Search Results (16)

Mango productivity suffers greatly from leaf diseases, leading to economic and food security issues. Current visual inspection methods are slow and subjective. Previous Deep-Learning (DL) solutions have shown promise but suffer from imbalanced datasets, modest generalization, and limited interpretability. To address these challenges, this study introduces the ViX-MangoEFormer, which combines convolutional kernels and self-attention to effectively diagnose multiple mango leaf conditions in both balanced and imbalanced image sets. To benchmark against ViX-MangoEFormer, we developed a stacking ensemble model (MangoNet-Stack) that utilizes five transfer learning networks as base learners. All models were trained with Grad-CAM produced pixel-level explanations. In a combined dataset of 25,530 images, ViX-MangoEFormer achieved an F1 score of 99.78% and a Matthews Correlation Coefficient (MCC) of 99.34%. This performance consistently outperformed individual pre-trained models and MangoNet-Stack. Additionally, data augmentation has improved the performance of every architecture compared to its non-augmented version. Cross-domain tests on morphologically similar crop leaves confirmed strong generalization. Our findings validate the effectiveness of transformer attention and XAI in mango leaf disease detection. ViX-MangoEFormer is deployed as a web application that delivers real-time predictions, probability scores, and visual rationales. The system enables growers to respond quickly and enhances large-scale smart crop health monitoring. Full article

Commercial mango growers commonly spray potassium nitrate (KNO₃) solution to enhance flowering and fruit quality, yet there is limited information on the uptake efficiency of nitrogen (N) by mango cultivars through leaf cuticles. The study aimed to assess N uptake efficiency (NUpE) from foliar application of KNO₃ solution and compare NUpE among mango varieties. Mango cultivars were ‘Kensington Pride’ (‘KP’), ‘B74’ (‘Calypso^®’), and ‘NMBP 1201’ (‘AhHa!^®’), ‘NMBP 1243’ (‘Yess!^®’), and ‘NMBP 4069’ (‘Now^®’) grafted onto ‘KP’ seedlings. Leaves of six-month-old seedlings were dipped in ¹⁵N-enriched KNO₃ solution and analyzed for total N and ¹⁵N contents. A significant correlation was observed between the leaf area and the amount of solution retained after dipping the leaves in the KNO₃ solution. Moreover, leaves treated with the KNO₃ solution had higher ¹⁵N levels than the natural ¹⁵N abundance, indicating successful N uptake from the KNO₃ solution. The NUpE ranged from 27% to 44% and varied with variety. Cultivar ‘NMBP 4069’ had the highest NUE (44%) which was comparable with that of ‘B74’ (40%). ‘NMBP 1201’ showed the lowest (27%) NUpE which was comparable with that of ‘NMBP 1243’ (30%) and ‘KP’ (33%). These data on ¹⁵N uptake through the mango leaf cuticle demonstrates the effectiveness of foliar application as a method of supplying N to mango trees, highlighting important varietal differences in foliar ¹⁵N uptake efficiency. Considering these differences in NUpE among mango varieties will help in making informed decisions about cultivar selection and N management strategies for sustainable mango production. 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

As young workers prefer urban labors and migrate to USA and Canada, mango harvesting is becoming scarce on Mexican coasts. This seasonal labor is becoming expensive and when many orchards produce fruit simultaneously, grower losses increase. In this research, an innovative fruit detachment method was tested after applying a viscous paste to the pedicel of mango fruits hanging in the tree. Activated carbon or charcoal (AC), was mixed with different amounts of nitric acid to provide three AC composite blends named: light, medium, and dense. The nanomaterial was applied with a brush to the fruit pedicel/peduncle taking up to 4 h before the mango fruits felt to a net below the tree canopy. Mango detachment experiments indicated that the medium blend was the most efficient in releasing the fruit, taking an average of 2 h. The dense nano-material decreased latex exudation to 7% of the fruits. Fruit maturity emerged as a crucial factor for detachment time, followed by mango weight. Full article

Pollination is essential for the reproductive output of crops. Anthropogenic disturbance and global pollinator decline limit pollination success, reducing the quantity or quality of pollen. Relationships between the abundance of flower visitors and fruit production are often poorly understood. We aimed to (1) identify and quantify flower visitors in a mango orchard; (2) assess how much of the crop resulted from self- versus cross-pollination at increasing distances from a cross-pollen source in large, single-cultivar blocks of the cultivar Kensington Pride or the cultivar Calypso; and (3) determine how pollen parentage affected the size, colour, flavour attributes, and nutritional quality of fruit. Mango flowers were mostly visited by rhiniid flies and coccinellid beetles. Approximately 30% of the fruit were the result of cross-pollination, with the percentage significantly decreasing with an increasing distance from a cross-pollen source in the cultivar Calypso. Self-pollinated Calypso fruit were slightly larger and heavier, with higher acid and total polyphenol concentrations than cross-pollinated fruit. Our results showed higher-than-expected levels of cross-fertilisation among fruit, although self-pollen was likely more abundant than cross-pollen in the large orchard blocks. Our results suggest the preferential cross-fertilisation of flowers or the preferential retention of cross-fertilised fruitlets, both representing strategies for circumventing inbreeding depression. Growers should establish vegetated habitats to support pollinator populations and interplant cultivars more closely to maximise cross-pollen transfer. Full article

