Apple Research Articles

Optimizing ‘red Fuji’ apple quality: Auxin-mediated calcium distribution via fruit-stalk in bagging practices

In apples, a bottleneck effect in calcium (Ca) transport within fruit stalk has been observed. To elucidate that how auxin affects Ca forms and distribution in the apple fruit stalk, we investigated the effects of different concentrations of auxin treatment (0, 10, 20, and 30 mg·L−1) on Ca content, forms, distribution, and fruit quality during later stages of fruit expansion. The results showed that auxin treatment led to a dramatic reduction in total Ca content in stalk, while an approximately 30 % increase in fruit. Furthermore, auxin treatment effectively enhanced the functionality of xylem vessels in vascular bundles of the stalk in bagged apples. Finally, TOPSIS method was used to assess fruit quality, with treatments ranked as follows: IAA20 > NAA20 > IAA30 > IAA10 > CK > NPA. The findings lay a foundation for further studies on the bottleneck in Ca transport within stalk, uneven distribution of Ca in fruit, and provide insights into Ca utilization efficiency in bagged apples.

Determination of leaf nitrogen content in apple and jujube by near-infrared spectroscopy

The nitrogen content of apple leaves and jujube leaves is an important index to judge the growth and development of apple trees and jujube trees to a certain extent. The prediction performance of the two samples was compared between different models for leaf nitrogen content, respectively. The near-infrared absorption spectra of 287 apple leaf samples and 192 jujube leaf samples were collected. After eliminating the outliers by Mahalanobis distance method, the remaining spectral data were processed by six different preprocessing methods. BP neural network (BP), random forest regression (RF), least partial squares (PLS), K-Nearest Neighbor (KNN), and support vector regression (SVR) were compared to establish prediction models of nitrogen content in apple leaves and jujube leaves. The results showed that the determination coefficient (R2), root mean square error (RMSE) and residual prediction deviation (RPD) of the models established by different combined pretreatment methods were compared among the five methods. Compared with the performance of the other four models, the modeling method of SG + SD + CARS + RF was suitable for the prediction of nitrogen content in apple leaves, and its modeling set R2 was 0.85408, RMSE was 0.082188, and RPD was 2.5864. The validation set R2 is 0.75527, RMSE is 0.099028, RPD is 2.1956. The modeling method of FD + CARS + PLS was suitable for the prediction of nitrogen content in jujube leaves. The modeling set R2 was 0.7954, RMSE was 0.14558, and RPD was 2.4264; the validation set R2 is 0.81348, RMSE is 0.089217, and RPD is 2.4552.In the prediction modeling of apple leaf nitrogen content in the characteristic band, the model quality of RF was better than the other four prediction models. The model quality of PLS in predictive modeling of nitrogen content of jujube leaves in characteristic bands is superior to the other four predictive models, These results provide a reference for the use of near-infrared spectroscopy to determine whether apple trees and jujube trees are deficient in nutrients.

Genome-wide identification, molecular evolution, and functional characterization of fructokinase gene family in apple reveal its role in improving salinity tolerance

Fructokinase (FRK) is a regulator of fructose signaling in plants and gateway proteins that catalyze the initial step in fructose metabolism through phosphorylation. Our previous study demonstrated that MdFRK2 protein exhibit not only high affinity for fructose, but also high enzymatic activity due to sorbitol. However, genome-wide identification of the MdFRK gene family and their evolutionary dynamics in apple are yet to be reported. A systematic genome-wide analysis in this study identified a total of nine MdFRK gene members, which could phylogenetically be clustered into seven groups. Chromosomal location and synteny analysis of MdFRKs revealed that their expansion in the apple genome is primarily driven by tandem and segmental duplication events. Divergent expression patterns of MdFRKs were observed in four source-sink tissues and at five different apple fruit developmental stages, which suggested their potential crucial roles in the apple fruit development and sugar accumulation. Reverse transcription-quantitative PCR (RT-qPCR) identified candidate NaCl or drought stress responsive MdFRKs, and transgenic apple plants overexpressing MdFRK2 exhibited considerably enhanced salinity tolerance. Our results will be useful for understanding the functions of MdFRKs in the regulation of apple fruit development and salt stress response.

The MdIAA29-MdARF4 complex plays an important role in balancing plant height with salt and drought stress responses.

