Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (124)

Search Parameters:
Keywords = Apple-Net

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
20 pages, 4261 KiB  
Article
Apple Yield Estimation Method Based on CBAM-ECA-Deeplabv3+ Image Segmentation and Multi-Source Feature Fusion
by Wenhao Cui, Yubin Lan, Jingqian Li, Lei Yang, Qi Zhou, Guotao Han, Xiao Xiao, Jing Zhao and Yongliang Qiao
Sensors 2025, 25(10), 3140; https://doi.org/10.3390/s25103140 - 15 May 2025
Viewed by 118
Abstract
Apple yield estimation is a critical task in precision agriculture, challenged by complex tree canopy structures, growth stage variability, and orchard heterogeneity. In this study, we apply multi-source feature fusion by combining vegetation indices from UAV remote sensing imagery, structural feature ratios from [...] Read more.
Apple yield estimation is a critical task in precision agriculture, challenged by complex tree canopy structures, growth stage variability, and orchard heterogeneity. In this study, we apply multi-source feature fusion by combining vegetation indices from UAV remote sensing imagery, structural feature ratios from ground-based fruit tree images, and leaf chlorophyll content (SPAD) to improve apple yield estimation accuracy. The DeepLabv3+ network, optimized with Convolutional Block Attention Module (CBAM) and Efficient Channel Attention (ECA), improved fruit tree image segmentation accuracy. Four structural feature ratios were extracted, visible-light and multispectral vegetation indices were calculated, and feature selection was performed using Pearson’s correlation coefficient analysis. Yield estimation models were constructed using k-nearest neighbors (KNN), partial least squares (PLS), random forest (RF), and support vector machine (SVM) algorithms under both single feature sets and combined feature sets (including vegetation indices, structural feature ratios, SPAD, vegetation indices + SPAD, vegetation indices + structural feature ratios, structural feature ratios + SPAD, and the combination of all three). The optimized CBAM-ECA-DeepLabv3+ model achieved a mean Intersection over Union (mIoU) of 0.89, an 8% improvement over the baseline DeepLabv3+, and outperformed U2Net and PSPNet. The SVM model based on multi-source feature fusion achieved the highest apple yield estimation accuracy in small-scale orchard sample plots (R2 = 0.942, RMSE = 12.980 kg). This study establishes a reliable framework for precise fruit tree image segmentation and early yield estimation, advancing precision agriculture applications. Full article
(This article belongs to the Section Smart Agriculture)
Show Figures

Figure 1

27 pages, 411 KiB  
Systematic Review
Artificial Neural Networks for Image Processing in Precision Agriculture: A Systematic Literature Review on Mango, Apple, Lemon, and Coffee Crops
by Christian Unigarro, Jorge Hernandez and Hector Florez
Informatics 2025, 12(2), 46; https://doi.org/10.3390/informatics12020046 - 6 May 2025
Viewed by 268
Abstract
Precision agriculture is an approach that uses information technologies to improve and optimize agricultural production. It is based on the collection and analysis of agricultural data to support decision making in agricultural processes. In recent years, Artificial Neural Networks (ANNs) have demonstrated significant [...] Read more.
Precision agriculture is an approach that uses information technologies to improve and optimize agricultural production. It is based on the collection and analysis of agricultural data to support decision making in agricultural processes. In recent years, Artificial Neural Networks (ANNs) have demonstrated significant benefits in addressing precision agriculture needs, such as pest detection, disease classification, crop state assessment, and soil quality evaluation. This article aims to perform a systematic literature review on how ANNs with an emphasis on image processing can assess if fruits such as mango, apple, lemon, and coffee are ready for harvest. These specific crops were selected due to their diversity in color and size, providing a representative sample for analyzing the most commonly employed ANN methods in agriculture, especially for fruit ripening, damage, pest detection, and harvest prediction. This review identifies Convolutional Neural Networks (CNNs), including commonly employed architectures such as VGG16 and ResNet50, as highly effective, achieving accuracies ranging between 83% and 99%. Additionally, it discusses the integration of hardware and software, image preprocessing methods, and evaluation metrics commonly employed. The results reveal the notable underuse of vegetation indices and infrared imaging techniques for detailed fruit quality assessment, indicating valuable opportunities for future research. Full article
Show Figures

