Leaf Disease Image Research Articles

Soybean Leaf Diseases Recognition Based on Generative Adversarial Network and Transfer Learning

Soybean leaf disease labeling data are not easy to obtain, and soybean leaf disease model training often needs a lot of data. Due to the limitations of fixed rules such as rotation and clipping, traditional data enhancement cannot generate images with diversity and variability. In view of the above problems, this study proposed a data enhancement method based on generative adversarial network to expand the original soybean leaf disease dataset. This method was based on cyclic confrontation network, and its discriminator uses dense connection strategy to realize feature reuse, so as to reduce the amount of calculation. In the training process, improved transfer learning is used to automatically fine tune the pre-training model. The accuracy of the optimized method in 9 kinds of soybean leaf disease image recognition is 95.84%, which is 0.98% higher than the traditional fine-tuning method. The experimental results show that this method based on generating confrontation network has significant ability in generating soybean leaf disease image, and can expand the existing dataset. In addition, this method also provides an effective data enhancement solution for the expansion of other crop disease image datasets.

Potato Leaf Disease Segmentation Method Based on Improved UNet

The precise control of potato diseases is an urgent demand in smart agriculture, with one of the key aspects being the accurate identification and segmentation of potato leaf diseases. Some disease spots on potato leaves are relatively small, and to address issues such as information loss and low segmentation accuracy in the process of potato leaf disease image segmentation, a novel approach based on an improved UNet network model is proposed. Firstly, the incorporation of ResNet50 as the backbone network is introduced to deepen the network structure, effectively addressing problems like gradient vanishing and degradation. Secondly, the unique characteristics of the UNet network are fully utilized, using UNet as the decoder to ingeniously integrate the characteristics of potatoes with the network. Finally, to better enable the network to learn disease spot features, the SE (squeeze and excitation) attention mechanism is introduced on top of ResNet50, further optimizing the network structure. This design allows the network to selectively emphasize useful information features and suppress irrelevant ones during the learning process, significantly enhancing the accuracy of potato disease segmentation and identification. The experimental results demonstrate that compared with the traditional UNet algorithm, the improved RS-UNet network model achieves values of 79.8% and 88.86% for the MIoU and Dice metrics, respectively, which represent improvements of 8.96% and 6.33% over UNet. These results provide strong evidence for the outstanding performance and generalization ability of the RS-UNet model in potato leaf disease spot segmentation, as well as its practical application value in the task of potato leaf disease segmentation.

Identification and localization of grape diseased leaf images captured by UAV based on CNN

In order to realize the acquisition of plant leaf images by agricultural drones and perform regional segmentation and disease recognition on leaf cluster images over a wide area, this paper takes grape leaves as an example and designs a set of processing procedures combining the improved U-net and VGG-19 networks. This paper aims to address the difficulty of obtaining target leaves for recognition in complex environments due to the presence of multiple invalid leaves. First, the feature extraction process is performed on the training and validation datasets to reduce the input parameters of the network and increase the training speed. Second, the test data is divided into three datasets: low-light, jitter interference, and two-factor combination. After recovery processing, the Multi-fusion U-net network is fed to locate diseased leaves. Finally, the improved VGG-19 network was used again to locate and identify the disease. Experimental results show that the proposed procedure achieves satisfactory performance in UAV image processing. The average accuracy of segmentation reaches 71.91%, and the identification rate of disease location is increased by 12.33% after segmentation, which provides a strong practical basis for the implementation of unmanned smart ecological farms.

Construction and verification of machine vision algorithm model based on apple leaf disease images.

