Apple Leaf Diseases Research Articles

VMF-SSD: A Novel V-Space based Multi-scale Feature Fusion SSD for Apple Leaf Disease Detection.

Apple leaf diseases seriously affect the quality of apples and may lead to yield losses, detecting apple leaf diseases accurately can prevent diseases from spreading and promote the healthy growth of the industry. However, recent studies cannot achieve accurate detection of leaf diseases with high accuracy because the lesions are of different sizes. So, this paper proposed a novel apple leaf disease detection method called VMF-SSD (V-space-based Multi-scale Feature-fusion SSD), which is designed to extract more reliable multi-scale feature representations for varied sizes of diseased spots and improve the final detection performance. The multi-scale feature extraction is established with multi-scale feature representation to further improve the disease detection performance, especially for small spots. After that, a V-space-based location branch is presented to enhance the texture feature information and help further identify disease spot location. Finally, attention mechanisms are utilized to automatically learn the importance of feature channels at different scales for distinguishing diseased spots of different sizes. Experimental results showed that the VMF-SSD method achieves 83.19% mAP and obtains the detection speed of 27.53 FPS on the test set, which indicates that the proposed VMF-SSD method can achieve competitive performance on apple leaf diseases detection task and satisfy the requirements of agricultural production applications.

基于语义分割网络的苹果叶片病害分割与分级研究

The key to diagnosing the types and degree of apple leaf diseases is to correctly segment apple leaf disease spots. Therefore, in order to effectively solve the problem of poor segmentation of leaves and diseased areas, the U2Net semantic segmentation network model was used in the research of apple leaf disease identification and disease diagnosis, and compared with the classic semantic segmentation network model DeepLabV3+ and UNet. In addition, the effects of different learning rates (0.01, 0.001, 0.0001) and optimizers (Adam, SGD) on the performance of U2Net network model were compared and analyzed. The experimental results showed that the learning rate is 0.001 and the optimizer is Adam, the average pixel accuracy (MPA) and mean intersection over union (MIoU) of the research model for lesion segmentation reach 98.87% and 84.43%, respectively. The results of this study were expected to provide the theoretical basis for the precise control of apple leaf disease.

Deep Learning Jaringan Saraf Tiruan Untuk Pemecahan Masalah Deteksi Penyakit Daun Apel

Diseases on apple leaves are becoming a major issue for apple growers since they can cause the crop to fail. Due to the diversity of diseases that can affect apple leaves, it can be challenging for farmers to determine the cause of leaf damage. The purpose of this research is to evaluate a convolutional neural network (CNN) method for its potential use in solving the problem of apple leaf disease identification. Four types of illness are dealt with: normal, multi-illness, rusty, and scabby. Many methods, such as data preparation and a preset VGG-16 artificial neural network (CNN) architecture, are recommended for use in the deep artificial neural network processing method. The most precise outcomes occurred when the beta parameter value was set to 2 = 0.999 at Ephoch to 85/100 with an accuracy of 0.7582, and when the epsilon parameter value was set to 1e-07 at Ephoch to 32/100 with an accuracy of 0.7582 with the best accuracy.

