Leaf disease identification is one of the most promising applications of convolutional neural networks(CNNs).This method represents a significant step towards revolutionizing agriculture by enabling the quick and accu...Leaf disease identification is one of the most promising applications of convolutional neural networks(CNNs).This method represents a significant step towards revolutionizing agriculture by enabling the quick and accurate assessment of plant health.In this study,a CNN model was specifically designed and tested to detect and categorize diseases on fig tree leaves.The researchers utilized a dataset of 3422 images,divided into four classes:healthy,fig rust,fig mosaic,and anthracnose.These diseases can significantly reduce the yield and quality of fig tree fruit.The objective of this research is to develop a CNN that can identify and categorize diseases in fig tree leaves.The data for this study was collected from gardens in the Amandi and Mamash Khail Bannu districts of the Khyber Pakhtunkhwa region in Pakistan.To minimize the risk of overfitting and enhance the model’s performance,early stopping techniques and data augmentation were employed.As a result,the model achieved a training accuracy of 91.53%and a validation accuracy of 90.12%,which are considered respectable.This comprehensive model assists farmers in the early identification and categorization of fig tree leaf diseases.Our experts believe that CNNs could serve as valuable tools for accurate disease classification and detection in precision agriculture.We recommend further research to explore additional data sources and more advanced neural networks to improve the model’s accuracy and applicability.Future research will focus on expanding the dataset by including new diseases and testing the model in real-world scenarios to enhance sustainable farming practices.展开更多
Among all the plagues threatening cocoa cultivation in general, and particularly in West Africa, the swollen shoot viral disease is currently the most dangerous. The greatest challenge in the fight to eradicate this p...Among all the plagues threatening cocoa cultivation in general, and particularly in West Africa, the swollen shoot viral disease is currently the most dangerous. The greatest challenge in the fight to eradicate this pandemic remains its early detection. Traditional methods of swollen shoot detection are mostly based on visual observations, leading to late detection and/or diagnostic errors. The use of machine learning algorithms is now an alternative for effective plant disease detection. It is therefore crucial to provide efficient solutions to farmers’ cooperatives. In our study, we built a database of healthy and diseased cocoa leaves. We then explored the power of feature extractors based on convolutional neural networks such as VGG 19, Inception V3, DenseNet 201, and a custom CNN, combining their strengths with the XGBOOST classifier. The results of our experiments showed that this fusion of methods with XGBOOST yielded highly promising scores, outperforming the results of algorithms using the sigmoid function. These results were further consolidated by the use of evaluation metrics such as accuracy, mean squared error, F score, recall, and Matthews’s correlation coefficient. The proposed approach, combining state of the art feature extractors and the XGBOOST classifier, offers an efficient and reliable solution for the early detection of swollen shoot. Its implementation could significantly assist West African cocoa farmers in combating this devastating disease and preserving their crops.展开更多
In India’s economy, agriculture has been the most significantcontributor. Despite the fact that agriculture’s contribution is decreasing asthe world’s population grows, it continues to be the most important sourceo...In India’s economy, agriculture has been the most significantcontributor. Despite the fact that agriculture’s contribution is decreasing asthe world’s population grows, it continues to be the most important sourceof employment with a little margin of difference. As a result, there is apressing need to pick up the pace in order to achieve competitive, productive,diverse, and long-term agriculture. Plant disease misinterpretations can resultin the incorrect application of pesticides, causing crop harm. As a result,early detection of infections is critical as well as cost-effective for farmers.To diagnose the disease at an earlier stage, appropriate segmentation of thediseased component from the leaf in an accurate manner is critical. However,due to the existence of noise in the digitally captured image, as well asvariations in backdrop, shape, and brightness in sick photographs, effectiverecognition has become a difficult task. Leaf smut, Bacterial blight andBrown spot diseases are segmented and classified using diseased Apple (20),Cercospora (60), Rice (100), Grape (140), and wheat (180) leaf photos in thesuggested work. In addition, a superior segmentation technique for the ROIfrom sick leaves with living backdrop is presented here. Textural features of thesegmented ROI, such as 1st and 2nd order WPCA Features, are discoveredafter segmentation. This comprises 1st order textural features like kurtosis,skewness, mean and variance as well as 2nd procedure textural features likesmoothness, energy, correlation, homogeneity, contrast, and entropy. Finally,the segmented region of interest’s textural features is fed into four differentclassifiers, with the Enhanced Deep Convolutional Neural Network provingto be the most precise, with a 96.1% accuracy.展开更多
Tomato leaf diseases significantly reduce crop yield;therefore,early and accurate disease detection is required.Traditional detection methods are laborious and error-prone,particularly in large-scale farms,whereas exi...Tomato leaf diseases significantly reduce crop yield;therefore,early and accurate disease detection is required.Traditional detection methods are laborious and error-prone,particularly in large-scale farms,whereas existing hybrid deep learning models often face computational inefficiencies and poor generalization over diverse environmental and disease conditions.This study presents a unified U-Net-Vision Mamba Model with Hierarchical Bottleneck AttentionMechanism(U-net-Vim-HBAM),which integrates U-Net’s high-resolution segmentation,Vision Mamba’s efficient contextual processing,and a Hierarchical Bottleneck Attention Mechanism to address the challenges of disease detection accuracy,computational complexity,and efficiency in existing models.The model was trained on the Tomato Leaves and PlantVillage combined datasets from Kaggle and achieved 98.63% accuracy,98.24% precision,96.41% recall,and 97.31%F1 score,outperforming baselinemodels.Simulation tests demonstrated the model’s compatibility across devices with computational efficacy,ensuring its potential for integration into real-time mobile agricultural applications.The model’s adaptability to diverse datasets and conditions suggests that it is a versatile and high-precision instrument for disease management in agriculture,supporting sustainable agricultural practices.This offers a promising solution for crop health management and contributes to food security.展开更多
The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and quality.Misdiagnosis by the farmers poses the risk of inadequate treatments,harming both toma...The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and quality.Misdiagnosis by the farmers poses the risk of inadequate treatments,harming both tomato plants and agroecosystems.Precision of disease diagnosis is essential,necessitating a swift and accurate response to misdiagnosis for early identification.Tropical regions are ideal for tomato plants,but there are inherent concerns,such as weather-related problems.Plant diseases largely cause financial losses in crop production.The slow detection periods of conventional approaches are insufficient for the timely detection of tomato diseases.Deep learning has emerged as a promising avenue for early disease identification.This study comprehensively analyzed techniques for classifying and detecting tomato leaf diseases and evaluating their strengths and weaknesses.The study delves into various diagnostic procedures,including image pre-processing,localization and segmentation.In conclusion,applying deep learning algorithms holds great promise for enhancing the accuracy and efficiency of tomato leaf disease diagnosis by offering faster and more effective results.展开更多
Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry.The objective of this research was to p...Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry.The objective of this research was to propose a simple and efficient approach to improve grape leaf disease identification accuracy with limited computing resources and scale of training image dataset based on deep transfer learning and an improved MobileNetV3 model(GLD-DTL).A pre-training model was obtained by training MobileNetV3 using the ImageNet dataset to extract common features of the grape leaves.And the last convolution layer of the pre-training model was modified by adding a batch normalization function.A dropout layer followed by a fully connected layer was used to improve the generalization ability of the pre-training model and realize a weight matrix to quantify the scores of six diseases,according to which the Softmax method was added as the top layer of the modified networks to give probability distribution of six diseases.Finally,the grape leaf diseases dataset,which was constructed by processing the image with data augmentation and image annotation technologies,was input into the modified networks to retrain the networks to obtain the grape leaf diseases recognition(GLDR)model.Results showed that the proposed GLD-DTL approach had better performance than some recent approaches.The identification accuracy was as high as 99.84%while the model size was as small as 30 MB.展开更多
Grape diseases are main factors causing serious grapes reduction.So it is urgent to develop an automatic identification method for grape leaf diseases.Deep learning techniques have recently achieved impressive success...Grape diseases are main factors causing serious grapes reduction.So it is urgent to develop an automatic identification method for grape leaf diseases.Deep learning techniques have recently achieved impressive successes in various computer vision problems,which inspires us to apply them to grape diseases identification task.In this paper,a united convolutional neural networks(CNNs)architecture based on an integrated method is proposed.The proposed CNNs architecture,i.e.,UnitedModel is designed to distinguish leaves with common grape diseases i.e.,black rot,esca and isariopsis leaf spot from healthy leaves.The combination of multiple CNNs enables the proposed UnitedModel to extract complementary discriminative features.Thus the representative ability of United-Model has been enhanced.The UnitedModel has been evaluated on the hold-out PlantVillage dataset and has been compared with several state-of-the-art CNN models.The experimental results have shown that UnitedModel achieves the best performance on various evaluation metrics.The UnitedModel achieves an average validation accuracy of 99.17%and a test accuracy of 98.57%,which can serve as a decision support tool to help farmers identify grape diseases.展开更多
In the agriculture field,one of the recent research topics is recognition and classification of diseases from the leaf images of a plant.The recognition of agricultural plant diseases by utilizing the image processing...In the agriculture field,one of the recent research topics is recognition and classification of diseases from the leaf images of a plant.The recognition of agricultural plant diseases by utilizing the image processing techniques will minimize the reliance on the farmers to protect the agricultural products.In this paper,Recognition and Classification of Paddy Leaf Diseases using Optimized Deep Neural Network with Jaya Algorithm is proposed.For the image acquisition the images of rice plant leaves are directly captured from the farm field for normal,bacterial blight,brown spot,sheath rot and blast diseases.In pre-processing,for the background removal the RGB images are converted into HSV images and based on the hue and saturation parts binary images are extracted to split the diseased and non-diseased part.For the segmentation of diseased portion,normal portion and background a clustering method is used.Classification of diseases is carried out by using Optimized Deep Neural Network with Jaya Optimization Algorithm(DNN_JOA).In order to precise the stability of this approach a feedback loop is generated in the post processing step.The experimental results are evaluated and compared with ANN,DAE and DNN.The proposed method achieved high accuracy of 98.9%for the blast affected,95.78%for the bacterial blight,92%for the sheath rot,94%for the brown spot and 90.57%for the normal leaf image.展开更多
Sun flower(Helianthus annuus L.)is one of the important oil seed crops and potentially fit in agricultural system and oil production sector of India.Sunflower crop gets damaged by the impact of various diseases,insect...Sun flower(Helianthus annuus L.)is one of the important oil seed crops and potentially fit in agricultural system and oil production sector of India.Sunflower crop gets damaged by the impact of various diseases,insects and nematodes resulting in wide range of loss in production.Disease detection is possible through naked eye observation,but this method is unsuccessful when one has to monitor the large farms.As a solution to this problem,we developed and present a system for segmentation and classification of Sunflower leaf images.This research paper presents surveys conducted on different diseases classification techniques that can be used for sunflower leaf disease detection.Segmentation of Sunflower leaf images,which is an important aspect for disease classification,is done by using Particle swarm optimization algorithm.Satisfactory results have been given by the experiments done on leaf images.The average accuracy of classification of proposed algorithm is 98.0%compared to 97.6 and 92.7%reported in state-of-the-art methods.展开更多
In order to realize the intelligent identification of maize leaf diseases for accurate prevention and control,this study proposed a maize disease detection method based on improved MobileNet V3-small,using a UAV to co...In order to realize the intelligent identification of maize leaf diseases for accurate prevention and control,this study proposed a maize disease detection method based on improved MobileNet V3-small,using a UAV to collect maize disease images and establish a maize disease dataset in a complex context,and explored the effects of data expansion and migration learning on model recognition accuracy,recall rate,and F1-score instructive evaluative indexes,and the results show that the two approaches of data expansion and migration learning effectively improved the accuracy of the model.The structured compression of MobileNet V3-small bneck layer retains only 6 layers,the expansion multiplier of each layer was redesigned,32-fold fast downsampling was used in the first layer,and the location of the SE module was optimized.The improved model had an average accuracy of 79.52%in the test set,a recall of 77.91%,an F1-score of 78.62%,a model size of 2.36 MB,and a single image detection speed of 9.02 ms.The detection accuracy and speed of the model can meet the requirements of mobile or embedded devices.This study provides technical support for realizing the intelligent detection of maize leaf diseases.展开更多
The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non...The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.展开更多
