Search Results (75)

Mangoes are an important part of Hainan’s tropical characteristic agriculture. In response to the requirements of building an ecological civilization pilot demonstration zone in Hainan, China, green and sustainable development will be the future development trend of the mango planting system. However, the economic benefits and environmental impact during its planting and management process remain unclear. This paper combines emergy, life cycle assessment (LCA), and economic analysis to compare the system sustainability, environmental impact, and economic benefits of the traditional mango cultivation system (TM) in Dongfang City, Hainan Province, and the early-maturing mango cultivation system (EM) in Sanya City. The emergy evaluation results show that the total emergy input of EM (1.37 × 10¹⁶ sej ha⁻¹) was higher than that of TM (1.32 × 10¹⁶ sej ha⁻¹). From the perspective of the emergy index, compared with TM, EM exerted less pressure on the local environment and has better stability and sustainability. This was due to the higher input of renewable resources in EM. The LCA results showed that based on mass as the functional unit, the potential environmental impact of the EM is relatively high, and its total environmental impact index was 18.67–33.19% higher than that of the TM. Fertilizer input and On-Farm emissions were the main factors causing environmental consequences. Choosing alternative fertilizers that have a smaller impact on the environment may effectively reduce the environmental impact of the system. The economic analysis results showed that due to the higher selling price of early-maturing mango, the total profit and cost–benefit ratio of the EM have increased by 55.84% and 36.87%, respectively, compared with the TM. These results indicated that EM in Sanya City can enhance environmental sustainability and boost producers’ annual income, but attention should be paid to the negative environmental impact of excessive fertilizer input. These findings offer insights into optimizing agricultural inputs for Hainan mango production to mitigate multiple environmental impacts while enhancing economic benefits, aiming to provide theoretical support for promoting the sustainable development of the Hainan mango industry. Full article

The accurate estimation of evapotranspiration ($ET$) and its components are vital for water resource management and irrigation planning. This study models tree transpiration ($T$) and $ET$ for grapefruit, litchi, and mango orchards using light gradient-boosting machine (LightGBM) optimized using the Bayesian hyperparameter optimization. Grounds $T$ and $ET$ for these crops were measured using the heat ratio method of monitoring sap flow and the eddy covariance technique for quantifying $ET$. The Sentinel 2 satellite was used to compute field leaf area index ($LAI$). The modelled data were used to partition the orchard $ET$ into beneficial ($T$) and non-beneficial water uses (orchard floor evaporation—$E_s$). We adopted the 10-fold cross-validation to test the model robustness and an independent validation to test performance on unseen data. The 10-fold cross-validation and independent validation on $ET$ and $T$ models produced high accuracy with coefficient of determination ($R^2$) $\ge$ 0.88, Kling–Gupta efficiency ($KGE$) $\ge$ 0.91, root mean square error ($RMSE$) $\le$ 0.04 mm/h, and mean absolute error ($MAE$) $\le$ 0.03 mm/h for all the crops. The study demonstrates that LightGBM can accurately model the transpiration and evapotranspiration for subtropical tree crops using Sentinel 2 data. The study found that $E_s$ which combined soil evaporation and understorey vegetation transpiration contributed 35, 32, and 31% to the grapefruit, litchi and mango orchard evapotranspiration, respectively. We conclude that improvements on orchard floor management practices can be utilized to minimize non-beneficial water losses while promoting the productive water use ($T$). Full article

A method based on Vis-NIR spectroscopy and machine learning-based modeling for non-destructive detection of the internal disorders of black flesh, spongy tissue, jelly seed, and soft nose in mango fruit was developed using the vis-NIR spectra of intact mango fruit of three cultivars sourced from three orchards in each of the two seasons, with spectra collected both at harvest and after storage. After spectra were acquired of the stored fruit, the fruit cheeks were cut longitudinally to allow visual assessment of the incidence of the internal disorders. Five models were evaluated: two tree-based algorithms (J48 and random forest), one neural network (multilayer perceptron, MLP), and two SVM training algorithms (sequential minimal optimization, SMO, and LibSVM). The models were evaluated using a tenfold cross-validation approach. Non-destructive discrimination of health from all disordered and healthy fruit from fruit with specific disorders was achieved with an accuracy ranging from 72.3 to 97.0% when using spectra collected at harvest and 63.7 to 96.2% when using spectra collected after ripening. No one machine learning algorithm out-performed other methods—for spectra collected at harvest, the highest discrimination accuracy was achieved with RF and MLP for black flesh, J48 for spongy tissue, and LibSVM for soft nose and jelly seed. For spectra collected of stored fruit, the highest discrimination accuracy was achieved with SMO for jelly seed and RF for soft nose. A recommendation is made for the consideration of ensemble models in future. The ability to predict the development of the disorder using spectra of at-harvest fruit offers the potential to guide postharvest practices and reduce incidence of internal disorders in mangoes. Full article