Figure 1

16 pages, 1970 KiB  
Article
Impact of Photoselective Nets on Phenolic Composition and Antioxidant Capacity in Different Apple Cultivars Under the Same Edaphoclimatic Conditions
by João David Teixeira, Miguel Leão de Sousa, Sílvia Cruz Barros, Pier Parpot, Carina Almeida and Ana Sanches Silva
Molecules 2025, 30(9), 1995; https://doi.org/10.3390/molecules30091995 - 30 Apr 2025
Viewed by 157
Abstract
Phenolic compounds in apples provide significant health benefits, including antioxidant, and anti-inflammatory properties. The phenolic profile and content in apples are influenced by genetics, environmental factors, and agricultural practices. Photoselective nets, which are designed to filter specific wavelengths of light, might impact fruit [...] Read more.
Phenolic compounds in apples provide significant health benefits, including antioxidant, and anti-inflammatory properties. The phenolic profile and content in apples are influenced by genetics, environmental factors, and agricultural practices. Photoselective nets, which are designed to filter specific wavelengths of light, might impact fruit quality and phenolic content. This study aimed to assess the effects of photoselective nets on the antioxidant capacity and phenolic composition of three different apple cultivars grown under the same edaphoclimatic conditions. Five nets were selected. Fruits grown under the nets were compared with unprotected fruits. Antioxidant capacity was evaluated, and phenolic profiles were established by Ultra-High Performance Liquid Chromatography coupled with Time of Flight–Mass Spectrometry (UHPLC-ToF-MS). The results demonstrate a significant impact of the nets on the phenolic composition and antioxidant activities of apples. Different net colors had distinct effects on the accumulation of phenolic compounds, with some nets increasing flavonoid production and others reducing the levels of important phenolic acids. The gray and IRIDIUM® Red nets enhanced the production of quercetin and its derivatives, while chlorogenic acid showed a general decline under net-covered conditions, indicating a possible dependence on direct sunlight. The responses were also cultivar-dependent, with Gala redlum apples showing the largest reductions in phenolic compounds when protected by nets. Antioxidant assays also confirmed that the nets influenced the antioxidant potential of apples in a cultivar-dependent manner. These findings suggest that the retention of bioactive compounds in fruits might be strategically managed by selecting appropriate net materials for specific cultivars. Full article
Show Figures

Figure 1

27 pages, 5073 KiB  
Review
A Comprehensive Review of Deep Learning in Computer Vision for Monitoring Apple Tree Growth and Fruit Production
by Meng Lv, Yi-Xiao Xu, Yu-Hang Miao and Wen-Hao Su
Sensors 2025, 25(8), 2433; https://doi.org/10.3390/s25082433 - 12 Apr 2025
Viewed by 724
Abstract
The high nutritional and medicinal value of apples has contributed to their widespread cultivation worldwide. Unfavorable factors in the healthy growth of trees and extensive orchard work are threatening the profitability of apples. This study reviewed deep learning combined with computer vision for [...] Read more.
The high nutritional and medicinal value of apples has contributed to their widespread cultivation worldwide. Unfavorable factors in the healthy growth of trees and extensive orchard work are threatening the profitability of apples. This study reviewed deep learning combined with computer vision for monitoring apple tree growth and fruit production processes in the past seven years. Three types of deep learning models were used for real-time target recognition tasks: detection models including You Only Look Once (YOLO) and faster region-based convolutional network (Faster R-CNN); classification models including Alex network (AlexNet) and residual network (ResNet); segmentation models including segmentation network (SegNet), and mask regional convolutional neural network (Mask R-CNN). These models have been successfully applied to detect pests and diseases (located on leaves, fruits, and trunks), organ growth (including fruits, apple blossoms, and branches), yield, and post-harvest fruit defects. This study introduced deep learning and computer vision methods, outlined in the current research on these methods for apple tree growth and fruit production. The advantages and disadvantages of deep learning were discussed, and the difficulties faced and future trends were summarized. It is believed that this research is important for the construction of smart apple orchards. Full article
(This article belongs to the Section Smart Agriculture)
Show Figures