Apple leaf diseases without timely control will affect fruit quality and yield, intelligent detection of apple leaf diseases was especially important. So this paper mainly focuses on apple leaf disease detection problem, proposes a machine vision algorithm model for fast apple leaf disease detection called LALNet (High-speed apple leaf network). First, an efficient sacked module for apple leaf detection, known as EALD (efficient apple leaf detection stacking module), was designed by utilizing the multi-branch structure and depth-separable modules. In the backbone network of LALNet, (High-speed apple leaf network) four layers of EALD modules were superimposed and an SE(Squeeze-and-Excitation) module was added in the last layer of the model to improve the attention of the model to important features. A structural reparameterization technique was used to combine the outputs of two layers of deeply separable convolutions in branch during the inference phase to improve the model's operational speed. The results show that in the test set, the detection accuracy of the model was 96.07%. The total precision was 95.79%, the total recall was 96.05%, the total F1 was 96.06%, the model size was 6.61 MB, and the detection speed of a single image was 6.68 ms. Therefore, the model ensures both high detection accuracy and fast execution speed, making it suitable for deployment on embedded devices. It supports precision spraying for the prevention and control of apple leaf disease.

FOTCA: hybrid transformer-CNN architecture using AFNO for accurate plant leaf disease image recognition.

Plants are widely grown around the world and have high economic benefits. plant leaf diseases not only negatively affect the healthy growth and development of plants, but also have a negative impact on the environment. While traditional manual methods of identifying plant pests and diseases are costly, inefficient and inaccurate, computer vision technologies can avoid these drawbacks and also achieve shorter control times and associated cost reductions. The focusing mechanism of Transformer-based models(such as Visual Transformer) improves image interpretability and enhances the achievements of convolutional neural network (CNN) in image recognition, but Visual Transformer(ViT) performs poorly on small and medium-sized datasets. Therefore, in this paper, we propose a new hybrid architecture named FOTCA, which uses Transformer architecture based on adaptive Fourier Neural Operators(AFNO) to extract the global features in advance, and further down sampling by convolutional kernel to extract local features in a hybrid manner. To avoid the poor performance of Transformer-based architecture on small datasets, we adopt the idea of migration learning to make the model have good scientific generalization on OOD (Out-of-Distribution) samples to improve the model's overall understanding of images. In further experiments, Focal loss and hybrid architecture can greatly improve the convergence speed and recognition accuracy of the model in ablation experiments compared with traditional models. The model proposed in this paper has the best performance with an average recognition accuracy of 99.8% and an F1-score of 0.9931. It is sufficient for deployment in plant leaf disease image recognition.

Sample Expansion and Classification Model of Maize Leaf Diseases Based on the Self-Attention CycleGAN

In order to address the limited scale and insufficient diversity of research datasets for maize leaf diseases, this study proposes a maize disease image generation algorithm based on the cycle generative adversarial network (CycleGAN). With the disease image transfer method, healthy maize images can be transformed into diseased crop images. To improve the accuracy of the generated data, the category activation mapping attention mechanism is integrated into the original CycleGAN generator and discriminator, and a feature recombination loss function is constructed in the discriminator. In addition, the minimum absolute error is used to calculate the differences between the hidden layer feature representations, and backpropagation is employed to enhance the contour information of the generated images. To demonstrate the effectiveness of this method, the improved CycleGAN algorithm is used to transform healthy maize leaf images. Evaluation metrics, such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Fréchet inception distance (FID), and grayscale histogram can prove that the obtained maize leaf disease images perform better in terms of background and detail preservation. Furthermore, using this method, the original CycleGAN method, and the Pix2Pix method, the dataset is expanded, and a recognition network is used to perform classification tasks on different datasets. The dataset generated by this method achieves the best performance in the classification tasks, with an average accuracy rate of over 91%. These experiments indicate the feasibility of this model in generating high-quality maize disease leaf images. It not only addresses the limitation of existing maize disease datasets but also improves the accuracy of maize disease recognition in small-sample maize leaf disease classification tasks.

Apple leaf disease recognition method based on Siamese dilated Inception network with less training samples

The application of existing deep learning networks may prove difficult when datasets are small. Siamese network can achieve better accuracy on small dataset. A recognition method of apple leaf disease based on Siamese dilated Inception network (SDINet) with few training samples is proposed. Dilated Inception module is introduced into AlexNet to construct two subnetworks for SDINet, and two subnetworks are responsible for extracting multi-scale features from image pair in the same layer, and the global pooling instead of the fully connected layers are utilized to reduce the number of model parameters and ensure that the features are not lost. SDINet is trained with image pairs, each pair consisting of two real diseased leaf images or one real and one healthy leaf image. SDINet makes full use of the advantages of multi-scale dilated Inception to enrich and improve the information, enhance the adaptability of the model. Different from the existing deep CNNs, SDINet uses cosine distance learning to calculate the similarity between the leaf image pairs to recognize apple diseases. Experimental results on the apple diseased leaf image dataset validate that the proposed method is effective to recognize apple leaf disease using a small number of training samples.