Botryosphaeriaceae species were recently found to be responsible for heavy mango crop losses worldwide. In 2020, mango fruit samples showing fruit decay symptoms were collected from Glenn, Kent, Irwin, Palmer, Brokaw 2, and Gomera 3 accessions in 4 orchards located in Sicily (Italy). A molecular analysis of the ITS and tub2 regions performed on 41 representative isolates allowed for the identification of mainly Neofusicoccum parvum and occasionally Botryosphaeria dothidea (1/41) as the causal agents of fruit decay. Pathogenicity proofs were satisfied for both fungal pathogens. Ripe and unripe Gomera 3 mango fruits were used to compare the virulence among the N. parvum isolates. Postharvest experiments performed on Gomera 3 fruits and by using different biocontrol agents (BCAs) showed that the performance of treatments in reducing fruit decay depends on N. parvum virulence. The data show that unregistered Wickerhamomyces anomalus WA-2 and Pichia kluyveri PK-3, followed by the trade bioformulate Serenade™ (Bacillus amyloliquefaciens QST713), were the most effective in managing mango fruit rot. This paper shows, for the first time, the potential of different BCAs, including Trichoderma spp., for the controlling of postharvest decay caused by N. parvum on mango fruits. Full article

Litter and pruning biomass are integral to nutrient cycling in the plant–soil ecosystem, contributing significantly to organic matter formation and humus development through decomposition and nutrient mineralization, which ultimately influence soil fertility and health. However, the litterfall dynamics in mango orchards are not well understood, and its contribution to nutrient cycling has seldom been measured. This study aimed to estimate litterfall and pruning biomass in mango orchards and assess the nutrient contents of various biomass components. Litter and pruning biomass samples were collected from four commercial mango orchards planted with Kensington Pride (‘KP’) and ‘B74’ (‘Calypso^®’) cultivars in the Darwin and Katherine regions, using litter traps placed on the orchard floors. Samples were sorted (leaves, flowers, panicles, fruits, and branches) and analyzed for nutrient contents. Results showed that most biomass abscissions occurred between late June and August, spanning approximately 100 days involving floral induction phase, fruit set, and maturity. Leaves made up most of the abscised litter biomass, while branches were the primary component of pruning biomass. The overall ranking of biomass across both regions and orchards is as follows: leaves > branches > panicles > flowers > fruits. The carbon–nitrogen (C:N) ratio of litter pruning material ranged from 30 (flowers) to 139 (branches). On a hectare basis, litter and biomass inputs contained 1.2 t carbon (C), 21.2 kg nitrogen (N), 0.80 kg phosphorus (P), 4.9 kg potassium (K), 8.7 kg calcium (Ca), 2.0 kg magnesium (Mg), 1.1 kg sulfur (S), 15 g boron (B), 13.6 g copper (Cu), 99.3 g iron (Fe), 78.6 g manganese (Mn), and 28.6 g zinc (Zn). The results indicate that annual litterfall may contribute substantially to plant nutrient supply and soil health when incorporated into the soil to undergo decomposition. This study contributes to a better understanding of litter biomass, nutrient sources, and nutrient cycling in tropical mango production systems, offering insights that support accurate nutrient budgeting and help prevent over-fertilization. However, further research is needed to examine biomass accumulation under different pruning regimes, decomposition dynamics, microbial interactions, and broader ecological effects to understand litterfall’s role in promoting plant growth, enhancing soil health, and supporting sustainable mango production. 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