Figure 1

14 pages, 2404 KiB  
Article
Wind Pollination of Apple Flowers Under Insect Exclusion Nets Questions the Insect-Dependent Pollination Model of Modern Apple Plantations
by Mokhles Elsysy, Aziz Ebrahimi and Todd Einhorn
Plants 2025, 14(8), 1196; https://doi.org/10.3390/plants14081196 - 11 Apr 2025
Viewed by 297
Abstract
Pollination is essential for producing temperate-zone tree fruits like apples (Malus × domestica). While traditionally considered insect-dependent, this view may result from orchard designs tailored to European honeybees. Previous research showed that low-seed apples could develop in insect exclusion nets, suggesting [...] Read more.
Pollination is essential for producing temperate-zone tree fruits like apples (Malus × domestica). While traditionally considered insect-dependent, this view may result from orchard designs tailored to European honeybees. Previous research showed that low-seed apples could develop in insect exclusion nets, suggesting wind as an alternative pollinator. This study investigated the paternal origin of seeds and fruit set under nets compared to open canopies. Netted canopies of ‘Gala’, Fuji’, and ‘Honeycrisp’ set commercial fruit numbers without manual thinning. To determine the parental source of seeds, genotyping was performed using 16 SNP markers tailored for distinguishing apple cultivars, with primer design and genotyping conducted via the KASP™ system. Results showed significant genetic overlap between seeds from netted and non-netted fruits and nearby pollinizers, ruling out self-pollination. Netted canopies retained fruits with similar or fewer seeds compared to abscised fruits in open canopies, indicating fruit set depends on the population’s seed content rather than individual fruit seed count. These findings supporting the hypothesis that apple trees are adapted to utilize both wind and insect pollination. While wind pollination offers a sustainable approach, it requires adjustments in orchard design to ensure sufficient pollen transfer for reliable fertilization and yield. Full article
(This article belongs to the Section Plant Development and Morphogenesis)
Show Figures

Figure 1

20 pages, 10432 KiB  
Article
Apple Watercore Grade Classification Method Based on ConvNeXt and Visible/Near-Infrared Spectroscopy
by Chunlin Zhao, Zhipeng Yin, Yushuo Tan, Wenbin Zhang, Panpan Guo, Yaxing Ma, Haijian Wu, Ding Hu and Quan Lu
Agriculture 2025, 15(7), 756; https://doi.org/10.3390/agriculture15070756 - 31 Mar 2025
Viewed by 248
Abstract
To address the issues of insufficient rigor in existing methods for quantifying apple watercore severity and the complexity and low accuracy of traditional classification models, this study proposes a method for watercore quantification and a classification model based on a deep convolutional neural [...] Read more.
To address the issues of insufficient rigor in existing methods for quantifying apple watercore severity and the complexity and low accuracy of traditional classification models, this study proposes a method for watercore quantification and a classification model based on a deep convolutional neural network. Initially, visible/near-infrared transmission spectral data of apple samples were collected. The apples were then sliced into 4.5 mm thick sections using a specialized tool, and image data of each slice were captured. Using BiSeNet and RIFE algorithms, a three-dimensional model of the watercore regions was constructed from the apple slices to calculate the watercore severity, which was subsequently categorized into five distinct levels. Next, methods such as the Gramian Angular Summation Field (GASF), Gram Angular Difference Field (GADF), and Markov Transition Field (MTF) were applied to transform the one-dimensional spectral data into two-dimensional images. These images served as input for training and prediction using the ConvNeXt deep convolutional neural network. The results indicated that the GADF method yielded the best performance, achieving a test set accuracy of 98.73%. Furthermore, the study contrasted the classification and prediction of watercore apples using traditional methods with the existing quantification approaches for watercore levels. The comparative results demonstrated that the proposed GADF-ConvNeXt model is more straightforward and efficient, achieving superior performance in classifying watercore grades. Furthermore, the newly proposed quantification method for watercore levels proved to be more effective. Full article
(This article belongs to the Section Digital Agriculture)
Show Figures

