This review highlights the significant nutritional and bioactive value of the cashew apple (Anacardium occidentale), a widely distributed yet overlooked tropical fruit with major potential for juice production. Cashew apples are rich in nutrients—such as potassium, calcium, iron, and vitamins C, B1, and B2—that support immunity, skin, cardiovascular, and digestive health. Their distinctive tart-sweet flavor makes them useful in beverages and culinary products. Importantly, cashew apple juice offers antioxidant, antibacterial, and anticancer properties, drawing increased scientific attention. Although previous studies have described the nutritional composition or individual biological activities of cashew apple, a comprehensive review integrating its phytochemical profile, pharmacological potential, and industrial applications remains limited. This review addresses this gap by providing an updated and multidisciplinary synthesis of recent advances, linking food science, health, and sustainable utilization. The key contribution of this review is its comprehensive synthesis of the cashew apple’s nutritional content, pharmacological potential, and industrial applications, connecting insights from food science, medicine, and sustainability. Encouraging cashew apple juice production addresses both nutrition and agricultural waste, while stimulating food innovation. Overall, the cashew apple emerges as a promising functional food with health and economic advantages, deserving further research on its clinical and industrial applications.
Tamarind (Tamarindus indica L. cv. ‘Aglibut Sweet’) is an underutilized tropical fruit tree for which nutrient management strategies remain poorly defined, particularly regarding fruit biomass allocation among economically important components. A two-season field experiment evaluated the effects of nitrogen and potassium fertilization on fruit morphology, yield, and quality of mature tamarind trees grown on nutrient-deficient soil. Potassium exerted the strongest influence on fruit performance, significantly increasing pod width, total pod number, and the proportion of pods containing three or more seeds. The highest pod production was recorded under N1 × K4 (46.5 pods tree−1). A significant nitrogen × potassium interaction was observed for pulp percentage; with N1 combined with moderate potassium rates (K1–K2) producing the highest pulp proportions (57.3–58.4
This study compared tissue culture-derived (TC) plants with their respective mother plants (MPs) in three date palm (Phoenix dactylifera L.) cultivars (‘Gulistan’, ‘Kashuwari’, and ‘Dedhi’) to assess variations in fruit quality, biochemical traits, and multivariate relationships. Significant cultivar × plant type interactions (p < 0.0001) were observed for total sugars, reducing sugars, total soluble solids (TSS), and most fruit physical traits. Compared with those of the TC plants, the total sugars (2.48
Diaphorina citri Kuwayama (Hemiptera: Psyllidae) is a key citrus pest in India, and an important vector of Huanglongbing. This study characterized the seasonality and population dynamics of D. citri in ‘Kinnow’ mandarin orchards in south-western Punjab in relation to weather variables and host phenology conducted between 2012 and 2018. Adults were present almost year-round, surviving winter in the adult stage within the canopy, while nymphs occurred primarily during two major flushing periods: early February to April and August–September, with the first flush supporting the highest populations. Population build-up peaked in the 13th standard meteorological week (56.36 ± 2.05 nymphs and 32.46 ± 2.49 adults per 10-cm twig). Optimal conditions for proliferation were maximum temperatures of 21.69–37.87 °C, minimum temperatures of 7.30–20.53 °C, and relative humidity of 63.90–92.66
Fine-scale topographic heterogeneity may generate contrasting microenvironments within rainfed vineyards, but it remains unclear whether leaf gas exchange and secondary metabolism respond with similar sensitivity to these natural gradients. Topographic micro-abiotic stress gradients were evaluated for their effects on leaf-level physiological efficiency and secondary metabolism plasticity in the indigenous red grape cultivar ‘Papazkarası’ (Vitis vinifera L.) grafted on 5BB rootstock. The study was conducted during the 2024 growing season in a non-irrigated vineyard along a uniform 10.17