Apple Leaf Disease Identification in Complex Background Based on BAM-Net

Apples are susceptible to infection by various pathogens during growth, which induces various leaf diseases and thus affects apple quality and yield. The timely and accurate identification of apple leaf diseases is essential to ensure the high-quality development of the apple industry. In practical applications in orchards, the complex background in which apple leaves are located poses certain difficulties for the identification of leaf diseases. Therefore, this paper suggests a novel approach to identifying and classifying apple leaf diseases in complex backgrounds. First, we used a bilateral filter-based MSRCR algorithm (BF-MSRCR) to pre-process the images, aiming to highlight the color and texture features of leaves and to reduce the difficulty of extracting leaf disease features with subsequent networks. Then, BAM-Net, with ConvNext-T as the backbone network, was designed to achieve an accurate classification of apple leaf diseases. In this network, we used the aggregate coordinate attention mechanism (ACAM) to strengthen the network’s attention to disease feature regions and to suppress the interference of redundant background information. Then, the multi-scale feature refinement module (MFRM) was used to further identify deeper disease features and to improve the network’s ability to discriminate between similar disease features. In our self-made complex background apple leaf disease dataset, the proposed method achieved 95.64% accuracy, 95.62% precision, 95.89% recall, and a 95.25% F1-score. Compared with existing methods, BAM-Net has higher disease recognition accuracy and classification results. It is worth mentioning that BAM-Net still performs well when applied to the task of the leaf disease identification of other crops in the PlantVillage public dataset. This indicates that BAM-Net has good generalization ability. Therefore, the method proposed in this paper can be helpful for apple disease control in modern agriculture, and it also provides a new reference for the disease identification of other crops.

Method for Classifying Apple Leaf Diseases Based on Dual Attention and Multi-Scale Feature Extraction

Image datasets acquired from orchards are commonly characterized by intricate backgrounds and an imbalanced distribution of disease categories, resulting in suboptimal recognition outcomes when attempting to identify apple leaf diseases. In this regard, we propose a novel apple leaf disease recognition model, named RFCA ResNet, equipped with a dual attention mechanism and multi-scale feature extraction capacity, to more effectively tackle these issues. The dual attention mechanism incorporated into RFCA ResNet is a potent tool for mitigating the detrimental effects of complex backdrops on recognition outcomes. Additionally, by utilizing the class balance technique in conjunction with focal loss, the adverse effects of an unbalanced dataset on classification accuracy can be effectively minimized. The RFB module enables us to expand the receptive field and achieve multi-scale feature extraction, both of which are critical for the superior performance of RFCA ResNet. Experimental results demonstrate that RFCA ResNet significantly outperforms the standard CNN network model, exhibiting marked improvements of 89.61%, 56.66%, 72.76%, and 58.77% in terms of accuracy rate, precision rate, recall rate, and F1 score, respectively. It is better than other approaches, performs well in generalization, and has some theoretical relevance and practical value.

Real-Time Detection of Apple Leaf Diseases in Natural Scenes Based on YOLOv5

Aiming at the problem of accurately locating and identifying multi-scale and differently shaped apple leaf diseases from a complex background in natural scenes, this study proposed an apple leaf disease detection method based on an improved YOLOv5s model. Firstly, the model utilized the bidirectional feature pyramid network (BiFPN) to achieve multi-scale feature fusion efficiently. Then, the transformer and convolutional block attention module (CBAM) attention mechanisms were added to reduce the interference from invalid background information, improving disease characteristics’ expression ability and increasing the accuracy and recall of the model. Experimental results showed that the proposed BTC-YOLOv5s model (with a model size of 15.8M) can effectively detect four types of apple leaf diseases in natural scenes, with 84.3% mean average precision (mAP). With an octa-core CPU, the model could process 8.7 leaf images per second on average. Compared with classic detection models of SSD, Faster R-CNN, YOLOv4-tiny, and YOLOx, the mAP of the proposed model was increased by 12.74%, 48.84%, 24.44%, and 4.2%, respectively, and offered higher detection accuracy and faster detection speed. Furthermore, the proposed model demonstrated strong robustness and mAP exceeding 80% under strong noise conditions, such as exposure to bright lights, dim lights, and fuzzy images. In conclusion, the new BTC-YOLOv5s was found to be lightweight, accurate, and efficient, making it suitable for application on mobile devices. The proposed method could provide technical support for early intervention and treatment of apple leaf diseases.