Figure 1

17 pages, 7698 KiB  
Article
Plant Disease Segmentation Networks for Fast Automatic Severity Estimation Under Natural Field Scenarios
by Chenyi Zhao, Changchun Li, Xin Wang, Xifang Wu, Yongquan Du, Huabin Chai, Taiyi Cai, Hengmao Xiang and Yinghua Jiao
Agriculture 2025, 15(6), 583; https://doi.org/10.3390/agriculture15060583 - 10 Mar 2025
Viewed by 735
Abstract
The segmentation of plant disease images enables researchers to quantify the proportion of disease spots on leaves, known as disease severity. Current deep learning methods predominantly focus on single diseases, simple lesions, or laboratory-controlled environments. In this study, we established and publicly released [...] Read more.
The segmentation of plant disease images enables researchers to quantify the proportion of disease spots on leaves, known as disease severity. Current deep learning methods predominantly focus on single diseases, simple lesions, or laboratory-controlled environments. In this study, we established and publicly released image datasets of field scenarios for three diseases: soybean bacterial blight (SBB), wheat stripe rust (WSR), and cedar apple rust (CAR). We developed Plant Disease Segmentation Networks (PDSNets) based on LinkNet with ResNet-18 as the encoder, including three versions: ×1.0, ×0.75, and ×0.5. The ×1.0 version incorporates a 4 × 4 embedding layer to enhance prediction speed, while versions ×0.75 and ×0.5 are lightweight variants with reduced channel numbers within the same architecture. Their parameter counts are 11.53 M, 6.50 M, and 2.90 M, respectively. PDSNetx0.5 achieved an overall F1 score of 91.96%, an Intersection over Union (IoU) of 85.85% for segmentation, and a coefficient of determination (R2) of 0.908 for severity estimation. On a local central processing unit (CPU), PDSNetx0.5 demonstrated a prediction speed of 34.18 images (640 × 640 pixels) per second, which is 2.66 times faster than LinkNet. Our work provides an efficient and automated approach for assessing plant disease severity in field scenarios. Full article
(This article belongs to the Special Issue Computational, AI and IT Solutions Helping Agriculture)
Show Figures

Figure 1

40 pages, 36566 KiB  
Article
Web-Based AI System for Detecting Apple Leaf and Fruit Diseases
by Serra Aksoy, Pinar Demircioglu and Ismail Bogrekci
AgriEngineering 2025, 7(3), 51; https://doi.org/10.3390/agriengineering7030051 - 20 Feb 2025
Viewed by 659
Abstract
The present study seeks to improve the accuracy and reliability of disease identification in apple fruits and leaves through the use of state-of-the-art deep learning techniques. The research investigates several state-of-the-art architectures, such as Xception, InceptionV3, InceptionResNetV2, EfficientNetV2M, MobileNetV3Large, ResNet152V2, DenseNet201, and NASNetLarge. [...] Read more.
The present study seeks to improve the accuracy and reliability of disease identification in apple fruits and leaves through the use of state-of-the-art deep learning techniques. The research investigates several state-of-the-art architectures, such as Xception, InceptionV3, InceptionResNetV2, EfficientNetV2M, MobileNetV3Large, ResNet152V2, DenseNet201, and NASNetLarge. Among the models evaluated, ResNet152V2 performed best in the classification of apple fruit diseases, with a rate of 92%, whereas Xception proved most effective in the classification of apple leaf diseases, with 99% accuracy. The models were able to correctly recognize familiar apple diseases like blotch, scab, rot, and other leaf infections, showing their applicability in agriculture diagnosis. An important by-product of this research is the creation of a web application, easily accessible using Gradio, to conduct real-time disease detection through the upload of apple fruit and leaf images by users. The app gives predicted disease labels along with confidence values and elaborate information on symptoms and management. The system also includes a visualization tool for the inner workings of the neural network, thereby enabling higher transparency and trust in the diagnostic process. Future research will aim to widen the scope of the system to other crop species, with larger disease databases, and to improve explainability further to facilitate real-world agricultural application. Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
Show Figures