Mango leaf diseases need to be accurately identified to ensure high-quality mango production. Conventional testing methods take a lot of time and are at risk of mistakes, especially when identifying diseases that seem to be identical. To solve this problem, we propose REDNet, a deep learning approach for automated mango leaf disease detection. Based on a dataset of 3010 high-resolution images classified into five classes: Die Back, Bacterial Canker, Anthracnose, Healthy, and Gall Midge. We assess eight transfer learning models as a baseline in our study. Global average pooling, batch normalization, Gaussian noise regularization, and feature concatenation are all used in the model’s structure to increase generalization and minimize overfitting. When deploying in situations with limited resources, the model may take into account the additional architectural complexity and computing demands brought about by the integration of various backbone networks. To further increase interpretability and transparency, explainable artificial intelligence (XAI) methods, namely Gradient-weighted Class Activation Mapping (Grad-CAM), Score-weighted Class Activation Mapping (Score-CAM), and Eigen Class Activation Mapping (Eigen-CAM) are used to highlight the most significant areas of leaf images that affect the predictions that the model makes. The REDNet was the best among the tested models, with a classification accuracy of 99.668
The citrus mealybug, Planococcus citri Risso (Hemiptera: Pseudococcidae), is a major pest causing significant damage to pomegranate orchards through sap-feeding and sooty mold development. Effective management is complicated by the pest’s cryptic behavior and resistance to chemical controls. This study evaluated the combined augmentative release of the predator Cryptolaemus montrouzieri Mulsant (Coleoptera: Coccinellidae) and the parasitoid Anagyrus pseudococci Girault (Hymenoptera: Encyrtidae) using a banker box system in Iraqi pomegranate orchards over two years. The banker boxes, stocked with mealybug-infested potato tubers as an internal rearing substrate, were suspended within the lower tree canopy. The experiment utilized a randomized block design to compare two release rates: a standard rate (5 predators and 10 parasitoids per tree) and a high rate (10 predators and 20 parasitoids per tree) against a non-release control. Results indicated that the biological control treatments were significantly effective, with the high release rate achieving maximum mealybug suppression efficacy of 82.30
Intensive use of chemical fertilizers in ‘Kinnow’ mandarin orchards of the north-western plains has led to soil degradation, microbial depletion and nutrient imbalances, resulting in increased fruit drop, poor fruit quality and declining yields. Despite the need for sustainable nutrient management, systematic evaluation of integrated biofertilizer use in this region remains limited. To address this gap, the present study attempted to assess the effectiveness of vesicular arbuscular mycorrhiza (VAM), consortium biofertilizers (CB) and zinc-solubilizing bacteria (ZSB) applied individually and in combination on soil biological health, nutrient availability, fruit yield and quality of mandarin cv. ‘Kinnow’. The combined basal application of ZSB + VAM + CB was most effective, significantly increasing soil microbial populations and dehydrogenase activity and resulting in improved nutrient mobilization as reflected by enhanced soil and foliar nutrient concentrations (N, P, K and Zn). Ultimately, this treatment led to a substantial reduction in fruit drop and enhanced fruit yield and physico-chemical quality. Integrated biofertilizer application improves nutrient uptake, soil biological health, fruit yield and quality, offering a sustainable alternative to conventional fertilizer-based nutrient management in ‘Kinnow’ mandarin orchards.
Ficus semicordata: Buch.-Ham. ex Sm., an underutilized wild fig native to South and Southeast Asia, has long served as both food and traditional medicine in rural communities. This review critically synthesizes current knowledge on its nutritional composition, phytochemical diversity, biofunctional properties, and potential applications as a functional food. The fruits provide 138.86 kcal/100 g of energy and are good sources of carbohydrates, proteins, minerals (e.g., Ca 2623 ppm, Fe 264 ppm), and vitamins. Phytochemical screening reveals phenolics, flavonoids, tannins, terpenoids, sterols, and other compounds (e.g., lupeol, β‑sitosterol, squalene via gas chromatography–mass spectrometry), varying by plant part. Reported bioactivities include potent antioxidant (especially methanolic bark extracts), broad-spectrum antimicrobial, antidiarrheal, antidepressant and anxiolytic-like effects (preclinical), antidiabetic (enzyme inhibition), and variable hepatoprotective activity. However, most evidence is derived from in vitro and preclinical studies, and differences in plant parts, extraction methods, and experimental protocols limit direct comparison and practical application. Future research should emphasize standardized evaluation, postharvest and processing studies, safety assessment, and clinical validation to facilitate the development of F. semicordata as a functional food, nutraceutical, and value-added fruit crop.