Mango, banana, papaya, pineapple, and avocado are categorized as major tropical fruits grown for local consumption, export, and sources of income to the growers. These fruit crops are susceptible to infection by Fusarium in the field, and after harvest, it causes root rot, vascular wilt, stem rot, and fruit rot. Among the most common and economically important Fusarium species associated with diseases of major fruit are F. oxysporum and F. solani, which are prevalent in tropical regions. Other species include F. incarnatum, F. proliferatum, and F. verticilliodes. Most of these species have a wide host range and infect different parts of the plant. Due to the economic importance of these fruit crops, this review highlights the diseases and Fusarium species that infect fruit crops in the field as well as after harvest. Updated information on Fusarium species infecting major tropical fruit crops is important as disease management in the field and after harvest often relies on the causal pathogens. Moreover, major fruit crops are traded worldwide, and newly recorded species associated with these fruit crops are important for biosecurity purposes. Information on the diseases and causal pathogens may help to facilitate routine diagnosis and planning of suitable plant disease management methods. Full article

Mango is an extremely perishable fruit with a short postharvest time, and a considerable proportion of harvested mangoes become spoiled due to the postharvest decay in mango-producing areas of the world. The current study was designed to evaluate the effects of chitosan on the storage life of mango. Mango samples were coated with 750, 1000, and 1500 ppm chitosan solution, before storing them in the open or zip-bags under ambient and refrigeration conditions for different storage periods. Changes in different physical and chemical parameters were recorded to evaluate the treatments’ effectiveness in extending fruit shelf-life and sustaining postharvest quality of mangoes. The results showed that chitosan coating was able to reduce weight loss up to 65% in comparison to the uncoated control. Total mold and bacterial counts were also significantly lower in postharvest mangos when they were coated with chitosan compared to the uncoated samples. In addition, different fruit quality attributes, such as vitamin C content, titratable acidity, sugar content, ash, and protein content were also retained to a considerable extent by the chitosan coatings. Chitosan at refrigeration temperature (4 °C) with zip-bag packaging had a greater positive effect on fruit shelf-life, weight maintenance, and quality attributes than ambient temperature. Among the different coating concentrations, 1000 ppm chitosan solutions could provide better performance to extend the shelf-life of mango fruit while maintaining quality attributes. Altogether, our findings suggest that chitosan coating effectively prolongs the storage life of mango fruit and maintains fruit quality during storage, and offers promising potential for successful commercialization of this edible coating for mango growers and the industry. Full article

Mango fruit is in high demand. So, the timely control of mango plant diseases is necessary to gain high returns. Automated recognition of mango plant leaf diseases is still a challenge as manual disease detection is not a feasible choice in this computerized era due to its high cost and the non-availability of mango experts and the variations in the symptoms. Amongst all the challenges, the segmentation of diseased parts is a big issue, being the pre-requisite for correct recognition and identification. For this purpose, a novel segmentation approach is proposed in this study to segment the diseased part by considering the vein pattern of the leaf. This leaf vein-seg approach segments the vein pattern of the leaf. Afterward, features are extracted and fused using canonical correlation analysis (CCA)-based fusion. As a final identification step, a cubic support vector machine (SVM) is implemented to validate the results. The highest accuracy achieved by this proposed model is 95.5%, which proves that the proposed model is very helpful to mango plant growers for the timely recognition and identification of diseases. Full article

Mango fruit quality plays a significant role in fruit storage. It also directly affects the economic value of fruit in the national and international markets. However, deterioration of soil health due to low organic matter is a major hurdle for mango growers. Scientists suggest incorporation of organic matter. However, high temperature and low precipitation lead to oxidation of organic residues in soil. On the other hand, biochar is gaining the attention of growers due to its resistance against decomposition. It can improve soil physicochemical attributes. Limited literature is available regarding biochar effects on the quality attributes of mango. Therefore, the current study was planned to investigate the effects of acidified biochar on mango quality and yield attributes in alkaline soil. Five levels of biochar, i.e., 0, 5, 10, 20 and 40 Mg/ha, were applied in a randomized complete block design (RCBD). Results showed that 20 and 40 Mg/ha acidified biochar significantly enhanced fruit retention, sugar contents, ash contents and TSS of mango compared to control. A significant increase in mango fruit weight and yield per plant validated the efficacious role of 40 Mg/ha acidified biochar over control. Furthermore, the maximum significant decrease in fruit juice acidity signified the imperative functioning of 40 Mg/ha acidified biochar in alkaline soil. In conclusion, 40 Mg/ha acidified biochar application can improve mango quality and yield attributes in alkaline soil. More investigations on different soil types, climatic zones and mango varieties are recommended to declare 40 Mg/ha acidified biochar as the best treatment for improvement in the quality and yield of mango fruit in alkaline soils. Full article

Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) systems are useful tools for deriving horticultural tree structure estimates. However, there are limited studies to guide growers and agronomists on different applications of the two technologies for horticultural tree crops, despite the importance of measuring tree structure for pruning practices, yield forecasting, tree condition assessment, irrigation and fertilization optimization. Here, we evaluated ALS data against near coincident TLS data in avocado, macadamia and mango orchards to demonstrate and assess their accuracies and potential application for mapping crown area, fractional cover, maximum crown height, and crown volume. ALS and TLS measurements were similar for crown area, fractional cover and maximum crown height (coefficient of determination (R²) ≥ 0.94, relative root mean square error (rRMSE) ≤ 4.47%). Due to the limited ability of ALS data to measure lower branches and within crown structure, crown volume estimates from ALS and TLS data were less correlated (R² = 0.81, rRMSE = 42.66%) with the ALS data found to consistently underestimate crown volume. To illustrate the effects of different spatial resolution, capacity and coverage of ALS and TLS data, we also calculated leaf area, leaf area density and vertical leaf area profile from the TLS data, while canopy height, tree row dimensions and tree counts) at the orchard level were calculated from ALS data. Our results showed that ALS data have the ability to accurately measure horticultural crown structural parameters, which mainly rely on top of crown information, and measurements of hedgerow width, length and tree counts at the orchard scale is also achievable. While the use of TLS data to map crown structure can only cover a limited number of trees, the assessment of all crown strata is achievable, allowing measurements of crown volume, leaf area density and vertical leaf area profile to be derived for individual trees. This study provides information for growers and horticultural industries on the capacities and achievable mapping accuracies of standard ALS data for calculating crown structural attributes of horticultural tree crops. Full article

Accurate pre-harvest yield estimation of high value fruit tree crops provides a range of benefits to industry and growers. Currently, yield estimation in Avocado (Persea americana) and Mango (Mangifera indica) orchards is undertaken by a visual count of a limited number of trees. However, this method is labour intensive and can be highly inaccurate if the sampled trees are not representative of the spatial variability occurring across the orchard. This study evaluated the accuracies of high resolution WorldView (WV) 2 and 3 satellite imagery and targeted field sampling for the pre-harvest prediction of yield. A stratified sampling technique was applied in each block to measure relevant yield parameters from eighteen sample trees representing high, medium and low vigour zones (6 from each) based on classified normalised difference vegetation index (NDVI) maps. For avocado crops, principal component analysis (PCA) and non-linear regression analysis were applied to 18 derived vegetation indices (VIs) to determine the index with the strongest relationship to the measured yield parameters. For mango, an integrated approach of geometric (tree crown area) and optical (spectral vegetation indices) data using artificial neural network (ANN) model produced more accurate predictions. The results demonstrate that accurate maps of yield variability and total orchard yield can be achieved from WV imagery and targeted sampling; whilst accurate maps of fruit size and the incidence of phytophthora can also be achieved in avocado. These outcomes offer improved forecasting than currently adopted practices and therefore offer great benefit to both the avocado and mango industries. Full article

‘Nam Dok Mai’ mango is a luxury commercial fruit in Thailand, but post-harvest diseases infecting the ripe fruit is a major problem affecting marketability. The objective of the present study was to map the supply chain of ‘Nam Dok Mai’ mangoes exported to Japan and analyze the critical points of post-harvest disease infection caused mainly by Colletotrichum gloeosporioides. Risk points of the post-harvest diseases were found by examining the material and information flows from processes ranging from field production to post-harvest handling, and these were obtained from mango growers and an exporter. The findings of interviews with mango growers and observations of the mangoes in field production were that the first point of risk was cultivar selection, while branch pruning and fruit bagging were further important processes causing post-harvest fruit decay. On the other hand, it was found that post-harvest handling was significant in decreasing anthracnose disease infection; this was seen at the step of dipping the fruit in 50 °C hot water for 3 min at the processing line. Full article