Figure 1

15 pages, 2799 KiB  
Article
Effects of Different Coverage Years of Hail-Proof Nets on Environment, Leaf Traits and Fruit Quality in Apple Orchards
by Junjiao Guo, Yu Guo, Panpan Tong, Xiang Wang and Jiangbo Wang
Horticulturae 2025, 11(2), 198; https://doi.org/10.3390/horticulturae11020198 - 13 Feb 2025
Viewed by 601
Abstract
The aim of this study was to explore the impacts of hail-proof nets with different coverage years on the environment, leaf traits and fruit quality of ‘Fuji’ apple orchards, with the expectation of providing a basis for the scientific application of the coverage [...] Read more.
The aim of this study was to explore the impacts of hail-proof nets with different coverage years on the environment, leaf traits and fruit quality of ‘Fuji’ apple orchards, with the expectation of providing a basis for the scientific application of the coverage years of hail-proof nets. The test results indicated that hail nets with different coverage years could reduce light intensity in the orchard and increase air humidity to a certain extent, exerting a certain positive regulatory effect on the orchard’s temperature. The laying of hail nets had no significant influence on the thickness of tree foliage but significantly enlarged the leaf area. The hail nets covered for 2 years notably enhanced the chlorophyll content and photosynthetic performance of leaves. Different coverage years of hail netting had no significant effect on the fruit weight per fruit and the fruit shape index. The fruit luster gradually diminished and was significantly lower than that of the control as the coverage years increased. Further determination of the intrinsic quality of the fruits revealed that hail nets with different coverage years had no significant impact on the fruit hardness, soluble solids and total phenolic content. However, the soluble sugar, solid/acid ratio, flavonoids and vitamin C content of the fruits covered with 2-year hail nets were significantly higher than those of the other treatments. In addition, covering the hail net for 3 years significantly reduced the percentage of the sugar core fruit rate and sugar core index, while covering the hail net for 1 year, 2 years, and not covering the hail net were more effective in maintaining the sugar core index of the fruits. A comprehensive evaluation of the principal components of the hail net treatments with different coverage years demonstrated that the 2-year hail net treatment was superior to the others. In summary, covering hail nets could improve the microenvironment of the orchard, leaf traits and fruit quality to a certain degree. When the hail-proof net had been covered for more than 2 years, its protective performance and the enhancement effect on fruit quality weakened, and it was recommended that the hail-proof net should be replaced in a timely manner. Full article
(This article belongs to the Section Fruit Production Systems)
Show Figures