Accurate monitoring of phenological dynamics in perennial apple orchards is essential for improving productivity and climate-resilient orchard management. This study integrated unmanned aerial vehicle (UAV)-derived multispectral imagery, field measurements, and atmospheric datasets to characterize tree-scale phenological variability and its environmental drivers. A total of 17 apple trees representing two cultivars (‘Royal Delicious’ and ‘Red Golden’) and four age classes (4, 12, 14, and 16 years) were monitored across six phenological stages, from winter dormancy (January) to post-harvest (October). Buffer-based canopy sampling extracted tree-level spectral information while minimizing mixed-pixel effects. Vegetation indices remained low during dormancy (−0.014–0.289), increased during full bloom (0.793–0.894), peaked at 0.931 during fruit set and development, and declined during maturation and post-harvest. Structural analysis showed strong age-dependent variation, with canopy volume increasing from 1.3 to 2.1 m3 in young trees to 86.0 m3 in mature trees, accompanied by higher canopy density indices. Multispectral analysis identified the red-edge (717 nm) and near-infrared (NIR; 840 nm) bands as the most sensitive indicators of canopy vigor, with NIR reflectance reaching 0.360–0.520 during active growth. Enhanced vegetation performance coincided with higher photosynthetically active radiation, precipitation (1455.83 mm), and root-zone soil moisture (up to 0.90). Integrating UAV multispectral observations with atmospheric information provides a robust framework for high-resolution phenological assessment, supporting precision orchard management, climate adaptation, and long-term phenological monitoring in perennial cropping systems.
Warmer winters and increased temperature variability in Mediterranean environments are expected to reduce winter chill and alter the timing and uniformity of flowering in European pears (Pyrus communis L.), with cascading effects on cross-pollination in self-incompatible cultivars and consequently affecting productivity. Endodormancy dynamics and agroclimatic requirements were assessed in El Hajeb, Morocco, during two crop seasons (2021–2022 and 2022–2023) for two pear cultivars (‘Elliot’ and ‘Harrow Sweet’). Endodormancy release was determined using forcing tests on excised shoots, combining bud fresh-weight dynamics and phenological development. Chill requirements from October to endodormancy release were quantified using the 0–7 °C, Utah, and Dynamic models. Heat requirements were calculated as Growing Degree Hours (GDH) from the day following endodormancy release to full flowering. Our findings highlighted that endodormancy release occurred about 10 days earlier in ‘Elliot’ than in ‘Harrow Sweet’ during the two studied years. Chill requirements averaged 115 Chill Hours (CH), 326 Chill Units (CU), and 27.2 Chill Portions (CP) for ‘Elliot’ and 149.5 CH, 388 CU, and 31.3 CP for ‘Harrow Sweet’, while heat requirements averaged 13,699 and 12,614 GDH, respectively. The full flowering dates, which were approximately similar between cultivars (variation of 2–3 days), were advanced by around 2 weeks in 2022–2023 compared with 2021–2022. This close temporal proximity suggests a potential overlap in flowering under the observed conditions, constituting an important trait for orchard management.