Plant-Leaf Disease Prediction Using Deep Learning

Abstract: Brown spot, Mosaic, Grey spot, and Rust all significantly reduce apple yield. Rust is a sign of Foliar illness in this instance. The primary factor influencing apple output is the occurrence of apple leaf diseases, which results in significant yearly economic losses. Therefore, it is very important to research apple leaf disease identification. Plants are frequently attacked by pests, bacterial diseases, and other microorganisms. Inspection of the leaves, stem, or fruit usually identifies the attack's signs. Powdery Mildew and Leaf Blight are two common plant diseases that can cause severe harm if not treated quickly. In the realm of agriculture, image processing is frequently utilized for classification, detection, grading, and quality control. Finding and identifying plant diseases is crucial, especially when trying to produce fruit of the highest caliber. The real-time identification of apple leaf diseases is addressed in this research using a deep learning strategy that is based on enhanced convolutional neural networks (CNNs). This study uses data augmentation and image annotation tools to create the foliar disease dataset, which is made up of complex images captured in the field and laboratories. Overall, we can identify the illness present in plants on a massive scale by utilizing machine learning to train the vast data sets that are publically available. The project explains how to identify plant leaf diseases, how they affect plant yield, and which pesticides should be used to treat them. in agriculture. To monitor huge plant fields and automatically identify disease symptoms as soon as they develop on plant leaves, research on automatic plant disease is crucial. In this essay, we'll demonstrate how to identify plant illnesses by obtaining photos of their leaves

RE-RCNN: A Novel Representation-Enhanced RCNN Model for Early Apple Leaf Disease Detection

Apple leaf diseases have significant impacts on apple quality and productivity. So, the implementation of accurate disease detection in the early stages is a powerful guarantee for the rapid and high-quality development of the apple industry. However, early apple leaf disease often represents very small size disease spots, which makes the detection of early apple leaf disease a challenge for existing deep learning-based detection models. In this paper, a novel detection model called Representation-Enhanced RCNN(RE-RCNN) is proposed to perform accurate detection of early apple leaf disease spots. Firstly, an object-enhanced branch is proposed to achieve feature enhancement of small disease spots by introducing small disease spots feature enrichment extractor (SDSFEE). Secondly, a SCMLoss is proposed to balance the inter-class differences of various size disease spots under the same category. Thirdly, an one2one computation strategy is leveraged to sample data reasonably during the training process. From the final experimental results, it can be seen that the proposed model could achieve outstanding performance on the early apple leaf disease detection task.

Detecting diseases in apple tree leaves using FPN–ISResNet–Faster RCNN

ABSTRACT Apple leaf diseases typified by small disease spots are generally difficult to detect in images. This study proposes a deep learning model called the feature pyramid networks (FPNs) –inception squeeze-and-excitation ResNet (ISResNet)–Faster RCNN (region with convolutional neural network) model to improve the accuracy of detecting apple leaf diseases. Apple leaf diseases were identified, evaluated, and validated by using the FPN–ISResNet–Faster RCNN. The results were compared with those obtained by the single-shot multibox detector (SSD), Faster RCNN, and ISResNet–Faster RCNN. The detection accuracies obtained by using different feature extraction networks, positions and numbers of SE, inception modules, scales of FPN structures, and scales of anchor frames were also compared. The results showed that the values of average precision (AP), and APs with the thresholds of the intersection over union of 0.5 and 0.75 (AP50 and AP75), obtained from the FPN–ISResNet–Faster RCNN were 62.71%, 93.68%, and 70.94%, respectively, which are higher than those of the SSD, VGG–Faster RCNN, GoogleNet–Faster RCNN, ResNet50–Faster RCNN, ResNeXt–Faster RCNN, and ISResNet–Faster RCNN. FPN–ISResNet–Faster RCNN was shown to be able to detect diseases in apple leaves with high accuracy and generalizability.

LAD-Net: A Novel Light Weight Model for Early Apple Leaf Pests and Diseases Classification.