Figure 1

24 pages, 20137 KiB  
Article
Real-Time Accurate Apple Detection Based on Improved YOLOv8n in Complex Natural Environments
by Mingjie Wang and Fuzhong Li
Plants 2025, 14(3), 365; https://doi.org/10.3390/plants14030365 - 25 Jan 2025
Viewed by 818
Abstract
Efficient and accurate apple detection is crucial for the operation of apple-picking robots. To improve detection accuracy and speed, we propose a lightweight apple-detection model based on the YOLOv8n framework. The proposed model introduces a novel Self-Calibrated Coordinate (SCC) attention module, which enhances [...] Read more.
Efficient and accurate apple detection is crucial for the operation of apple-picking robots. To improve detection accuracy and speed, we propose a lightweight apple-detection model based on the YOLOv8n framework. The proposed model introduces a novel Self-Calibrated Coordinate (SCC) attention module, which enhances feature extraction, especially for partially occluded apples, by effectively capturing spatial and channel information. Additionally, we replace the C2f module within the YOLOv8n neck with a Partial Convolution Module improved with Reparameterization (PCMR), which accelerates detection, reduces redundant computations, and minimizes both parameter count and memory access during inference. To further optimize the model, we fuse multi-scale features from the second and third pyramid levels of the backbone architecture, achieving a lightweight design suitable for real-time detection. To address missed detections and misclassifications, Polynomial Loss (PolyLoss) is integrated, enhancing class discrimination for different apple subcategories. Compared to the original YOLOv8n, the improved model increases the mAP by 2.90% to 88.90% and improves the detection speed to 220 FPS, which is 30.55% faster. Additionally, it reduces the parameter count by 89.36% and the FLOPs by 2.47%. Experimental results demonstrate that the proposed model outperforms mainstream object-detection algorithms, including Faster R-CNN, RetinaNet, SSD, RT-DETR-R18, RT-DETR-R34, YOLOv5n, YOLOv6-N, YOLOv7-tiny, YOLOv8n, YOLOv9-T and YOLOv11n, in both mAP and detection speed. Notably, the improved model has been used to develop an Android application deployed on the iQOO Neo6 SE smartphone, achieving a 40 FPS detection speed, a 26.93% improvement over the corresponding deployment of YOLOv8n, enabling real-time apple detection. This study provides a valuable reference for designing efficient and lightweight detection models for resource-constrained apple-picking robots. Full article
(This article belongs to the Section Plant Modeling)
Show Figures

Figure 1

24 pages, 13159 KiB  
Article
Plant Leaf Disease Detection Using Deep Learning: A Multi-Dataset Approach
by Manjunatha Shettigere Krishna, Pedro Machado, Richard I. Otuka, Salisu W. Yahaya, Filipe Neves dos Santos and Isibor Kennedy Ihianle
J 2025, 8(1), 4; https://doi.org/10.3390/j8010004 - 15 Jan 2025
Viewed by 4181
Abstract
Agricultural productivity is increasingly threatened by plant diseases, which can spread rapidly and lead to significant crop losses if not identified early. Detecting plant diseases accurately in diverse and uncontrolled environments remains challenging, as most current detection methods rely heavily on lab-captured images [...] Read more.
Agricultural productivity is increasingly threatened by plant diseases, which can spread rapidly and lead to significant crop losses if not identified early. Detecting plant diseases accurately in diverse and uncontrolled environments remains challenging, as most current detection methods rely heavily on lab-captured images that may not generalise well to real-world settings. This paper aims to develop models capable of accurately identifying plant diseases across diverse conditions, overcoming the limitations of existing methods. A combined dataset was utilised, incorporating the PlantDoc dataset with web-sourced images of plants from online platforms. State-of-the-art convolutional neural network (CNN) architectures, including EfficientNet-B0, EfficientNet-B3, ResNet50, and DenseNet201, were employed and fine-tuned for plant leaf disease classification. A key contribution of this work is the application of enhanced data augmentation techniques, such as adding Gaussian noise, to improve model generalisation. The results demonstrated varied performance across the datasets. When trained and tested on the PlantDoc dataset, EfficientNet-B3 achieved an accuracy of 73.31%. In cross-dataset evaluation, where the model was trained on PlantDoc and tested on a web-sourced dataset, EfficientNet-B3 reached 76.77% accuracy. The best performance was achieved with the combination of the PlanDoc and web-sourced datasets resulting in an accuracy of 80.19% indicating very good generalisation in diverse conditions. Class-wise F1-scores consistently exceeded 90% for diseases such as apple rust leaf and grape leaf across all models, demonstrating the effectiveness of this approach for plant disease detection. Full article
Show Figures