Identification of Maize Leaf Diseases Based On AlexNet and ResNet50 Convolutional Neural Networks

Maize crop protection is crucial for global food security, requiring accurate disease identification. In Kenya, farmers rely on subjective visual analysis of symptomatic leaves, which is time-consuming and prone to errors. Computer vision technologies, like deep learning and machine learning, offer promising solutions for disease identification. This study applies Convolutional Neural Networks (CNNs), specifically AlexNet and ResNet-50, to automatically learn image features and enhance speed and accuracy in maize leaf disease identification. A dataset of 3200 digital maize leaf disease images from Embu County is used for training and testing. AlexNet achieved the highest average accuracy of 98.3%, followed by ResNet-50 at 96.6%. The machine learning, support vector machine (SVM) exhibited the lowest average accuracy of 85.5%. These findings highlight the significance of utilizing AlexNet and ResNet-50 in maize leaf disease identification and classification.

(Digital Presentation) Design and Development of Leaf Disease Detection Using the ML and Open CV for Tomato Plants

This paper proposes a system for identification of the accurate disease of the plants through the captured images and provides the remedies to increase the crop yielding. Initially the images disease leaf images are taken from the internet and created a database of different plant species and the named according to their disease for the checking the accuracy and this data available in the data base is the training data. The classifier is trained with the data base to achieve the optimum accuracy of the predicted output. Convolution Neural Network (CNN) is used for the prediction of the disease that consists of different layers. To capture the leaf images in the agriculture fields with large area drones are used with high resolution cameras that gives the input to the system. 78% accuracy is obtained from the proposed system to detect the plant is health. The proposed systems use ML and Open CV to detect the plant disease. Keywords— Disease, Machine Learning, Open-CV, SVM, plant leaf disease

A precise apple leaf diseases detection using BCTNet under unconstrained environments

Accurate detection of apple diseases is crucial for agricultural production as it enables timely diagnosis and control, thereby reducing crop yield losses. However, detecting apple leaf spots presents significant challenges due to unconstrained environmental conditions and multi-scale variations. This paper proposes a novel deep learning-based detection algorithm to accurately detect multi-scale apple leaf spots in unconstrained environments. The proposed method consists of several steps. First, a dataset of apple leaf disease spots with high-quality labels is created under the guidance of agricultural experts. Second, a Bole convolution module (BCM) for reducing the interference of redundant feature information on extracting feature information of apple leaf disease images in unconstrained environments is designed. Third, a cross-attention module (CAM) for reducing the computational effort of the detection network in non-diseased regions to reduce the impact of background interference information on the feature representation capability of the network is proposed. Fourth, to reduce the loss of surface and deep feature information of apple leaf diseases during feature fusion and communication, we employ a bidirectional transposition feature pyramid network (BTFPN) to address this problem. Finally, the proposed apple leaf disease detection network, which combines the Bole convolution module, Cross-attention module, and Bidirectional Transposed feature pyramid Network (BCTNet), achieves an accuracy of 85.23% and an average detection speed of 33 FPS on the self-built dataset. The proposed method outperforms other state-of-the-art methods in terms of accuracy. From visible light images, it can detect multi-scale disease spots on apple leaf surfaces in natural environments and provide decision information to growers and pesticide spraying robots. The proposed method can replace traditional manual diagnosis methods, optimize the spraying efficiency of pesticide robots, and reduce pesticide waste in non-diseased areas. Part of the dataset used in this paper can be found in the https://github.com/ZhouGuoXiong/BCTNet.