Leaf disease recognition using image processing and deep learning techniques is currently a vibrant research area.Most studies have focused on recognizing diseases from images of whole leaves.This approach limits the ...Leaf disease recognition using image processing and deep learning techniques is currently a vibrant research area.Most studies have focused on recognizing diseases from images of whole leaves.This approach limits the resulting models’ability to estimate leaf disease severity or identify multiple anomalies occurring on the same leaf.Recent studies have demonstrated that classifying leaf diseases based on individual lesions greatly enhances disease recognition accuracy.In those studies,however,the lesions were laboriously cropped by hand.This study proposes a semi-automatic algorithm that facilitates the fast and efficient preparation of datasets of individual lesions and leaf image pixel maps to overcome this problem.These datasets were then used to train and test lesion classifier and semantic segmentation Convolutional Neural Network(CNN)models,respectively.We report that GoogLeNet’s disease recognition accuracy improved by more than 15%when diseases were recognized from lesion images compared to when disease recognition was done using images of whole leaves.A CNN model which performs semantic segmentation of both the leaf and lesions in one pass is also proposed in this paper.The proposed KijaniNet model achieved state-of-the-art segmentation performance in terms of mean Intersection over Union(mIoU)score of 0.8448 and 0.6257 for the leaf and lesion pixel classes,respectively.In terms of mean boundary F1 score,the KijaniNet model attained 0.8241 and 0.7855 for the two pixel classes,respectively.Lastly,a fully automatic algorithm for leaf disease recognition from individual lesions is proposed.The algorithm employs the semantic segmentation network cascaded to a GoogLeNet classifier for lesion-wise disease recognition.The proposed fully automatic algorithm outperforms competing methods in terms of its superior segmentation and classification performance despite being trained on a small dataset.展开更多
Selecting techniques is a crucial aspect of disease detection analysis,particularly in the convergence of computer vision and agricultural technology.Maintaining crop disease detection in a timely and accurate manner ...Selecting techniques is a crucial aspect of disease detection analysis,particularly in the convergence of computer vision and agricultural technology.Maintaining crop disease detection in a timely and accurate manner is essential to maintaining global food security.Deep learning is a viable answer to meet this need.To proceed with this study,we have developed and evaluated a disease detection model using a novel ensemble technique.We propose to introduce DenseNetMini,a smaller version of DenseNet.We propose combining DenseNetMini with a learning resizer in ensemble approach to enhance training accuracy and expedite learning.Another unique proposition involves utilizing Gradient Product(GP)as an optimization technique,effectively reducing the training time and improving the model performance.Examining images at different magnifications reveals noteworthy diagnostic agreement and accuracy improvements.Test accuracy rates of 99.65%,98.96%,and 98.11%are seen in the Plantvillage,Tomato leaf,and Appleleaf9 datasets,respectively.One of the research's main achievements is the significant decrease in processing time,which suggests that using the GP could improve disease detection in agriculture's accessibility and efficiency.Beyond quantitative successes,the study highlights Explainable Artificial Intelligence(XAl)methods,which are essential to improving the disease detection model's interpretability and transparency.XAI enhances the interpretability of the model by visually identifying critical areas on plant leaves for disease identification,which promotes confidence and understanding of the model's functionality.展开更多
Aiming at the problem of low detection accuracy due to the different scale sizes of apple leaf disease spots and their similarity to the background,this paper proposes a multi-scale lightweight network(MSL-Net).Firstl...Aiming at the problem of low detection accuracy due to the different scale sizes of apple leaf disease spots and their similarity to the background,this paper proposes a multi-scale lightweight network(MSL-Net).Firstly,a multiplexed aggregated feature extraction network is proposed using residual bottleneck block(RES-Bottleneck)and middle partial-convolution(MP-Conv)to capture multi-scale spatial features and enhance focus on disease features for better differentiation between disease targets and background information.Secondly,a lightweight feature fusion network is designed using scale-fuse concatenation(SF-Cat)and triple-scale sequence feature fusion(TSSF)module to merge multi-scale feature maps comprehensively.Depthwise convolution(DWConv)and GhostNet lighten the network,while the cross stage partial bottleneck with 3 convolutions ghost-normalization attention module(C3-GN)reduces missed detections by suppressing irrelevant background information.Finally,soft non-maximum suppression(Soft-NMS)is used in the post-processing stage to improve the problem of misdetection of dense disease sites.The results show that the MSL-Net improves mean average precision at intersection over union of 0.5(mAP@0.5)by 2.0%over the baseline you only look once version 5s(YOLOv5s)and reduces parameters by 44%,reducing computation by 27%,outperforming other state-of-the-art(SOTA)models overall.This method also shows excellent performance compared to the latest research.展开更多
Turmeric Leaf diseases pose a major threat to turmeric cultivation,causing significant yield loss and economic impact.Early and accurate identification of these diseases is essential for effective crop management and ...Turmeric Leaf diseases pose a major threat to turmeric cultivation,causing significant yield loss and economic impact.Early and accurate identification of these diseases is essential for effective crop management and timely intervention.This study proposes DenseSwinGNNNet,a hybrid deep learning framework that integrates DenseNet-121,the Swin Transformer,and a Graph Neural Network(GNN)to enhance the classification of turmeric leaf conditions.DenseNet121 extracts discriminative low-level features,the Swin Transformer captures long-range contextual relationships through hierarchical self-attention,and the GNN models inter-feature dependencies to refine the final representation.A total of 4361 images from the Mendeley turmeric leaf dataset were used,categorized into four classes:Aphids Disease,Blotch,Leaf Spot,and Healthy Leaf.The dataset underwent extensive preprocessing,including augmentation,normalization,and resizing,to improve generalization.An 80:10:10 split was applied for training,validation,and testing respectively.Model performance was evaluated using accuracy,precision,recall,F1-score,confusion matrices,and ROC curves.Optimized with the Adam optimizer at the learning rate of 0.0001,DenseSwinGNNNet achieved an overall accuracy of 99.7%,with precision,recall,and F1-scores exceeding 99%across all classes.The ROC curves reported AUC values near 1.0,indicating excellent class separability,while the confusion matrix showed minimal misclassification.Beyond high predictive performance,the framework incorporates considerations for cybersecurity and privacy in data-driven agriculture,supporting secure data handling and robust model deployment.This work contributes a reliable and scalable approach for turmeric leaf disease detection and advances the application of AI-driven precision agriculture.展开更多
As nearly half of the people in the world live on rice, so the rice leaf disease detection is very important for our agricultural sector. Many researchers worked on this problem and they achieved different results acc...As nearly half of the people in the world live on rice, so the rice leaf disease detection is very important for our agricultural sector. Many researchers worked on this problem and they achieved different results according to their applied techniques. In this paper, we applied AlexNet technique to detect the three prevalence rice leaf diseases termed as bacterial blight, brown spot as well as leaf smut and got a remarkable outcome rather than the previous works. AlexNet is a special type of classification technique of deep learning. This paper shows more than 99% accuracy due to adjusting an efficient technique and image augmentation.展开更多