Scab disease is a common and destructive fungal infection that affects fruits such as apples, bananas, grapes, peaches, and pears. It appears as dark, rough lesions on the fruit surface, reducing both market value and shelf life. Early and accurate detection is essential to minimize economic losses and maintain fruit quality. Conventional techniques depend on manual evaluation conducted by agricultural specialists, which is a time-intensive process, subjective, and prone to errors, especially in large orchards. Deep learning models have shown good performance in image classification; however, current deep learning models for plant disease detection lack accuracy due to the small datasets used and the subtle visual symptoms. This limits the applicability of the models, as they fail to generalize well across different fruit types and ecological conditions. This study addresses these challenges and limitations. It incorporates advanced deep learning approaches for scab disease detection and classification. We propose FruScab-E3Net, a Vision Transformer (ViT)-based method that employs a convolutional neural network (CNN), specifically a transfer learning model based on ResNet50. FruScab-E3Net achieved an accuracy of 98.7
Apple is one of the most important fruit species globally in terms of production volume, consumption level, and trade volume. In recent years, in line with the increasing population, changing consumer preferences, and healthy nutrition trends, the demand for apples has increased, and in parallel, there have been significant developments in global production and trade volumes. However, there is not always a direct relationship between the production levels of countries and their competitiveness in international trade. The aim of this study was to determine the international competitiveness of prominent countries in global apple production and export. Apple production and export data for the period 2015–2024 were analyzed. The analysis focused on China, the United States, Türkiye, Poland, India, Italy, Iran, the Russian Federation, France, and Chile. The data were obtained from the Food and Agriculture Organization of the United Nations (FAO) database, and the competitiveness of the countries was evaluated using the revealed comparative advantage (RCA), symmetric revealed comparative advantage (SRCA), and normalized revealed comparative advantage (NRCA) indices. According to the research findings, while global apple production was approximately 82.4 million tons in 2015, it reached 97.8 million tons in 2024. China maintains its leading position in global apple production with an average annual production of 44 million tons and a share of 52.4
Epigenetic control represents the first tier of gene regulation, enabling plants to dynamically respond to environmental stresses and coordinate developmental processes without altering DNA sequences. This study examined the complex roles of epigenetic mechanisms—DNA methylation, histone modification, chromatin remodeling, and non-coding RNAs—in enhancing stress tolerance and regulating fruit quality traits. The epigenomic response to abiotic stresses such as drought, salinity, and extreme temperatures involves dynamic modifications that lead to metabolic reprogramming and the establishment of stress memory, thereby improving resilience to future stress. Concurrently, epigenetic reprogramming governs key aspects of fruit development, including ripening initiation and the synthesis of pigments and flavor compounds. Variations in DNA methylation, mediated by methyltransferases and demethylases such as DML2, play crucial roles in both stress adaptation and fruit development. Histone modifications, including H3K4me3 and H3K27me3, regulate stress memory and developmental transitions through gene expression control. Small RNAs, particularly miRNAs and siRNAs, contribute to epigenetic regulation via RNA-directed DNA methylation pathways, silencing transposable elements and modulating stress-responsive genes. Advances in biotechnological approaches, including CRISPR/dCas9-based epigenome editing and epigenetic marker-assisted selection, hold significant potential for developing climate-resilient and high-quality cultivars. This review provides a comprehensive perspective on harnessing epigenetic variation to address global challenges in agricultural sustainability and food security by integrating both mechanistic insights and translational applications.
Asymmetric tropical fruits such as papaya, jackfruit, and guava are difficult to subject to automated ripeness detection because of their irregular shapes, inconsistent color change, and textures. We propose a field-programmable gate array (FPGA)-optimized hybrid asymmetric fruit ripeness detection system (AFRDS) with hue, saturation, and value (HSV) color codes, texture features, shape features, statistical size moments, and lightweight convolutional neural network (CNN) embedding. We further proposed fruit-specific adaptive thresholding for automatic differentiation between unripe, ripe, and overripe fruit. The proposed pipelined FPGA architecture allows real-time processing at the same time. The system achieved over 94.2
Banana cultivation plays an important role in the agricultural economy of Andhra Pradesh, particularly in the Ravulapalem region, which is well known for banana production. This study presents EAAF-Net (Ensemble AI Annotation Framework), an applied AI workflow for banana lifecycle classification and semi-automated image annotation using images acquired with a Raspberry Pi-based imaging system under controlled postharvest storage conditions. The workflow is designed to support the classification of three banana lifecycle stages—unripe, ripe, and spoiled—while reducing manual annotation effort and facilitating the development of reliable agricultural image datasets. Rather than proposing a new deep learning architecture, EAAF-Net combines three established pretrained convolutional neural network backbones, namely, EfficientNet-B0, Xception, and MobileNetV2, through ensemble probability averaging. A confidence-guided decision mechanism automatically accepts high-confidence predictions while identifying uncertain samples for manual verification, thereby improving annotation consistency and supporting efficient semi-automated dataset construction. The workflow is implemented as an edge-artificial intelligence (AI) system in which image acquisition and model inference are performed locally, without cloud computing or network-based Internet of Things (IoT) connectivity. The evaluation demonstrates that the proposed workflow provides competitive classification performance while offering a practical and scalable framework for confidence-guided annotation of banana lifecycle images. The proposed approach provides a useful foundation for intelligent vision-based applications in postharvest quality assessment, banana lifecycle monitoring, and precision agriculture, with future work directed toward broader field validation and IoT-enabled agricultural monitoring systems.