Aphids, brown spots, mosaics, rusts, powdery mildew and Alternaria blotches are common types of early apple leaf pests and diseases that severely affect the yield and quality of apples. Recently, deep learning has been regarded as the best classification model for apple leaf pests and diseases. However, these models with large parameters have difficulty providing an accurate and fast diagnosis of apple leaf pests and diseases on mobile terminals. This paper proposes a novel and real-time early apple leaf disease recognition model. AD Convolution is firstly utilized to replace standard convolution to make smaller number of parameters and calculations. Meanwhile, a LAD-Inception is built to enhance the ability of extracting multiscale features of different sizes of disease spots. Finally, the LAD-Net model is built by the LR-CBAM and the LAD-Inception modules, replacing a full connection with global average pooling to further reduce parameters. The results show that the LAD-Net, with a size of only 1.25MB, can achieve a recognition performance of 98.58%. Additionally, it is only delayed by 15.2ms on HUAWEI P40 and by 100.1ms on Jetson Nano, illustrating that the LAD-Net can accurately recognize early apple leaf pests and diseases on mobile devices in real-time, providing portable technical support.

EADD-YOLO: An efficient and accurate disease detector for apple leaf using improved lightweight YOLOv5.

Current detection methods for apple leaf diseases still suffer some challenges, such as the high number of parameters, low detection speed and poor detection performance for small dense spots, which limit the practical applications in agriculture. Therefore, an efficient and accurate model for apple leaf disease detection based on YOLOv5 is proposed and named EADD-YOLO. In the EADD-YOLO, the lightweight shufflenet inverted residual module is utilized to reconstruct the backbone network, and an efficient feature learning module designed through depthwise convolution is proposed and introduced to the neck network. The aim is to reduce the number of parameters and floating point of operations (FLOPs) during feature extraction and feature fusion, thus increasing the operational efficiency of the network with less impact on detection performance. In addition, the coordinate attention module is embedded into the critical locations of the network to select the critical spot information and suppress useless information, which is to enhance the detection accuracy of diseases with various sizes from different scenes. Furthermore, the SIoU loss replaces CIoU loss as the bounding box regression loss function to improve the accuracy of prediction box localization. The experimental results indicate that the proposed method can achieve the detection performance of 95.5% on the mean average precision and a speed of 625 frames per second (FPS) on the apple leaf disease dataset (ALDD). Compared to the latest research method on the ALDD, the detection accuracy and speed of the proposed method were improved by 12.3% and 596 FPS, respectively. In addition, the parameter quantity and FLOPs of the proposed method were much less than other relevant popular algorithms. In summary, the proposed method not only has a satisfactory detection effect, but also has fewer parameters and high calculation efficiency compared with the existing approaches. Therefore, the proposed method provides a high-performance solution for the early diagnosis of apple leaf disease and can be applied in agricultural robots. The code repository is open-sourced at https://github.com/AWANWY/EADD-YOLO.

Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis.

Infection in apple leaves is typically brought on by unanticipated weather conditions such as rain, hailstorms, draughts, and fog. As a direct consequence of this, the farmers suffer a significant loss of productivity. It is essential to be able to identify apple leaf diseases in advance in order to prevent the occurrence of this disease and minimise losses to productivity caused by it. The research offers a bibliometric analysis of the effectiveness of artificial intelligence in diagnosing diseases affecting apple leaves. The study provides a bibliometric evaluation of apple leaf disease detection using artificial intelligence. Through an analysis of broad current developments, publication and citation structures, ownership and cooperation patterns, bibliographic coupling, productivity patterns, and other characteristics, this scientometric study seeks to discover apple diseases. Nevertheless, numerous exploratory, conceptual, and empirical studies have concentrated on the identification of apple illnesses. However, given that disease detection is not confined to a single field of study, there have been very few attempts to create an extensive science map of transdisciplinary studies. In bibliometric assessments, it is important to take into account the growing amount of research on this subject. The study synthesises knowledge structures to determine the trend in the research topic. A scientometric analysis was performed on a sample of 214 documents in the subject of identifying apple leaf disease using a scientific search technique on the Scopus database for the years 2011-2022. In order to conduct the study, the Bibliometrix suite's VOSviewer and the web-based Biblioshiny software were also utilised. Important journals, authors, nations, articles, and subjects were chosen using the automated workflow of the software. Furthermore, citation and co-citation checks were performed along with social network analysis. In addition to the intellectual and social organisation of the meadow, this investigation reveals the conceptual structure of the area. It contributes to the body of literature by giving academics and practitioners a strong conceptual framework on which to base their search for solutions and by making perceptive recommendations for potential future research areas.