Breeding dwarf apple (Malus domestica) varieties is a recent trend in agriculture because such varieties are easy to maintain and have high yields; however, dwarf apple trees generally have poor stress tolerance. Balancing apple plant height and stress response has been an important breeding goal. In this study, aux/indole-3-acetic acid 29 gene in apple (MdIAA29) overexpression lines (#1, #2, #3) had reduced plant height by 39%, 31%, and 35%, respectively, suitable for close planting applications. Surprisingly, the dwarf MdIAA29-overexpression lines also showed increased plant tolerance to salt and drought stresses. Further analysis showed that MdIAA29 inhibited the regulation of auxin response factor 4 (ARF4) on Gretchen Hagen 3.9 (GH3.9) gene and 9-cis-epoxycarotenoid dioxygenase 3 (NCED3) gene in apple and changed the contents of auxin and abscisic acid in different tissues, thus achieving a balance between plant height and stress tolerance. In addition, we also found that MdIAA7 enhanced the inhibitory effect of MdIAA29 on MdARF4. In brief, the MdIAA29-MdARF4 complex significantly impacts the height of apple plants and their ability to respond to salt and drought stress.

Predictions of apple mechanical damage volume using micro-CT measurements and support vector regression(SVR)

Accurately calculating the damage volume and making clear the interconnected effects of the physical and chemical properties of apples on mechanical damage are crucial steps in reducing the possibility of apple damage. Tests have been conducted on apples at different maturity levels, including measuring the firmness, moisture content, water-soluble pectin (WSP) content, soluble solids content (SSC) of the flesh, and elastic modulus of the apple flesh and peel. Transient collisions were performed using a pendulum device to create damage zones under specific impact energies. Then, the X-ray micro-computed tomography (Micro-CT) was utilized to quantitatively analyse mechanical damage volumes, the effects of apple tissue characteristics and impact energy on the damage volume were analysed in detail. The results indicated that higher-maturity apples were more susceptible to mechanical damage, and Micro-CT measurements were more accurate when the impact energy ≥ 0.05 J, while the empirical formula showed greater deviation; the curvature radius at the impact point can be considered as a latent variable influencing the apple damage volume. Furthermore, a damage volume prediction model, based on bruise area calculated by the empirical formula, WSP content of the flesh, and elastic modulus of the apple flesh and peel, was established. With a testing dataset without anticipate in model training for verification, the developed model achieved a coefficient of determination of 0.9782, indicating that the model can predict damage volume effectively and reduce errors associated with the empirical formula, particularly at higher impact energies. The research can provide insights into potential applications in apple industry practices to reduce the mechanical damage.

γ-Aminobutyric acid mediated by MdCBF3- MdGAD1 mitigates low temperature damage in apple

Low temperatures can seriously affect apple yield and can also cause chilling injury to apple fruit. γ-aminobutyric acid (GABA) plays an important role in improving plant stress resistance. Some studies have reported that GABA can improve cold resistance in plants, only through exogenous treatment; however, the molecular mechanism of its resistance to low temperature is still unknown. This result suggested that exogenous GABA treatment of both apple seedlings and fruit could improve the resistance of apple to low temperatures. MdGAD1, a key gene involved in GABA synthesis, was overexpressed in tomato plants and apple callus to improve their cold tolerance. Both yeast one-hybrid and luciferase assay showed that MdCBF3 could bind to the MdGAD1 promoter to activate its expression and promote GABA synthesis. These results revealed a molecular mechanism utilizing the MdCBF3-MdGAD1 regulatory module that can enhance cold resistance by increasing endogenous GABA synthesis in apple.

Early Detection and Classification of Apple Leaf Diseases Using Deep Learning Technique

Apple leaf diseases represent a critical challenge for apple crop production, threatening both the quality and quantity of harvests. Leaves are essential to the growth and health of apple trees, as they play a pivotal role in photosynthesis and nutrient transport. When these leaves become infected, the diseases can rapidly spread across the entire tree, leading to significant damage that can impact the yield and the overall health of the orchard. The consequences of such outbreaks can be devastating, with severe economic losses for farmers and potentially reduced marketability of the crops. To mitigate these risks, timely and accurate detection of apple leaf diseases is crucial. Early intervention allows farmers to apply targeted treatments and prevent the spread of infection to other parts of the tree or adjacent trees in the orchard. This research proposes an improved deep learning approach, utilizing the VGG16 model, to detect early signs of apple leaf diseases. By focusing on small spots and widespread lesions, the model can identify symptoms at an early stage, allowing for prompt intervention to prevent further spread. The study employs a dataset from Kaggle and a custom repository called Apple Fungal Diseases Dataset, with the classification results compared to other leading techniques to ensure accuracy. By providing a reliable method for early detection, this research aims to support farmers in managing apple leaf diseases more effectively, ultimately contributing to healthier orchards and improved crop yields.