Mango is an important tropical fruit crop, but its production is affected by fungal diseases, which compromise fruit quality, market value, and overall yield. Common diseases such as anthracnose, Alternaria rot, black mould rot, and stem-end rot can lead to huge pre- and postharvest losses. Thus, early and accurate detection of these diseases is crucial for effective crop protection and management. This study introduces an automated image-based system for multi-class mango disease classification. The proposed method integrates a Vision Transformer (ViT) for feature extraction with an Extreme Gradient Boosting classifier that is optimized with the Harris Hawks Optimization algorithm (HHO). Experiments were conducted on a publicly available dataset of 5000 mango fruit images of five classes. The proposed framework achieved a best classification accuracy of 97.47 ± 0.73
Passiflora Cincinnata (Mast) ‘BRS Sertão Forte’ was developed for semi-arid regions due to its tolerance to water stress. However, the green color of the fruits makes it difficult to determine the most suitable harvest stage. Additionally, cultivation systems (irrigated vs. rainfed) may influence fruit quality by altering physiological and biochemical processes. Therefore, this study aimed to evaluate how cultivation systems and ripening stages affect the post-harvest characteristics of P. cincinnata ‘BRS Sertão Forte’ fruits. The experiment was conducted at the experimental farm of UFCG, Pombal campus, using wild passion fruit plants in their second production cycle. A randomized block design was employed in a 2 × 4 factorial scheme: two cultivation systems (irrigated and rainfed) and four ripening stages (60, 80, 100, and 120 days after anthesis—DAA). Physical, chemical, and bioactive characteristics of the fruits were evaluated. Irrigation promoted larger fruit development, while rainfed conditions enhanced carotenoid accumulation. Based on an integrated assessment of quality indicators such as pulp volume, SS/TA ratio, and non-reducing sugar content, fruits harvested around 80 DAA represent a balanced and suitable harvest stage under both cultivation systems.
The objective of this study was to evaluate the effects of ascorbic acid on the physiology, growth, quality, and tolerance of sour passion fruit seedlings cv. BRS GA1 under salt stress conditions. The treatments consisted of five levels of electrical conductivity of irrigation water—ECw (0.3, 1.3, 2.3, 3.3, and 4.3 dS m−1) and four concentrations of ascorbic acid—AsA (0, 120, 240, and 360 mg L−1), arranged in a randomized block design in a 5 × 4 factorial scheme, with four replicates and two plants per plot, totaling 160 experimental units. Irrigation water salinity above 0.75 dS m−1 compromises the development of sour passion fruit seedlings cv. BRS GA1 by reducing growth, photosynthetic pigment accumulation, and biomass production, while increasing electrolyte leakage. Foliar application of ascorbic acid at concentrations ranging from 110 to 360 mg L−1 promotes the maintenance of plant water status, cellular integrity, and seedling growth under high salinity (4.3 dS m−1), with 230 mg L−1 being the most effective concentration for plant height, biomass accumulation, and Dickson Quality Index. However, ascorbic acid intensified the negative effects of salt stress on the CO2 assimilation rate, instantaneous carboxylation efficiency, water use efficiency, and number of leaves.
Timely and accurate detection of plant diseases is crucial for minimizing manual labor associated with large-scale farm monitoring and for enabling early intervention. Walnut (Juglans regia), a commercially valuable crop, is susceptible to various foliar diseases, including anthracnose, powdery mildew, and gall mite. These diseases are often difficult to diagnose accurately without expert knowledge, especially in the early stages. While several existing studies have focused primarily on Anthracnose, limited attention has been given to the comprehensive identification of multiple walnut leaf diseases. In this study, we propose a lightweight deep learning-based approach for automatic detection and classification of walnut leaf diseases using convolutional neural networks (CNNs). A custom image dataset was manually collected from different regions of Kashmir (Pahalgam, Kulgam, and Yaripora), capturing diverse disease manifestations under real-world conditions. The model was trained and validated using k‑fold cross-validation. Experimental results demonstrate that the proposed CNN model significantly outperforms conventional machine learning algorithms and pre-trained deep learning models. Specifically, our approach achieved a mean classification accuracy of 97