Diagnosis and Mobile Application of Apple Leaf Disease Degree Based on a Small-Sample Dataset.

The accurate segmentation of apple leaf disease spots is the key to identifying the classification of apple leaf diseases and disease severity. Therefore, a DeepLabV3+ semantic segmentation network model with an actors spatial pyramid pool module (ASPP) was proposed to achieve effective extraction of apple leaf lesion features and to improve the apple leaf disease recognition and disease severity diagnosis compared with the classical semantic segmentation network models PSPNet and GCNet. In addition, the effects of the learning rate, optimizer, and backbone network on the performance of the DeepLabV3+ network model with the best performance were analyzed. The experimental results show that the mean pixel accuracy (MPA) and mean intersection over union (MIoU) of the model reached 97.26% and 83.85%, respectively. After being deployed into the smartphone platform, the detection time of the detection system was 9s per image for the portable and intelligent diagnostics of apple leaf diseases. The transfer learning method provided the possibility of quickly acquiring a high-performance model under the condition of small datasets. The research results can provide a precise guide for the prevention and precise control of apple diseases in fields.

Multidimensional Attention-Based CNN Model for Identifying Apple Leaf Disease

To prevent the spread of illnesses and guarantee the steady and healthy growth of the apple sector, the proper diagnosis of apple leaf diseases is of utmost importance. The subtle interclass variations and enormous intraclass variances among apple leaf disease features, together with the uniformity of disease spots and the complicated background environment, make apple leaf disease diagnosis extremely challenging. A unique dual-branch apple leaf disease diagnosis system (DBNet) was put out to address the aforementioned issues. An attention branch with many dimensions and a multiscale joint branch (MS) make up the dual-branch network topology of the DBNet (DA). In this study, the MS branch and the DA branch are combined to create a DBNet, which successfully improves recognition accuracy while mitigating the negative impacts of complicated backdrop environments and lesion similarities. The accuracy of the DBNet network increases by 0.02843, 0.02412, 0.0144, and 0.0125, respectively, when compared to previous leaf disease detection models. This makes it evident that the suggested DBNet model has certain benefits over others in terms of identifying apple leaf disease.

A High-Precision Detection Method of Apple Leaf Diseases Using Improved Faster R-CNN

Apple leaf diseases seriously affect the sustainable production of apple fruit. Early infection monitoring of apple leaves and timely disease control measures are the key to ensuring the regular growth of apple fruits and achieving a high-efficiency economy. Consequently, disease detection schemes based on computer vision can compensate for the shortcomings of traditional disease detection methods that are inaccurate and time-consuming. Nowadays, to solve the limitations ranging from complex background environments to dense and small characteristics of apple leaf diseases, an improved Faster region-based convolutional neural network (Faster R-CNN) method was proposed. The advanced Res2Net and feature pyramid network architecture were introduced as the feature extraction network for extracting reliable and multi-dimensional features. Furthermore, RoIAlign was also employed to replace RoIPool so that accurate candidate regions will be produced to address the object location. Moreover, soft non-maximum suppression was applied for precise detection performance of apple leaf disease when making inferences to the images. The improved Faster R-CNN structure behaves effectively in the annotated apple leaf disease dataset with an accuracy of 63.1% average precision, which is higher than other object detection methods. The experiments proved that our improved Faster R-CNN method provides a highly precise apple leaf disease recognition method that could be used in real agricultural practice.

