-dependent vegetation patterns. Pine–caragana mixed forests (PC) optimize biodiversity and spatial heterogeneity, suggesting moderated canopy structures enhance ecological stability. These findings provide a theoretical basis for sustainable grassland management in ecologically sensitive agro-pastoral zones. Full article

Water deficit during reproductive development is one of the main constraints on pea (Pisum sativum L.) productivity in tropical highlands. In this study, five varieties with contrasting leaf architectures were evaluated under controlled greenhouse conditions, with and without water deficit applied from the time of pod formation. Key ecophysiological variables, including leaf area index (LAI), radiation extinction coefficient (k), interception efficiency (RIE), radiation use efficiency (RUE), and water use efficiency (WUE), along with yield components, were measured. Deficit significantly reduced biomass, RUE, and yield, although the harvest index (HI) remained relatively stable. Varieties with the afila gene showed greater stability in LAI and WUE, but lower biomass accumulation. Correlation analyses revealed that, under optimal conditions, yield was closely associated with structural and functional traits, a relationship that weakened under stress. These results demonstrate the importance of integrating morphophysiological characteristics into breeding and agronomic management programs to develop more efficient and resilient varieties under water deficit conditions in the high tropics. Full article