Mathematical model for predicting the yield of apple trees on rootstock 62-396

Traditionally, forecasting apple tree yields was carried out on the basis of statistics of already obtained long-term yield dynamics or based on generative formations formed on the tree. The purpose of the work is to identify the most significant soil parameters for the formation of the apple tree varieties yield on the dwarf rootstock 62-396 and to build a mathematical model for predicting fruit yield in the conditions of the Central Chernozem region of the Russian Federation. Field research was carried out in 2004-2022 in industrial apple tree plantations at the age of full fruiting in the Lipetsk (two farms) and Tambov (eight farms) regions. Seven horticultural soil types have been studied. Based on statistical analysis, a mathematical model of apple tree productivity was obtained in the form of an analytical dependence on three factors. A geometric interpretation of the regression model is given using the Mathcad symbolic mathematics system in the form of response surfaces and the corresponding level lines. Practical recommendations are given for choosing rational parameters within selected intervals of factor variation.

Determination of Proximate Content and Phytochemical Analysis of Some Selected Grains, Fruits and Vegetables Commercially Sold in Eke Awka Market, Awka, Anambra State, Nigeria

Six samples from cucumber (Cucumis sativus), apple (Malus domestica), cabbage (Brassica oleracea), Tigernuts (Cyperus esculentus), Beans (Phaseolus vulgaris), Green peas (Pisum sativum) were purchased from Eke Awka market, Awka in Anambra state, and evaluated for their proximate composition and phytochemicals content using the standard procedures as described by Association of Official Analytical Collaboration International, (1995). The study revealed that moisture content of cucumber was significantly high, followed by cabbage, apple and then tiger nuts. The outcome of Phytochemical analysis showed that Alkaloids, Flavonoid Phenol and Oxalate levels were highest in cabbage; Saponin and Tannin were highest in tiger nuts while Steroids and Phytate were highest in cucumber. The fruits and vegetables analyzed contain appreciable amounts of phytochemicals and relatively high food value thus making them valuable nutraceutical sources and very useful for nutritional and medicinal purposes Keywords: Proximate composition, Phytochemical analysis, Grains, Fruits, Vegetables

Enhancement of Apple Stress Resistance via Proline Elevation by Sugar Substitutes.

Plants encounter numerous adversities during growth, necessitating the identification of common stress activators to bolster their resistance. However, the current understanding of these activators' mechanisms remains limited. This study identified three anti-stress activators applicable to apple trees, all of which elevate plant proline content to enhance resistance against various adversities. The results showed that the application of these sugar substitutes increased apple proline content by two to three times compared to the untreated group. Even at a lower concentration, these activators triggered plant stress resistance without compromising apple fruit quality. Therefore, these three sugar substitutes can be exogenously sprayed on apple trees to augment proline content and fortify stress resistance. Given their effectiveness and low production cost, these activators possess significant application value. Since they have been widely used in the food industry, they hold potential for broader application in plants, fostering apple industry development.

Apple fruit as a biological suppressant for potato tuber sprouting during ambient storage

In many countries, potato (Solanum tuberosum) is a crucial carbohydrate-rich crop and staple food. However, sprouting during storage can adversely affect the quality of the harvested tubers. To maintain the postharvest quality, this study assessed the potential of apple fruit as one of the biological suppressants for potato tuber sprouting at ambient storage. Potato tubers were obtained from four commercial farms. Thereafter, they were stored in a brown paper alone (control) or with apple fruit at ±23 °C for 30-day period. Potato tubers were evaluated for their weight loss, sprouting percentage, decay and soluble sugars during storage duration. Tubers stored with apple fruit had significantly (P < 0.05) reduced physiological weight loss after 30-day storage compared to the control. The results indicated that sprouting was significantly lower on tubers stored with fruit compared to the control. Sucrose, glucose and fructose increased in tubers stored with apple fruit compared to the control, especially in tubers obtained from Jamba and Leeubult. Tubers stored with apple fruit decayed significantly compared to the control in tubers from Jamba and Leeubult. Furthermore, dry matter and starch content were significantly lower tubers stored with apples compared to the control. In conclusion, apple fruit could serve as an effective sprout suppressant for potatoes at ambient storage. Therefore, apple fruit can be adopted as an alternative sprout suppressant to synthetic ethylene gas and various chemicals such as Chloropropham.