In the field of agricultural information,the identification and prediction of rice leaf disease have always been the focus of research,and deep learning(DL)technology is currently a hot research topic in the field of ...In the field of agricultural information,the identification and prediction of rice leaf disease have always been the focus of research,and deep learning(DL)technology is currently a hot research topic in the field of pattern recognition.The research and development of high-efficiency,highquality and low-cost automatic identification methods for rice diseases that can replace humans is an important means of dealing with the current situation from a technical perspective.This paper mainly focuses on the problem of huge parameters of the Convolutional Neural Network(CNN)model and proposes a recognitionmodel that combines amulti-scale convolution module with a neural network model based on Visual Geometry Group(VGG).The accuracy and loss of the training set and the test set are used to evaluate the performance of the model.The test accuracy of this model is 97.1%that has increased 5.87%over VGG.Furthermore,the memory requirement is 26.1M,only 1.6%of the VGG.Experiment results show that this model performs better in terms of accuracy,recognition speed and memory size.展开更多
[Objective] The study aimed to investigate the Ioquat leaf mould disease in Mengzi City of Yunnan Province and lay the foundation for determination of effective prevention and control methods.[Method] Loquat leaf moul...[Objective] The study aimed to investigate the Ioquat leaf mould disease in Mengzi City of Yunnan Province and lay the foundation for determination of effective prevention and control methods.[Method] Loquat leaf mould pathogenic fungus was isolated by tissue separation method and inoculated with conidial suspension.The pathogenicity of Ioquat leaf mould pathogen was verified by Koch＇s postulate.Under a microscope,mycelial morphology and conidial fructification were observed,spore sizes were measured,and Ioquat leaf mould pathogen was identified according to the morphological characteristics.[Result] Velvet-like,olive green fungal colonies were generated on PDA medium.Conidiophores erect,apex curved,dark brown,smooth,with obvious spore marks and no diaphragm,（33.0-152.8） μm×（2.6-4.0）μm.Cladosporium was brown or pale olive with spore marks,monocelled or with one diaphragm,（7.1-19.0） μm × （1.9-5.9） μm.Conidia concatenate （2-4）,oval or spherical,with no spore mark,light olive,monocelled,smooth,（2.1-9.4） μm × （1.2-2.6） μm.[Conclusion] Loquat leaf mold disease began to occur in the germination period of spring shoots and summer shoots and became serious in the germination period of autumn shoots.Sooty mold-like layer grew on both front and back surfaces and densely covered the whole leaves,thus seriously affecting the photosynthesis of plants.The pathogen was preliminarily identified as Cladosporium eriobotryae Pass.＆ Beltrani.展开更多
Banana is a significant crop,and three banana leaf diseases,including Sigatoka,Cordana and Pestalotiopsis,have the potential to have a serious impact on banana production.Existing studies are insufficient to provide a...Banana is a significant crop,and three banana leaf diseases,including Sigatoka,Cordana and Pestalotiopsis,have the potential to have a serious impact on banana production.Existing studies are insufficient to provide a reliable method for accurately identifying banana leaf diseases.Therefore,this paper proposes a novel method to identify banana leaf diseases.First,a new algorithm called K-scale VisuShrink algorithm(KVA)is proposed to denoise banana leaf images.The proposed algorithm introduces a new decomposition scale K based on the semi-soft and middle course thresholds,the ideal threshold solution is obtained and substituted with the newly established threshold function to obtain a less noisy banana leaf image.Then,this paper proposes a novel network for image identification called Ghost ResNeSt-Attention RReLU-Swish Net(GR-ARNet)based on Resnet50.In this,the Ghost Module is implemented to improve the network's effectiveness in extracting deep feature information on banana leaf diseases and the identification speed;the ResNeSt Module adjusts the weight of each channel,increasing the ability of banana disease feature extraction and effectively reducing the error rate of similar disease identification;the model's computational speed is increased using the hybrid activation function of RReLU and Swish.Our model achieves an average accuracy of 96.98%and a precision of 89.31%applied to 13,021 images,demonstrating that the proposed method can effectively identify banana leaf diseases.展开更多
Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses.Early detection of these diseases is essential for effective management.We propose a novel transformed wa...Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses.Early detection of these diseases is essential for effective management.We propose a novel transformed wavelet,feature-fused,pre-trained deep learning model for detecting olive leaf diseases.The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images.The model has four main phases:preprocessing using data augmentation,three-level wavelet transformation,learning using pre-trained deep learning models,and a fused deep learning model.In the preprocessing phase,the image dataset is augmented using techniques such as resizing,rescaling,flipping,rotation,zooming,and contrasting.In wavelet transformation,the augmented images are decomposed into three frequency levels.Three pre-trained deep learning models,EfficientNet-B7,DenseNet-201,and ResNet-152-V2,are used in the learning phase.The models were trained using the approximate images of the third-level sub-band of the wavelet transform.In the fused phase,the fused model consists of a merge layer,three dense layers,and two dropout layers.The proposed model was evaluated using a dataset of images of healthy and infected olive leaves.It achieved an accuracy of 99.72%in the diagnosis of olive leaf diseases,which exceeds the accuracy of other methods reported in the literature.This finding suggests that our proposed method is a promising tool for the early detection of olive leaf diseases.展开更多
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘Leaf disease identification is one of the most promising applications of convolutional neural networks(CNNs).This method represents a significant step towards revolutionizing agriculture by enabling the quick and accurate assessment of plant health.In this study,a CNN model was specifically designed and tested to detect and categorize diseases on fig tree leaves.The researchers utilized a dataset of 3422 images,divided into four classes:healthy,fig rust,fig mosaic,and anthracnose.These diseases can significantly reduce the yield and quality of fig tree fruit.The objective of this research is to develop a CNN that can identify and categorize diseases in fig tree leaves.The data for this study was collected from gardens in the Amandi and Mamash Khail Bannu districts of the Khyber Pakhtunkhwa region in Pakistan.To minimize the risk of overfitting and enhance the model’s performance,early stopping techniques and data augmentation were employed.As a result,the model achieved a training accuracy of 91.53%and a validation accuracy of 90.12%,which are considered respectable.This comprehensive model assists farmers in the early identification and categorization of fig tree leaf diseases.Our experts believe that CNNs could serve as valuable tools for accurate disease classification and detection in precision agriculture.We recommend further research to explore additional data sources and more advanced neural networks to improve the model’s accuracy and applicability.Future research will focus on expanding the dataset by including new diseases and testing the model in real-world scenarios to enhance sustainable farming practices.