VGG16 Deep Learning Architecture Using Imbalance Data Methods For The Detection Of Apple Leaf Diseases

Data in the real world, there are many conditions (situations) where the number of instances in one class is much less than the number of instances in other classes. This situation is a problem in unbalanced datasets (imbalance class). As a result, performance in classification will decrease in some data systems. In this study, it was identified that the apple leaf disease performance dataset used had a large enough data imbalance problem where the comparison between instances was 1:5, so an oversampling method was needed to solve the data imbalance problem. Methods that can be used include the Synthetic Minority Over Sampling Technique (SMOTE). In order to validate the effectiveness of the proposed model, two experimental scenarios were carried out: first, the VGG16 algorithm was directly applied to modeling without considering class imbalance by reducing the number of layers and kernels in each layer to achieve optimal results, second, over-sampling SMOTE to increase the number of balanced datasets. The results showed that using the confusion matrix the accuracy results for each method were obtained where VGG 16 scored 85.16%, VGG 16 with SMOTE scored 92.94%. The conclusion of this study is that SMOTE helps improve the accuracy of leaf disease detection in apples.

Apple leaf disease identification via improved CycleGAN and convolutional neural network

The identification of apple leaf diseases is crucial to reduce yield reduction and timely take disease control measures. Employing deep learning for apple leaf disease identification is challenging because of the limited availability of samples for supervised training and the serious class imbalance. Hence, this paper proposes an accurate deep learning-based pipeline to solve the problem of limited data sets on farms and reduce the partiality due to serious class imbalance. Firstly, an improved cycle-consistent adversarial networks (CycleGAN) is used to generate synthetic samples to improve the learning of data distribution and solve the problems of small data sets and class imbalance. Secondly, ResNet is trained as a baseline convolutional neural network classifier to classify apple leaf diseases. The experimental results show that ResNet has the highest recognition accuracy on the test set, reaching 97.78%, and the classification accuracy is significantly improved by the generated synthetic samples (+ 14.7%). In addition, the experiment results of t-distributed stochastic neighbor embedding (t-SNE) and visual Turing test visually confirmed that the images generated by improved CycleGAN have much better quality and are more convincing.

An apple leaf disease identification model for safeguarding apple food safety

Apples are the fourth most produced fruit in the world, so it is important to safeguard them from disease damage. Although there are many deep learning-based plant disease detection models, the existing deep learning networks have complex structures and require large amounts of computational resources for support. Lightweight networks such as MobileNet and ShuffleNet designed for mobile devices could achieve better classification and faster recognition on public datasets, but their accuracy rates are not yet up to the requirements of practical applications. To address these problems, we constructed an improved apple leaf disease recognition algorithm based on MobileNetV2. We used feature reuse to improve the network structure, added a dense connection structure to the inverse residual module, and an ECA-Net attention module to increase the model's focus on diseased regions. We trained the improved model on the network on a dataset expanded by a generative adversarial network. The results showed that the improved model had a smaller number of model parameters, only 3.3 M, and a higher accuracy rate of 96.23% compared to Resnet50, ShuffleNet, and MobileNet models. The improved model had only 0.34 M addition in the number of parameters compared with MobileNet-V2, and had a 2.2% improvement in accuracy.

Detection and Application of Apple Leaf Diseases Based on Object Detection

Detection and Application of Apple Leaf Diseases Based on Object Detection

Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection

Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique is also utilized for the identification of the affected plant region from test image. Furthermore, Adam optimizer with EfficientNet-based feature extraction and Spiking Neural Network (SNN)-based classification are employed to detect and classify the apple plant leaf images. A wide range of simulations was conducted to ensure the effective outcomes of ESFO-EALD technique on benchmark dataset. The results reported the supremacy of the proposed ESFO-EALD approach than the existing approaches.