The development of unmanned aerial spraying systems (UASSs) has significantly transformed pest and disease control methods of crop plants. Precisely adjusting pesticide application rates based on the target conditions is an effective method to improve pesticide use efficiency. In orchard spraying, the structural characteristics of the canopy are crucial for guiding the pesticide application system to adjust spraying parameters. This study selected mango trees as the research sample and evaluated the differences between UAV aerial photography with a Structure from Motion (SfM) algorithm and airborne LiDAR in the results of extracting canopy parameters. The maximum canopy height, canopy projection area, and canopy volume parameters were extracted from the canopy height model of SfM (${CHM}_{SfM}$) and the canopy height model of LiDAR (${CHM}_{LiDAR}$) by grids with the same width as the planting rows (5.0 m) and 14 different heights (0.2 m, 0.3 m, 0.4 m, 0.5 m, 0.6 m, 0.8 m, 1.0 m, 2.0 m, 3.0 m, 4.0 m, 5.0 m, 6.0 m, 8.0 m, and 10.0 m), respectively. Linear regression equations were used to fit the canopy parameters obtained from different sensors. The correlation was evaluated using $R^2$ and $rRMSE$, and a t-test (α = 0.05) was employed to assess the significance of the differences. The results show that as the grid height increases, the $R^2$ values for the maximum canopy height, projection area, and canopy volume extracted from ${CHM}_{SfM}$ and ${CHM}_{LiDAR}$ increase, while the $rRMSE$ values decrease. When the grid height is 10.0 m, the $R^2$ for the maximum canopy height extracted from the two models is $92.85\%$, with an $rRMSE$ of $0.0563$. For the canopy projection area, the $R^2$ is 97.83%, with an $rRMSE$ of 0.01, and for the canopy volume, the $R^2$ is 98.35%, with an $rRMSE$ of 0.0337. When the grid height exceeds 1.0 m, the t-test results for the three parameters are all greater than 0.05, accepting the hypothesis that there is no significant difference in the canopy parameters obtained by the two sensors. Additionally, using the coordinates $x_0$ of the intersection of the linear regression equation and $y=x$ as a reference, ${CHM}_{SfM}$ tends to overestimate lower canopy maximum height and projection area, and underestimate higher canopy maximum height and projection area compared to ${CHM}_{LiDAR}$. This to some extent reflects that the surface of ${CHM}_{SfM}$ is smoother. This study demonstrates the effectiveness of extracting canopy parameters to guide UASS systems for variable-rate spraying based on UAV oblique photography combined with the SfM algorithm. Full article

Bactrocera dorsalis (Hendel) is an invasive fruit and vegetable pest, infesting citrus, mango, carambola, etc. We observed that the posterior thoracic scutella of some B. dorsalis adults are yellow, some light yellow, and some white in China. Compared with the B. dorsalis races with a yellow scutellum (YS) and white scutellum (WS), the race with a light-yellow scutellum (LYS) is dominant in citrus and carambola orchards. To reveal genetic correlates among the three races, the genomes of 22 samples (8 with YS, 7 with LYS, and 7 with WS) were sequenced by high-throughput sequencing technology. Single-nucleotide polymorphism (SNP) annotation showed that there were 17,580 non-synonymous mutation sites located in the exonic region. Principal component analysis based on independent SNP data revealed that the SNPs with LYS were more similar to that with YS when compared with WS. Most genes associated with scutellum color variation were involved in three pathways: oxidative phosphorylation, porphyrin and chlorophyll metabolism, and terpenoid backbone biosynthesis. By comparing the sequences among the three races, we screened out 276 differential genes (DGs) in YS vs. WS, 185 DGs in LYS vs. WS, and 104 DGs in YS vs. LYS. Most genes determining color variation in B. dorsalis scutella were located on chromosomes 2–5. Biochemical analysis showed that β-carotene content in YS and LYS was significantly higher than that in WS at any stage of adult days 1, 10, and 20. No significant differences were observed in cytochrome P450 or melanin content in YS, LYS, or WS. Our study provides results on aspects of scutellum color variation in B. dorsalis adults, providing molecular and physiological information for revealing the adaptation and evolution of the B. dorsalis population. Full article

Nitrogen (N) is vital for mango yield and fruit quality, but finding the optimal amount is crucial to avoid the ‘stay green’ problem, which diminishes both fruit quality and profitability. This study aimed to assess the impact of N levels on the fruit quality and yield of ‘Kensington Pride’ (‘KP’) mangoes and determine the amount of N that triggers the ‘stay green’ effect in fruit. A field trial was conducted in a commercial orchard with N treatments (0, 12.5, 25, and 50 kg ha⁻¹) and four replications during the 2018 and 2019 cropping seasons. Fruit yield was quantified, and post-harvest quality (skin color during ripening, sugar content [°Brix], and texture) as well as ethylene effects were assessed. Fruit yields did not vary among N levels over the two cropping seasons but were significantly lower in 2018 (20.0 t ha⁻¹) compared to 2019 (38.5 t ha⁻¹), illustrating the alternate year-bearing habit of ‘KP’ mangoes. In the 2018 harvest, fruit from trees receiving 25 kg N ha⁻¹ appeared yellow–green compared to those with less N, while fruit from trees with 50 kg N ha⁻¹ exhibited ‘stay green’ skin, indicating that applications of 25 and 50 kg N ha⁻¹ were excessive. There was no ‘stay green’ skin observed in the 2019 harvest, indicating that the environment may also be a contributing factor. The texture of ripe fruit from untreated control trees had the highest flesh resistance. Moreover, ethylene-treated fruit ripened in nine days post-harvest and had significantly lower sugar content than untreated fruit, which ripened in 14 days. This study provides valuable insights into the complex interactions among N application, fruit quality, and yield of ‘KP’ mangoes, highlighting the importance of appropriate N management for a sustainable and environmentally friendly commercial mango production system. Full article