Figure 1

14 pages, 3521 KiB  
Article
Attention Score-Based Multi-Vision Transformer Technique for Plant Disease Classification
by Eu-Tteum Baek
Sensors 2025, 25(1), 270; https://doi.org/10.3390/s25010270 - 6 Jan 2025
Cited by 1 | Viewed by 1191
Abstract
This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision [...] Read more.
This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision Transformer (ViT) architecture, the Multi-ViT model aggregates diverse feature representations by combining outputs from multiple ViTs, each capturing unique visual patterns. This approach allows for a holistic analysis of spatially distributed symptoms, crucial for accurately diagnosing diseases in trees. Extensive experiments conducted on apple, grape, and tomato leaf disease datasets demonstrate the model’s superior performance, achieving over 99% accuracy and significantly improving F1 scores compared to traditional methods such as ResNet, VGG, and MobileNet. These findings underscore the effectiveness of the proposed model for precise and reliable plant disease classification. Full article
(This article belongs to the Special Issue Artificial Intelligence and Key Technologies of Smart Agriculture)
Show Figures

Figure 1

19 pages, 2196 KiB  
Article
Physiological and Biochemical Effects of Potassium Deficiency on Apple Tree Growth
by Evangelia-Vasiliki Ladikou, Gerasimos Daras, Marco Landi, Theocharis Chatzistathis, Thomas Sotiropoulos, Stamatis Rigas and Ioannis E. Papadakis
Horticulturae 2025, 11(1), 42; https://doi.org/10.3390/horticulturae11010042 - 6 Jan 2025
Viewed by 1085
Abstract
Potassium (K) is an essential mineral element that supports numerous plant processes, including photosynthesis, enzyme activation, osmoregulation, and nutrient balance. This study investigated how K deficiency impacts growth, physiological performance, and carbohydrate metabolism in ‘Granny Smith’ apple trees grafted onto M9 rootstock. The [...] Read more.
Potassium (K) is an essential mineral element that supports numerous plant processes, including photosynthesis, enzyme activation, osmoregulation, and nutrient balance. This study investigated how K deficiency impacts growth, physiological performance, and carbohydrate metabolism in ‘Granny Smith’ apple trees grafted onto M9 rootstock. The experimental material was cultivated hydroponically in a greenhouse under four K regimes, including 0.00, 0.75, 1.50, and 3.00 mM K, over 159 days. Deficiency symptoms such as chlorosis and necrosis were observed primarily in basal leaves. A reduced net photosynthetic rate in top and basal leaves was linked to a decreased stomatal conductance, thus limiting CO2 uptake (stomatal limitations of photosynthesis). Photosynthetic pigments, including chlorophyll a, chlorophyll b, and carotenoids, were also significantly reduced in K-limited leaves. Furthermore, photochemical performance of PSII also declined under K deficiency, with lower electron transport rates, PSII efficiency, and photochemical quenching (non-stomatal limitations of photosynthesis). While the photosynthetic rate declined under K deficiency conditions, the carbohydrate metabolism remained relatively stable without significant variation in total, translocating, or non-translocating sugars. Notably, an increase in sucrose-to-hexose ratio under low K suggests changes in sugar partitioning and utilization. Biomass allocation was also affected, with a notable decrease in the shoot-to-root ratio, mainly due to increased dry weight of roots, likely reflecting an adaptive response to enhance K uptake. Our study provides valuable insights into sustainable K fertilization practices aiming to maximize photosynthetic capacity, pigment content, and biomass production. These findings emphasize the importance of considering rootstock/scion interactions in future research to enhance apple tree vigor and productivity. Full article
(This article belongs to the Section Fruit Production Systems)
Show Figures