INSECTICIDE MEDOUZ, MD IN THE FIGHT AGAINST APPLE CODLING MOTH AND APHIDS ON APPLE TREES

The insecticide Medouz, MD, SC demonstrated high effectiveness against aphids and apple codling moth on apple trees when applied at a rate of 0.3 L/ha. We recommend the use of the insecticide Medouz, MD, SC against aphids and apple codling moth on apple trees, applied at a rate of 0.3 L/ha during the plant's vegetative period.

Gibberellic acid modulates the expression of flowering-related genes and the flower bud rate in apple (Malus domestica)

Gibberellic acid modulates the expression of flowering-related genes and the flower bud rate in apple (<i>Malus domestica</i>)

Apple fruits categorizing based on deep convolutional neural network techniques

&lt;p&gt;For a variety of reasons, including the high degree of similarity between varieties of the same type of fruit, the requirement to train the technique on a large amount of data, and the type and number of features suitable for application, the use of computer vision techniques in the classification of fruits still faces many challenges. Additionally, the technique's effectiveness and speed both need to be improved. Deep conventional neural network (DCNN) approaches were required for all of these reasons. A proposed CNN model is described in this work. The suggested methodology is intended to quickly and accurately categorize thirteen groups of apple fruits. The proposed technique was based on training and testing the model on a maximum number of images of apple fruits, by increasing the number of database images tenfold, after augmentation was performed on the images. The technology also relied on good tuning of the hyperparameters. To further ensure the efficiency of training, validation was performed on 20% of the database. All results that demonstrate the high efficiency of the proposed model were reviewed. The results of the proposal were compared with the results of four related techniques. The results showed the great advantage of the proposed technology at all levels.&lt;/p&gt;

Modeling the impact of long-term land use changes on deep soil hydrological processes in the Loess Plateau, China

Land use change can significantly affect soil hydrology in arid and semi-arid regions, making it crucial to understand the relationship between vegetation roots and soil moisture. Current models often fail to predict root growth and its impacts on water dynamics accurately. Our work presents a novel model that seamlessly integrates the Community Land Model (CLM) with the Soil & Water Assessment Tool (SWAT). Furthermore, it enhances the root module within the CLM, enabling more accurate simulations of dynamic root depth and distribution across varying tree ages. This improvement particularly considers the crucial processes of dormancy and plant maturity. Soil moisture and root patterns under apple trees of varying ages and in wheat fields on the Loess Plateau was analyzed. Our findings indicate that our dynamic root depth model outperforms traditional static models, and can accurately reflect soil moisture levels with high precision (R2 = 0.80–0.81; Nash-Sutcliffe efficiency (NSE) = 0.65–0.75). In contrast to methods that utilize fixed root depths, dynamic root simulation can provide new insights. As apple orchards mature, the roots of 22-year-old apple trees have been found to reach a depth of 21 m in the soil. Conversely, the maximum root depth of wheat is limited to 1.9 m. This latter finding aligns more closely with the measured root depths, highlighting the accuracy of dynamic simulations. This model reveals that older apple orchards show decreased soil moisture at greater depths (>20 m), contrasting with wheat fields that affect moisture mostly within the top 2 m. Our results underscore the crucial role of dynamic modeling in comprehending root-soil water interactions. Furthermore, they imply that extended orchard cultivation practices can lead to a substantial depletion of deep soil moisture. Specifically, over a period of 1 to 22 years, a water deficit of up to 85 mm yr−1 has been observed. For a 22-year-old forest, the D-D (dynamic distributions of coarse and fine roots) method calculates a significant cumulative deep Soil Water Storage loss. Over the course of 22 years, this loss amounts to 1664 mm, which is almost three times compare to the annual rainfall recorded. Such a large loss has the potential to significant impact on groundwater recharge. This highlights the need for careful consideration in future afforestation efforts to prevent increased soil aridity.

Competition for nutrient niches within the apple blossom microbiota antagonizes the initiation of fire blight infection.

Changes in the plant microbiota composition are intimately associated with the health of the plant, but factors controlling the microbial community in flowers are poorly understood. In this study, we used apple flowers and fire blight as a model system to investigate the effects of floral microbiota and microbial competition on disease development and suppression. To compare changes in microbial flora with the RNA expression patterns of plants, the flower samples were collected in three different flowering stages (Bud, Popcorn, and Full-bloom). Using advanced sequencing technology, we analyzed the data and conducted both in vitro and in vivo experiments to validate our findings. Our results show that the Erwinia amylovora use arabinogalactan, which is secreted on the flowers, for early colonization of apple flowers. Pantoea agglomerans was more competitive for arabinogalactan than E. amylovora. Additionally, P. agglomerans suppressed the expression of virulence factors of E. amylovora by using arabinose, which is a major component of arabinogalactan, which induces virulence gene expression. The present data provide new insights into developing control strategies for diverse plant diseases, including fire blight, by highlighting the importance of nutrients in disease development or suppression.