文摘Among all the plagues threatening cocoa cultivation in general, and particularly in West Africa, the swollen shoot viral disease is currently the most dangerous. The greatest challenge in the fight to eradicate this pandemic remains its early detection. Traditional methods of swollen shoot detection are mostly based on visual observations, leading to late detection and/or diagnostic errors. The use of machine learning algorithms is now an alternative for effective plant disease detection. It is therefore crucial to provide efficient solutions to farmers’ cooperatives. In our study, we built a database of healthy and diseased cocoa leaves. We then explored the power of feature extractors based on convolutional neural networks such as VGG 19, Inception V3, DenseNet 201, and a custom CNN, combining their strengths with the XGBOOST classifier. The results of our experiments showed that this fusion of methods with XGBOOST yielded highly promising scores, outperforming the results of algorithms using the sigmoid function. These results were further consolidated by the use of evaluation metrics such as accuracy, mean squared error, F score, recall, and Matthews’s correlation coefficient. The proposed approach, combining state of the art feature extractors and the XGBOOST classifier, offers an efficient and reliable solution for the early detection of swollen shoot. Its implementation could significantly assist West African cocoa farmers in combating this devastating disease and preserving their crops.
文摘In India’s economy, agriculture has been the most significantcontributor. Despite the fact that agriculture’s contribution is decreasing asthe world’s population grows, it continues to be the most important sourceof employment with a little margin of difference. As a result, there is apressing need to pick up the pace in order to achieve competitive, productive,diverse, and long-term agriculture. Plant disease misinterpretations can resultin the incorrect application of pesticides, causing crop harm. As a result,early detection of infections is critical as well as cost-effective for farmers.To diagnose the disease at an earlier stage, appropriate segmentation of thediseased component from the leaf in an accurate manner is critical. However,due to the existence of noise in the digitally captured image, as well asvariations in backdrop, shape, and brightness in sick photographs, effectiverecognition has become a difficult task. Leaf smut, Bacterial blight andBrown spot diseases are segmented and classified using diseased Apple (20),Cercospora (60), Rice (100), Grape (140), and wheat (180) leaf photos in thesuggested work. In addition, a superior segmentation technique for the ROIfrom sick leaves with living backdrop is presented here. Textural features of thesegmented ROI, such as 1st and 2nd order WPCA Features, are discoveredafter segmentation. This comprises 1st order textural features like kurtosis,skewness, mean and variance as well as 2nd procedure textural features likesmoothness, energy, correlation, homogeneity, contrast, and entropy. Finally,the segmented region of interest’s textural features is fed into four differentclassifiers, with the Enhanced Deep Convolutional Neural Network provingto be the most precise, with a 96.1% accuracy.
文摘Tomato leaf diseases significantly reduce crop yield;therefore,early and accurate disease detection is required.Traditional detection methods are laborious and error-prone,particularly in large-scale farms,whereas existing hybrid deep learning models often face computational inefficiencies and poor generalization over diverse environmental and disease conditions.This study presents a unified U-Net-Vision Mamba Model with Hierarchical Bottleneck AttentionMechanism(U-net-Vim-HBAM),which integrates U-Net’s high-resolution segmentation,Vision Mamba’s efficient contextual processing,and a Hierarchical Bottleneck Attention Mechanism to address the challenges of disease detection accuracy,computational complexity,and efficiency in existing models.The model was trained on the Tomato Leaves and PlantVillage combined datasets from Kaggle and achieved 98.63% accuracy,98.24% precision,96.41% recall,and 97.31%F1 score,outperforming baselinemodels.Simulation tests demonstrated the model’s compatibility across devices with computational efficacy,ensuring its potential for integration into real-time mobile agricultural applications.The model’s adaptability to diverse datasets and conditions suggests that it is a versatile and high-precision instrument for disease management in agriculture,supporting sustainable agricultural practices.This offers a promising solution for crop health management and contributes to food security.
文摘The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and quality.Misdiagnosis by the farmers poses the risk of inadequate treatments,harming both tomato plants and agroecosystems.Precision of disease diagnosis is essential,necessitating a swift and accurate response to misdiagnosis for early identification.Tropical regions are ideal for tomato plants,but there are inherent concerns,such as weather-related problems.Plant diseases largely cause financial losses in crop production.The slow detection periods of conventional approaches are insufficient for the timely detection of tomato diseases.Deep learning has emerged as a promising avenue for early disease identification.This study comprehensively analyzed techniques for classifying and detecting tomato leaf diseases and evaluating their strengths and weaknesses.The study delves into various diagnostic procedures,including image pre-processing,localization and segmentation.In conclusion,applying deep learning algorithms holds great promise for enhancing the accuracy and efficiency of tomato leaf disease diagnosis by offering faster and more effective results.