Tree- and block-level prediction of mango yield is important for farm operations, but current manual methods are inefficient. Previous research has identified the accuracies of mango yield forecasting using very-high-resolution (VHR) satellite imagery and an ’18-tree’ stratified sampling method. However, this approach still requires infield sampling to calibrate canopy reflectance and the derived block-level algorithms are unable to translate to other orchards due to the influences of abiotic and biotic conditions. To better appreciate these influences, individual tree yields and corresponding canopy reflectance properties were collected from 2015 to 2021 for 1958 individual mango trees from 55 orchard blocks across 14 farms located in three mango growing regions of Australia. A linear regression analysis of the block-level data revealed the non-existence of a universal relationship between the 24 vegetation indices (VIs) derived from VHR satellite data and fruit count per tree, an outcome likely due to the influence of location, season, management and cultivar. The tree-level fruit count predicted using a random forest (RF) model trained on all calibration data produced a percentage root mean squared error (PRMSE) of 26.5% and a mean absolute error (MAE) of 48 fruits/tree. The lowest PRMSEs produced from RF-based models developed from location, season and cultivar subsets at the individual tree level ranged from 19.3% to 32.6%. At the block level, the PRMSE for the combined model was 10.1% and the lowest values for the location, seasonal and cultivar subset models varied between 7.2% and 10.0% upon validation. Generally, the block-level predictions outperformed the individual tree-level models. Maps were produced to provide mango growers with a visual representation of yield variability across orchards. This enables better identification and management of the influence of abiotic and biotic constraints on production. Future research could investigate the causes of spatial yield variability in mango orchards. Full article

Mango stem-end rot caused by Lasiodiplodia theobromae is a major postharvest disease in China. Prochloraz is commonly used for disease control in mango orchards and in storage. However, prochloraz resistance has been detected in L. theobromae. This study aimed to explore the underlying mechanisms responsible for prochloraz resistance in L. theobromae. The results show that no point mutation in the target gene LtCYP51 of the prochloraz-resistant L. theobromae strain was detected, but the expression was upregulated significantly. Additionally, the full-length sequences of the cytochrome P450 gene CYP55A3 were successfully amplified and identified from L. theobromae, and the qRT-PCR results confirm that CYP55A3 was significantly upregulated after treatment with prochloraz. The knockout mutant of the CYP55A3 presented significantly lower gene expression levels than the wild-type strain HL02, with a 16.67-fold reduction, but a 1.34-fold reduction in P450 activities and a 1.72-fold increase in the accumulation of prochloraz in the mycelia. Treatment with the P450 enzyme inhibitor significantly synergized with the prochloraz toxicity. The wild-type strain was highly resistant to pyraclostrobin and carbendazim; similarly, the sensitivity of the knockout mutant to pyraclostrobin and carbendazim also notably increased. There was no significant difference between the wild-type strain and the gene-complemented strain. The homology model and molecular docking analysis provide evidence that prochloraz interacts with the protein structure of CYP55A3. These findings suggest that the overexpression of the target gene LtCYP51 and the detoxification gene CYP55A3 were involved in the molecular mechanisms of resistance to prochloraz by L. theobromae. Full article

The male annihilation technique (MAT) plays a crucial role in the pest management program of the oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae). However, a suitable method for real-time and accurate assessment of MAT’s control efficiency has not been established. Laboratory investigations found that motile sperms can be observed clearly under the microscope when the spermathecae dissected from mated females were torn, and no sperms were found in the spermathecae of virgin females. Furthermore, it was confirmed that sperms can be preserved in the spermathecae for more than 50 days once females have mated. Laboratory results also indicated that proportion of mated females decreased from 100% to 2% when the sex ratio (♀:♂) was increased from 1:1 to 100:1. Further observation revealed that there were no significant differences in the superficial area of the ovary or spermatheca between mated females and virgin females. Field investigations revealed that the proportion of mated females (PMF) could reach 81.2% in abandoned mango orchards, whereas the PMF was less than 36.4% in mango orchards where MAT was applied. This indicates that the PMF of the field population can be determined by examining the presence of sperms in the spermathecae. Therefore, we suggest that this method can be used to monitor the control efficiency when MAT is used in the field. Full article