Figure 1

24 pages, 9193 KiB  
Article
Determination Model of Epidermal Wettability for Apple Rootstock Cutting Based on the Improved U-Net
by Xu Wang, Lixing Liu, Jinxuan Zou, Hongjie Liu, Jianping Li, Pengfei Wang and Xin Yang
Agriculture 2024, 14(12), 2223; https://doi.org/10.3390/agriculture14122223 - 5 Dec 2024
Viewed by 647
Abstract
Keeping the epidermis of apple rootstock cuttings moist is important for maintaining physiological activities. It is necessary to monitor the epidermis moisture in real time during the growth process of apple rootstock cuttings. A machine vision-based discrimination model for the moisture degree of [...] Read more.
Keeping the epidermis of apple rootstock cuttings moist is important for maintaining physiological activities. It is necessary to monitor the epidermis moisture in real time during the growth process of apple rootstock cuttings. A machine vision-based discrimination model for the moisture degree of cuttings’ epidermis was designed. This model optimizes the structure of the semantic segmentation model U-Net. The model takes the Saturation channel and Value channel information of the cutting images in the HSV color space as the characteristics of the cuttings’ moisture, so that the model has good performance in the blue-purple supplementary light environment. The average accuracy of the improved model is 95.07% for dry and wet cuttings without supplementary light, and 84.83% with supplementary light. The humidification system implanted in the model can control the atomizer to complete the task of moisturizing the cuttings’ epidermis. The average moisture retention rate of the humidification system for cuttings was 92.5%. Compared with the original model, the moisturizing effect of the humidification system increased by 26.87%. The experimental results show that the improved U-Net model has good generalization and high accuracy, which provides a method for the design of an accurate humidification system. Full article
(This article belongs to the Section Digital Agriculture)
Show Figures

Figure 1

18 pages, 6257 KiB  
Article
Enhanced Disease Detection for Apple Leaves with Rotating Feature Extraction
by Zhihui Qiu, Yihan Xu, Chen Chen, Wen Zhou and Gang Yu
Agronomy 2024, 14(11), 2602; https://doi.org/10.3390/agronomy14112602 - 4 Nov 2024
Cited by 1 | Viewed by 1161
Abstract
Leaf diseases such as Mosaic disease and Black Rot are among the most common diseases affecting apple leaves, significantly reducing apple yield and quality. Detecting leaf diseases is crucial for the prevention and control of these conditions. In this paper, we propose incorporating [...] Read more.
Leaf diseases such as Mosaic disease and Black Rot are among the most common diseases affecting apple leaves, significantly reducing apple yield and quality. Detecting leaf diseases is crucial for the prevention and control of these conditions. In this paper, we propose incorporating rotated bounding boxes into deep learning-based detection, introducing the ProbIoU loss function to better quantify the difference between model predictions and real results in practice. Specifically, we integrated the Plant Village dataset with an on-site dataset of apple leaves from an orchard in Weifang City, Shandong Province, China. Additionally, data augmentation techniques were employed to expand the dataset and address the class imbalance issue. We utilized the EfficientNetV2 architecture with inverted residual structures (FusedMBConv and S-MBConv modules) in the backbone network to build sparse features using a top–down approach, minimizing information loss. The inclusion of the SimAM attention mechanism effectively captures both channel and spatial attention, expanding the receptive field and enhancing feature extraction. Furthermore, we introduced depth-wise separable convolution and the CAFM in the neck network to improve feature fusion capabilities. Finally, experimental results demonstrate that our model outperforms other detection models, achieving 93.3% mAP@0.5, 88.7% Precision, and 89.6% Recall. This approach provides a highly effective solution for the early detection of apple leaf diseases, with the potential to significantly improve disease management in apple orchards. Full article
(This article belongs to the Section Precision and Digital Agriculture)
Show Figures

Figure 1

Back to TopTop