基金The authors acknowledge that this work was financially supported by the National Natural Science Foundation of China(Grant No.32171910)the Natural Science Foundation of Shandong Province(Grant No.ZR2020MC085)+1 种基金the Key R&D Project of Shandong Province(Grant No.2019JZZY010734)the Key R&D Project of Zibo City,Shandong Province,China(Grant No.2019ZBXC143).
文摘Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry.The objective of this research was to propose a simple and efficient approach to improve grape leaf disease identification accuracy with limited computing resources and scale of training image dataset based on deep transfer learning and an improved MobileNetV3 model(GLD-DTL).A pre-training model was obtained by training MobileNetV3 using the ImageNet dataset to extract common features of the grape leaves.And the last convolution layer of the pre-training model was modified by adding a batch normalization function.A dropout layer followed by a fully connected layer was used to improve the generalization ability of the pre-training model and realize a weight matrix to quantify the scores of six diseases,according to which the Softmax method was added as the top layer of the modified networks to give probability distribution of six diseases.Finally,the grape leaf diseases dataset,which was constructed by processing the image with data augmentation and image annotation technologies,was input into the modified networks to retrain the networks to obtain the grape leaf diseases recognition(GLDR)model.Results showed that the proposed GLD-DTL approach had better performance than some recent approaches.The identification accuracy was as high as 99.84%while the model size was as small as 30 MB.
基金This work was supported by the PublicWelfare Industry(Agriculture)Research Projects Level-2 under Grant 201503116-04-06Postdoctoral Foundation of Heilongjiang Province under Grant LBHZ15020Harbin Applied Technology Research and Development Program under Grant 2017RAQXJ096 and National Key Application Research and Development Program in China under Grant 2018YFD0300105-2.
文摘Grape diseases are main factors causing serious grapes reduction.So it is urgent to develop an automatic identification method for grape leaf diseases.Deep learning techniques have recently achieved impressive successes in various computer vision problems,which inspires us to apply them to grape diseases identification task.In this paper,a united convolutional neural networks(CNNs)architecture based on an integrated method is proposed.The proposed CNNs architecture,i.e.,UnitedModel is designed to distinguish leaves with common grape diseases i.e.,black rot,esca and isariopsis leaf spot from healthy leaves.The combination of multiple CNNs enables the proposed UnitedModel to extract complementary discriminative features.Thus the representative ability of United-Model has been enhanced.The UnitedModel has been evaluated on the hold-out PlantVillage dataset and has been compared with several state-of-the-art CNN models.The experimental results have shown that UnitedModel achieves the best performance on various evaluation metrics.The UnitedModel achieves an average validation accuracy of 99.17%and a test accuracy of 98.57%,which can serve as a decision support tool to help farmers identify grape diseases.
文摘In the agriculture field,one of the recent research topics is recognition and classification of diseases from the leaf images of a plant.The recognition of agricultural plant diseases by utilizing the image processing techniques will minimize the reliance on the farmers to protect the agricultural products.In this paper,Recognition and Classification of Paddy Leaf Diseases using Optimized Deep Neural Network with Jaya Algorithm is proposed.For the image acquisition the images of rice plant leaves are directly captured from the farm field for normal,bacterial blight,brown spot,sheath rot and blast diseases.In pre-processing,for the background removal the RGB images are converted into HSV images and based on the hue and saturation parts binary images are extracted to split the diseased and non-diseased part.For the segmentation of diseased portion,normal portion and background a clustering method is used.Classification of diseases is carried out by using Optimized Deep Neural Network with Jaya Optimization Algorithm(DNN_JOA).In order to precise the stability of this approach a feedback loop is generated in the post processing step.The experimental results are evaluated and compared with ANN,DAE and DNN.The proposed method achieved high accuracy of 98.9%for the blast affected,95.78%for the bacterial blight,92%for the sheath rot,94%for the brown spot and 90.57%for the normal leaf image.
文摘Sun flower(Helianthus annuus L.)is one of the important oil seed crops and potentially fit in agricultural system and oil production sector of India.Sunflower crop gets damaged by the impact of various diseases,insects and nematodes resulting in wide range of loss in production.Disease detection is possible through naked eye observation,but this method is unsuccessful when one has to monitor the large farms.As a solution to this problem,we developed and present a system for segmentation and classification of Sunflower leaf images.This research paper presents surveys conducted on different diseases classification techniques that can be used for sunflower leaf disease detection.Segmentation of Sunflower leaf images,which is an important aspect for disease classification,is done by using Particle swarm optimization algorithm.Satisfactory results have been given by the experiments done on leaf images.The average accuracy of classification of proposed algorithm is 98.0%compared to 97.6 and 92.7%reported in state-of-the-art methods.
基金This study was supported by the Fruit Industry Innovation Team Project of the Modern Agricultural Industry Technology System of Shandong Province(SDAIT-06-12)the“Double First-class”Award and subsidy fund project of Shandong Agricultural University(SYL2017X).
文摘In order to realize the intelligent identification of maize leaf diseases for accurate prevention and control,this study proposed a maize disease detection method based on improved MobileNet V3-small,using a UAV to collect maize disease images and establish a maize disease dataset in a complex context,and explored the effects of data expansion and migration learning on model recognition accuracy,recall rate,and F1-score instructive evaluative indexes,and the results show that the two approaches of data expansion and migration learning effectively improved the accuracy of the model.The structured compression of MobileNet V3-small bneck layer retains only 6 layers,the expansion multiplier of each layer was redesigned,32-fold fast downsampling was used in the first layer,and the location of the SE module was optimized.The improved model had an average accuracy of 79.52%in the test set,a recall of 77.91%,an F1-score of 78.62%,a model size of 2.36 MB,and a single image detection speed of 9.02 ms.The detection accuracy and speed of the model can meet the requirements of mobile or embedded devices.This study provides technical support for realizing the intelligent detection of maize leaf diseases.