In the value chain, yields are key information for both growers and other stakeholders in market supply and exports. However, orchard yields are often still based on an extrapolation of tree production which is visually assessed on a limited number of trees; a tedious and inaccurate task that gives no yield information at a finer scale than the orchard plot. In this work, we propose a method to accurately map individual tree production at the orchard scale by developing a trade-off methodology between mechanistic yield modelling and extensive fruit counting using machine vision systems. A methodological toolbox was developed and tested to estimate and map tree species, structure, and yields in mango orchards of various cropping systems (from monocultivar to plurispecific orchards) in the Niayes region, West Senegal. Tree structure parameters (height, crown area and volume), species, and mango cultivars were measured using unmanned aerial vehicle (UAV) photogrammetry and geographic, object-based image analysis. This procedure reached an average overall accuracy of 0.89 for classifying tree species and mango cultivars. Tree structure parameters combined with a fruit load index, which takes into account year and management effects, were implemented in predictive production models of three mango cultivars. Models reached satisfying accuracies with R² greater than 0.77 and RMSE% ranging from 20% to 29% when evaluated with the measured production of 60 validation trees. In 2017, this methodology was applied to 15 orchards overflown by UAV, and estimated yields were compared to those measured by the growers for six of them, showing the proper efficiency of our technology. The proposed method achieved the breakthrough of rapidly and precisely mapping mango yields without detecting fruits from ground imagery, but rather, by linking yields with tree structural parameters. Such a tool will provide growers with accurate yield estimations at the orchard scale, and will permit them to study the parameters that drive yield heterogeneity within and between orchards. Full article

Vegetation metrics, such as leaf area (LA), leaf area density (LAD), and vertical leaf area profile, are essential measures of tree-scale biophysical processes associated with photosynthetic capacity, and canopy geometry. However, there are limited published investigations of their use for horticultural tree crops. This study evaluated the ability of light detection and ranging (LiDAR) for measuring LA, LAD, and vertical leaf area profile across two mango, macadamia and avocado trees using discrete return data from a RIEGL VZ-400 Terrestrial Laser Scanning (TLS) system. These data were collected multiple times for individual trees to align with key growth stages, essential management practices, and following a severe storm. The first return of each laser pulse was extracted for each individual tree and classified as foliage or wood based on TLS point cloud geometry. LAD at a side length of 25 cm voxels, LA at the canopy level and vertical leaf area profile were calculated to analyse tree crown changes. These changes included: (1) pre-pruning vs. post-pruning for mango trees; (2) pre-pruning vs. post-pruning for macadamia trees; (3) pre-storm vs. post-storm for macadamia trees; and (4) tree leaf growth over a year for two young avocado trees. Decreases of 34.13 m² and 8.34 m² in LA of mango tree crowns occurred due to pruning. Pruning for the high vigour mango tree was mostly identified between 1.25 m and 3 m. Decreases of 38.03 m² and 16.91 m² in LA of a healthy and unhealthy macadamia tree occurred due to pruning. After flowering and spring flush of the same macadamia trees, storm effects caused a 9.65 m² decrease in LA for the unhealthy tree, while an increase of 34.19 m² occurred for the healthy tree. The tree height increased from 11.13 m to 11.66 m, and leaf loss was mainly observed between 1.5 m and 4.5 m for the unhealthy macadamia tree. Annual increases in LA of 82.59 m² and 59.97 m² were observed for two three-year-old avocado trees. Our results show that TLS is a useful tool to quantify changes in the LA, LAD, and vertical leaf area profiles of horticultural trees over time, which can be used as a general indicator of tree health, as well as assist growers with improved pruning, irrigation, and fertilisation application decisions. Full article

Mango is affected by different decline disorders causing significant losses to mango growers. In the United Arab Emirates (UAE), the pathogen was isolated from all tissues sampled from diseased trees affected by Lasiodiplodia theobromae. Symptoms at early stages of the disease included general wilting appearance of mango trees, and dieback of twigs. In advanced stages, the disease symptoms were also characterized by the curling and drying of leaves, leading to complete defoliation of the tree and discolouration of vascular regions of the stems and branches. To substantially reduce the devastating impact of dieback disease on mango, the fungus was first identified based on its morphological and cultural characteristics. Target regions of 5.8S rRNA (ITS) and elongation factor 1-α (EF1-α) genes of the pathogen were amplified and sequenced. We also found that the systemic chemical fungicides, Score^®, Cidely^® Top, and Penthiopyrad^®, significantly inhibited the mycelial growth of L. theobromae both in vitro and in the greenhouse. Cidely^® Top proved to be a highly effective fungicide against L. theobromae dieback disease also under field conditions. Altogether, the morphology of the fruiting structures, molecular identification and pathogenicity tests confirm that the causal agent of the mango dieback disease in the UAE is L. theobromae. Full article