An efficient banana plant leaf disease classification using optimal ensemble deep transfer network

ABSTRACT Plants are a major source of food all around the world, and they are mainly affected by diseases caused by pathogens, insects, and parasitic plants. If the diseases are identified in the earlier stage, then it will be easy to apply pesticides and prevent the disease from further propagation. To recognise these diseases at earlier stages automatically, different researchers have established artificial intelligence-based approaches using machine learning and deep learning approaches which can identify the diseased lesions with high accuracy. In this paper, a novel Hybrid Moth Flame Optimization Algorithm-butterfly optimisation algorithm (HMFO-BOA) based optimal ensemble deep transfer network (OEDTN) classifier for banana leaf disease identification is developed. The OEDTN architecture offers increased predictability for banana leaf disease using Maximum Mean Discrepancy (MMD), ensemble learning, domain adaptation, and parameter transfer learning. The feature extraction is done by constructing different Deep Transfer Networks (DTN) via diverse kernel MMD. Finally, the DTNs are integrated via ensemble learning to obtain the final classification outcomes. The MFOBOA algorithm assigns optimal voting weights for each DTN to dynamically construct the OEDTN architecture. In the consequent testing process, the input banana leaf disease images are categorised into diverse classes such as BBW (Banana Bacterial Wilt Disease), BBS (Banana Black Sigatoka Disease), Cordana, pestalotiopsis, Sigatoka, and healthy. The experiments conducted on the Banana Leaf and BBW-BBS datasets prove that the proposed model offers improved performance than the state-of-art techniques.

The Efficiency of Transfer Learning and Data Augmentation in Lemon Leaf Image Classification

Leaf diseases in trees and plants are important factors that directly affect the yield of agricultural products. This problem may cause a decrease in the production capacity and profitability of farmers. For this reason, computer-aided detection and classification systems are needed to accurately detect plant diseases. In recent years, learning algorithms and image-processing techniques have been used effectively in the agricultural sector. In this study, the efficiency of transfer learning and data augmentation methods on a dataset consisting of lemon leaf images is examined and the classification of diseased and healthy lemon leaf images is performed. In our study, VGG16, ResNet50, and DenseNet201 transfer learning methods were applied both with and without data increment, and the effect of data augmentation on performance was evaluated. Among the deep transfer learning methods used, DenseNet201 gave the highest accuracy rate with 98.29%. This study shows that transfer learning methods can effectively distinguish between diseased and healthy lemon leaves. It has also been observed that data augmentation does not always provide performance improvement. In future studies, it is predicted that it will be appropriate to evaluate the effect of data augmentation more effectively by applying deep transfer learning methods to plants with different class numbers.

Detection of Early Blight Tomato Leaf Using k-Means Clustering

Early blight is one of the major diseases of tomatoes that affects the leaves and fruit quality. Detection and estimation of the disease severity are performed using the visual observation method. Visual detection requires significant time for visual inspection of a large cultivated area. Thus, image processing techniques have proven to be an effective method as compared to visual analysis. In this study, digital image processing methods and techniques were used to detect early blight of tomato (EBT), estimate the disease severity, and classify tomato leaves. Totally, 198 infected plants were randomly taken from the Haramaya University research site "Rare" at four different times. Diseased potato leaf images were captured, resized, and stored for experimentation. The stored images were processed using median filtering to remove noise while preserving useful features in an image and image enhancement. The RGB images were transformed to gray scale and CIELAB color space, and the k-means clustering was applied to estimate the disease severity of the potato leaves, and Otsu’s thresholding algorithm was applied to estimate the disease severity of both the detached and live leaves. MATLAB algorithms will be developed to determine the total area and infected lesion area of the leaf samples.