Increasing the planting density of mango orchards appears promising for obtaining higher yields, particularly during the first productive years. However, the challenge is to maintain a good balance between vegetative growth and fruit production in the longer term. The objective of this study was to decipher the effects of planting density, training system and cultivar on young mango trees’ growth and production. The experiment, conducted in North Queensland, consisted of five combinations of planting density and training system applied to the cultivars Keitt, Calypso and NMBP-1243. The planting densities were low (208 tree ha⁻¹), medium (416 tree ha⁻¹) and high (1250 tree ha⁻¹). The closed vase conventional training system was applied at each density. Single leader and espalier on trellis training systems were applied at medium and high densities, respectively. The tree canopy dimensions were measured every 6 months from planting, and tree production was recorded from the third to the fifth years after planting. Vegetative growth and fruit production were the results of complex interactions between planting density, training system, cultivar and/or time. The expected increase in orchard yield with higher planting density was observed from the first productive year, despite lower individual tree production at high planting density. Lower vegetative growth and fruit production at high planting density were probably caused by competition between trees. NMBP-1243 and Keitt showed more rapid vegetative growth. Keitt was the most productive cultivar during the first three productive years. The detailed results of this study provide avenues to further explore the behaviour of mango trees at high planting densities. Full article

Precision agriculture-based orchard management relies heavily on the accurate delineation of tree canopies, especially for high-value crops like mangoes. Traditional GIS and remote sensing methods, such as Object-Based Imagery Analysis (OBIA), often face challenges due to overlapping canopies, complex tree structures, and varied light conditions. This study aims to enhance the accuracy of mango orchard mapping by developing a novel deep-learning approach that combines fine-tuned object detection and segmentation techniques. UAV imagery was collected over a 65-acre mango orchard in Multan, Pakistan, and processed into an RGB orthomosaic with a 3 cm ground sampling distance. The You Only Look Once (YOLOv7) framework was trained on an annotated dataset to detect individual mango trees. The resultant bounding boxes were used as prompts for the segment anything model (SAM) for precise delineation of canopy boundaries. Validation against ground truth data of 175 manually digitized trees showed a strong correlation ($R^2$ = 0.97), indicating high accuracy and minimal bias. The proposed method achieved a mean absolute percentage error (MAPE) of 4.94% and root mean square error (RMSE) of 80.23 sq ft against manually digitized tree canopies with an average size of 1290.14 sq ft. The proposed approach effectively addresses common issues such as inaccurate bounding boxes and over- or under-segmentation of tree canopies. The enhanced accuracy can substantially assist in various downstream tasks such as tree location mapping, canopy volume estimation, health monitoring, and crop yield estimation. Full article

Recent developments in affordable depth imaging hardware and the use of 2D Convolutional Neural Networks (CNN) in object detection and segmentation have accelerated the adoption of machine vision in a range of applications, with mainstream models often out-performing previous application-specific architectures. The need for the release of training and test datasets with any work reporting model development is emphasized to enable the re-evaluation of published work. An additional reporting need is the documentation of the performance of the re-training of a given model, quantifying the impact of stochastic processes in training. Three mango orchard applications were considered: the (i) fruit count, (ii) fruit size and (iii) branch avoidance in automated harvesting. All training and test datasets used in this work are available publicly. The mAP ‘coefficient of variation’ (Standard Deviation, SD, divided by mean of predictions using models of repeated trainings × 100) was approximately 0.2% for the fruit detection model and 1 and 2% for the fruit and branch segmentation models, respectively. A YOLOv8m model achieved a mAP50 of 99.3%, outperforming the previous benchmark, the purpose-designed ‘MangoYOLO’, for the application of the real-time detection of mango fruit on images of tree canopies using an edge computing device as a viable use case. YOLOv8 and v9 models outperformed the benchmark MaskR-CNN model in terms of their accuracy and inference time, achieving up to a 98.8% mAP50 on fruit predictions and 66.2% on branches in a leafy canopy. For fruit sizing, the accuracy of YOLOv8m-seg was like that achieved using Mask R-CNN, but the inference time was much shorter, again an enabler for the field adoption of this technology. A branch avoidance algorithm was proposed, where the implementation of this algorithm in real-time on an edge computing device was enabled by the short inference time of a YOLOv8-seg model for branches and fruit. This capability contributes to the development of automated fruit harvesting. Full article