基金financially supported by the Deanship of Scientific Research,Qassim University,Saudi Arabia for funding the publication of this project.
文摘The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.
文摘Leaf disease recognition using image processing and deep learning techniques is currently a vibrant research area.Most studies have focused on recognizing diseases from images of whole leaves.This approach limits the resulting models’ability to estimate leaf disease severity or identify multiple anomalies occurring on the same leaf.Recent studies have demonstrated that classifying leaf diseases based on individual lesions greatly enhances disease recognition accuracy.In those studies,however,the lesions were laboriously cropped by hand.This study proposes a semi-automatic algorithm that facilitates the fast and efficient preparation of datasets of individual lesions and leaf image pixel maps to overcome this problem.These datasets were then used to train and test lesion classifier and semantic segmentation Convolutional Neural Network(CNN)models,respectively.We report that GoogLeNet’s disease recognition accuracy improved by more than 15%when diseases were recognized from lesion images compared to when disease recognition was done using images of whole leaves.A CNN model which performs semantic segmentation of both the leaf and lesions in one pass is also proposed in this paper.The proposed KijaniNet model achieved state-of-the-art segmentation performance in terms of mean Intersection over Union(mIoU)score of 0.8448 and 0.6257 for the leaf and lesion pixel classes,respectively.In terms of mean boundary F1 score,the KijaniNet model attained 0.8241 and 0.7855 for the two pixel classes,respectively.Lastly,a fully automatic algorithm for leaf disease recognition from individual lesions is proposed.The algorithm employs the semantic segmentation network cascaded to a GoogLeNet classifier for lesion-wise disease recognition.The proposed fully automatic algorithm outperforms competing methods in terms of its superior segmentation and classification performance despite being trained on a small dataset.
文摘Selecting techniques is a crucial aspect of disease detection analysis,particularly in the convergence of computer vision and agricultural technology.Maintaining crop disease detection in a timely and accurate manner is essential to maintaining global food security.Deep learning is a viable answer to meet this need.To proceed with this study,we have developed and evaluated a disease detection model using a novel ensemble technique.We propose to introduce DenseNetMini,a smaller version of DenseNet.We propose combining DenseNetMini with a learning resizer in ensemble approach to enhance training accuracy and expedite learning.Another unique proposition involves utilizing Gradient Product(GP)as an optimization technique,effectively reducing the training time and improving the model performance.Examining images at different magnifications reveals noteworthy diagnostic agreement and accuracy improvements.Test accuracy rates of 99.65%,98.96%,and 98.11%are seen in the Plantvillage,Tomato leaf,and Appleleaf9 datasets,respectively.One of the research's main achievements is the significant decrease in processing time,which suggests that using the GP could improve disease detection in agriculture's accessibility and efficiency.Beyond quantitative successes,the study highlights Explainable Artificial Intelligence(XAl)methods,which are essential to improving the disease detection model's interpretability and transparency.XAI enhances the interpretability of the model by visually identifying critical areas on plant leaves for disease identification,which promotes confidence and understanding of the model's functionality.
文摘Aiming at the problem of low detection accuracy due to the different scale sizes of apple leaf disease spots and their similarity to the background,this paper proposes a multi-scale lightweight network(MSL-Net).Firstly,a multiplexed aggregated feature extraction network is proposed using residual bottleneck block(RES-Bottleneck)and middle partial-convolution(MP-Conv)to capture multi-scale spatial features and enhance focus on disease features for better differentiation between disease targets and background information.Secondly,a lightweight feature fusion network is designed using scale-fuse concatenation(SF-Cat)and triple-scale sequence feature fusion(TSSF)module to merge multi-scale feature maps comprehensively.Depthwise convolution(DWConv)and GhostNet lighten the network,while the cross stage partial bottleneck with 3 convolutions ghost-normalization attention module(C3-GN)reduces missed detections by suppressing irrelevant background information.Finally,soft non-maximum suppression(Soft-NMS)is used in the post-processing stage to improve the problem of misdetection of dense disease sites.The results show that the MSL-Net improves mean average precision at intersection over union of 0.5(mAP@0.5)by 2.0%over the baseline you only look once version 5s(YOLOv5s)and reduces parameters by 44%,reducing computation by 27%,outperforming other state-of-the-art(SOTA)models overall.This method also shows excellent performance compared to the latest research.
基金supported through the Ongoing Research Funding Program(ORF-2025-498)King Saud University,Riyadh,Saudi Arabia。
文摘Turmeric Leaf diseases pose a major threat to turmeric cultivation,causing significant yield loss and economic impact.Early and accurate identification of these diseases is essential for effective crop management and timely intervention.This study proposes DenseSwinGNNNet,a hybrid deep learning framework that integrates DenseNet-121,the Swin Transformer,and a Graph Neural Network(GNN)to enhance the classification of turmeric leaf conditions.DenseNet121 extracts discriminative low-level features,the Swin Transformer captures long-range contextual relationships through hierarchical self-attention,and the GNN models inter-feature dependencies to refine the final representation.A total of 4361 images from the Mendeley turmeric leaf dataset were used,categorized into four classes:Aphids Disease,Blotch,Leaf Spot,and Healthy Leaf.The dataset underwent extensive preprocessing,including augmentation,normalization,and resizing,to improve generalization.An 80:10:10 split was applied for training,validation,and testing respectively.Model performance was evaluated using accuracy,precision,recall,F1-score,confusion matrices,and ROC curves.Optimized with the Adam optimizer at the learning rate of 0.0001,DenseSwinGNNNet achieved an overall accuracy of 99.7%,with precision,recall,and F1-scores exceeding 99%across all classes.The ROC curves reported AUC values near 1.0,indicating excellent class separability,while the confusion matrix showed minimal misclassification.Beyond high predictive performance,the framework incorporates considerations for cybersecurity and privacy in data-driven agriculture,supporting secure data handling and robust model deployment.This work contributes a reliable and scalable approach for turmeric leaf disease detection and advances the application of AI-driven precision agriculture.