Efficient batch size detection of apple fruit in a plantation environment

There is growing interest in using deep learning for object recognition in robots to enhance the efficiency of apple farming. While deep learning-based object detection has shown promising results in various visual tasks, more research is needed to accurately recognize apples in orchard environments. During the training phase, it is important to determine the optimal values of hyperparameters. This research aims to develop a deep learning model, YOLOv7, to reliably identify apples in orchards, using four different batch size values for training. The MinneApple dataset, trained with these batch sizes, serves as our reference model. To assess the model’s ability to work in different situations, we evaluate it using test data with varying input scales. Our results show that the optimal batch size for detecting apples in orchards is 16, achieving a mean average precision (mAP) of 50%. Furthermore, our findings suggest that increasing the batch size does not improve the efficiency of apple detection in orchard environments.

Deep transfer learning classification of apple fruit diseases

This paper applies deep convolution neural networks (DCNN) to apple fruit disease classification. Twelve DCNN methods (SqueezeNet, GoogleNet, InceptionV3, DenseNet201, ReaNet50, ResNet101, Xception, InceptionResnetV2, EfficientnetB0, AlexNet, VGG16, and VGG19) have been used. These methods have been trained to classify apples into four categories: normal, blotch, rot, and scab. A dataset of 5179 images, including 3472 for normal, 171 for blotch, 1166 for rot, and 370 for scab, has been used. A practical test on 120 images (30 for each category) has been applied. Seven of these DCNNs—InceptionV3, DenseNet201, ResNet101, ResNet50, GoogleNet, AlexNet, and VGG16—have the best accuracy. InceptionV3 is the highest. It has achieved an accuracy of 100% for all categories. The used dataset is unbalanced and small. So, it's necessary to use data augmentation to overcome any overfitting that may cause. After applying data augmentation, the dataset is balanced and contains 13888 images (3472 for each category). The seven DCNNs are retrained by the balanced dataset and retested by the same 120 images. All DCNN's accuracy has enhanced except InceptionV3, which has decreased. On the other hand, RasNet101 has achieved an accuracy of 100% for all categories. Therefore, ResNet101 has been recommended for apple fruit disease classification.

Review of image processing and artificial intelligence methodologies for apple leaf disease diagnosis

&lt;span lang="EN-US"&gt;Apple leaf disease (ALD) potentially affects the apple tree's health by reducing fruit yield and its capability to grow healthy. The prime purpose of the proposed study is to review and assess the strengths and weaknesses associated with the frequently exercised methods of ALD diagnosis using image processing and artificial intelligence (AI). Although these are widely adopted in recent studies, the core notion is to find the pros and cons associated with the practical viability. A desk research methodology is undertaken to carry out proposed review work where a database of recent scientific manuscripts is collected and studied very closely. The existing approaches are reviewed concerning identified problems, adopted solutions, advantages, and limitations. Finally, the paper contributes towards offering insight into potential research gap which will guide the upcoming researchers to make wise decisions for planning their models. The results acquired from this review work show that generalized challenges of ALD are not addressed, less emphasis on illumination variability, reduced target to minimize complexity, lesser evidence towards real-time processing, no evidence towards interpretability, limitation of available dataset, and tradeoff-between image processing and AI.&lt;/span&gt;

Unlocking the potential of biostimulants in sustainable agriculture: Effect of wood distillate on the nutritional profiling of apples

In this work, we report the investigation of the effect of exposure of apple trees to the bioeffector wood distillate (WD), a plant biostimulant used for improving the nutritional profiling of crop plants. We measured the effect by evaluating the biochemical and nutritional profile of both pulps and skin of fruits. WD (0.2 %, v/v) was applied once a week by foliar application, from May 2023 until September 2023. The results indicate that the WD-treated apples have a significant increase in several analyzed parameters (i.e., phenols, flavonoids, tannins, total antioxidant power, sugars, pectin, free amino acids, and mineral element content), especially in the pulp. These data were also confirmed by NMR and LC-ESI-MS techniques. This study pointed out that WD could be a handy tool for the cultivation of fruit trees.