文摘As nearly half of the people in the world live on rice, so the rice leaf disease detection is very important for our agricultural sector. Many researchers worked on this problem and they achieved different results according to their applied techniques. In this paper, we applied AlexNet technique to detect the three prevalence rice leaf diseases termed as bacterial blight, brown spot as well as leaf smut and got a remarkable outcome rather than the previous works. AlexNet is a special type of classification technique of deep learning. This paper shows more than 99% accuracy due to adjusting an efficient technique and image augmentation.
基金supported by National key research and development program sub-topics[2018YFF0213606-03(Mu Y.,Hu T.L.,Gong H.,Li S.J.and Sun Y.H.)http://www.most.gov.cn]Jilin Province Science and Technology Development Plan focuses on research and development projects[20200402006NC(Mu Y.,Hu T.L.,Gong H.and Li S.J.)http://kjt.jl.gov.cn]+1 种基金Science and technology support project for key industries in southern Xinjiang[2018DB001(Gong H.,and Li S.J.)http://kjj.xjbt.gov.cn]Key technology R&D project of Changchun Science and Technology Bureau of Jilin Province[21ZGN29(Mu Y.,Bao H.P.,Wang X.B.)http://kjj.changchun.gov.cn].
文摘In the field of agricultural information,the identification and prediction of rice leaf disease have always been the focus of research,and deep learning(DL)technology is currently a hot research topic in the field of pattern recognition.The research and development of high-efficiency,highquality and low-cost automatic identification methods for rice diseases that can replace humans is an important means of dealing with the current situation from a technical perspective.This paper mainly focuses on the problem of huge parameters of the Convolutional Neural Network(CNN)model and proposes a recognitionmodel that combines amulti-scale convolution module with a neural network model based on Visual Geometry Group(VGG).The accuracy and loss of the training set and the test set are used to evaluate the performance of the model.The test accuracy of this model is 97.1%that has increased 5.87%over VGG.Furthermore,the memory requirement is 26.1M,only 1.6%of the VGG.Experiment results show that this model performs better in terms of accuracy,recognition speed and memory size.
文摘[Objective] The study aimed to investigate the Ioquat leaf mould disease in Mengzi City of Yunnan Province and lay the foundation for determination of effective prevention and control methods.[Method] Loquat leaf mould pathogenic fungus was isolated by tissue separation method and inoculated with conidial suspension.The pathogenicity of Ioquat leaf mould pathogen was verified by Koch＇s postulate.Under a microscope,mycelial morphology and conidial fructification were observed,spore sizes were measured,and Ioquat leaf mould pathogen was identified according to the morphological characteristics.[Result] Velvet-like,olive green fungal colonies were generated on PDA medium.Conidiophores erect,apex curved,dark brown,smooth,with obvious spore marks and no diaphragm,（33.0-152.8） μm×（2.6-4.0）μm.Cladosporium was brown or pale olive with spore marks,monocelled or with one diaphragm,（7.1-19.0） μm × （1.9-5.9） μm.Conidia concatenate （2-4）,oval or spherical,with no spore mark,light olive,monocelled,smooth,（2.1-9.4） μm × （1.2-2.6） μm.[Conclusion] Loquat leaf mold disease began to occur in the germination period of spring shoots and summer shoots and became serious in the germination period of autumn shoots.Sooty mold-like layer grew on both front and back surfaces and densely covered the whole leaves,thus seriously affecting the photosynthesis of plants.The pathogen was preliminarily identified as Cladosporium eriobotryae Pass.＆ Beltrani.
基金supported by the Changsha Municipal Natural Science Foundation,China(kq2014160)in part by the Key Projects of Department of Education of Hunan Province,China(21A0179)+1 种基金the Hunan Key Laboratory of Intelligent Logistics Technology,China(2019TP1015)the National Natural Science Foundation of China(61902436)。
文摘Banana is a significant crop,and three banana leaf diseases,including Sigatoka,Cordana and Pestalotiopsis,have the potential to have a serious impact on banana production.Existing studies are insufficient to provide a reliable method for accurately identifying banana leaf diseases.Therefore,this paper proposes a novel method to identify banana leaf diseases.First,a new algorithm called K-scale VisuShrink algorithm(KVA)is proposed to denoise banana leaf images.The proposed algorithm introduces a new decomposition scale K based on the semi-soft and middle course thresholds,the ideal threshold solution is obtained and substituted with the newly established threshold function to obtain a less noisy banana leaf image.Then,this paper proposes a novel network for image identification called Ghost ResNeSt-Attention RReLU-Swish Net(GR-ARNet)based on Resnet50.In this,the Ghost Module is implemented to improve the network's effectiveness in extracting deep feature information on banana leaf diseases and the identification speed;the ResNeSt Module adjusts the weight of each channel,increasing the ability of banana disease feature extraction and effectively reducing the error rate of similar disease identification;the model's computational speed is increased using the hybrid activation function of RReLU and Swish.Our model achieves an average accuracy of 96.98%and a precision of 89.31%applied to 13,021 images,demonstrating that the proposed method can effectively identify banana leaf diseases.
文摘Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses.Early detection of these diseases is essential for effective management.We propose a novel transformed wavelet,feature-fused,pre-trained deep learning model for detecting olive leaf diseases.The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images.The model has four main phases:preprocessing using data augmentation,three-level wavelet transformation,learning using pre-trained deep learning models,and a fused deep learning model.In the preprocessing phase,the image dataset is augmented using techniques such as resizing,rescaling,flipping,rotation,zooming,and contrasting.In wavelet transformation,the augmented images are decomposed into three frequency levels.Three pre-trained deep learning models,EfficientNet-B7,DenseNet-201,and ResNet-152-V2,are used in the learning phase.The models were trained using the approximate images of the third-level sub-band of the wavelet transform.In the fused phase,the fused model consists of a merge layer,three dense layers,and two dropout layers.The proposed model was evaluated using a dataset of images of healthy and infected olive leaves.It achieved an accuracy of 99.72%in the diagnosis of olive leaf diseases,which exceeds the accuracy of other methods reported in the literature.This finding suggests that our proposed method is a promising tool for the early detection of olive leaf diseases.