Detection of Unhealthy Citrus Leaves using Deep Neural Network

Abstract: This research paper presents a novel approach for detecting unhealthy citrus leaves using deep neural networks (DNNs). By leveraging convolutional neural networks (CNNs) and a comprehensive dataset of healthy and diseased citrus leaf images, we train a robust model for accurate classification. Data augmentation and transfer learning techniques are employed to enhance the model's performance. Extensive evaluation demonstrates the superiority of our approach. Our automated method contributes to plant health monitoring, facilitating timely interventions and optimizing crop yield for sustainable agriculture

Improved multi-scale inverse bottleneck residual network based on triplet parallel attention for apple leaf disease identification

Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf (including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved. To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet. The final processed image count is 14,000. The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets. The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing. It also achieves competitive results in apple leaf disease identification compared to some state-of-the-art methods.

Segmentation of dry bean (Phaseolus vulgaris L.) leaf disease images with U-Net and classification using deep learning algorithms

Segmentation of dry bean (Phaseolus vulgaris L.) leaf disease images with U-Net and classification using deep learning algorithms

Veg-DenseCap: Dense Captioning Model for Vegetable Leaf Disease Images

The plant disease recognition model based on deep learning has shown good performance potential. However, high complexity and nonlinearity lead to the low transparency and poor interpretability of such models. These limitations greatly limit the deployment and application of such models in field scenarios. To solve the above problems, we propose a dense caption generative model, Veg DenseCap. This model takes vegetable leaf images as input and uses object detection technology to locate abnormal parts of the leaf and identify the disease results. More importantly, it can describe the disease features it sees in natural language, and users can judge whether the relevant features are semantically consistent with human cognition based on these descriptions. First of all, a dataset containing Chinese feature description statements for images of 10 leaf diseases involving two vegetables (cucumber and tomato) was established. Secondly, Faster R-CNN was used as a disease detector to extract visual features of diseases, and LSTM was used as a language generator to generate description statements for disease features. Finally, the Convolutional Block Attention Module (CBAM) and the Focal Loss function were employed to overcome the imbalance between positive and negative samples and the weak performance of Faster R-CNN in obtaining key features. According to the test results, the Intersection-over-Union (IoU) and Meteor joint evaluation index of Veg-DenseCap achieved a mean Average Precision (mAP) of 88.0% on the dense captioning dataset of vegetable leaf disease images, which is 9.1% higher than that of the classical FCLN model. The automatically generated description statements are characterized by advantages of accurate feature description, correct grammar, and high diversity.

Radial basis function Neural Network optimized with Salp Swarm algorithm espoused paddy leaf disease classification

Diseases in rice often cause yield losses of 20–40% in crop production and are closely linked globally. Disease detection is difficult to quickly plan treatments and decrease crop loss. Diagnosis of rice diseases till done manually. Attain AI-assisted detection of disease, here proposed radial function Neural Network optimized with Salp-Swarm approach (PLDC-RBFNN-SSA) for rice leaf disease segmentation. First, we extract rice leaf images from a dataset containing rice leaf disease image samples. Then the preprocessing augmentation process is performed using a random transformation method such as: Simple image rotation as well as mirror functions implemented to every images. B. Rotate Right 90°, Rotate Left 90°, Flip Vertically, Flip Horizontally, Rotate 180°. These preprocessed output images are then passed to Black Widow's k-means clustering method to segment regions of interest (ROI) for rice leaf disease. The segmented output image is then sent to an adaptive grayscale co-occurrence matrix windowing algorithm (GLCMWAA) to extract radiation features. Extracted features then input into a radial function Neural Network optimized with Salp-Swarm algorithm to classify rice leaf images into bacterial blight, blast, brown spot and tungro. The proposed method is implemented in MATLAB and the performance of the proposed PLDC-RBFNN-SSA approach achieves 15.25%, 18.98%, 20.5% and 24.85% higher accuracy. Computation time is reduced by 50.2%, 48.2%, 38.26%, 20.2% compared with existing methods. Finally, the simulation results demonstrate that the proposed PLDC-RBFNN-SSA method is more efficient and accurate to obtain the optimal global solution for detecting and classifying leaf diseases in rice breeding is shown.

Deep-Learning-Based Strawberry Leaf Pest Classification for Sustainable Smart Farms

This paper presents a deep-learning-based classification model that aims to detect diverse pest infections in strawberry plants. The proposed model enables the timely identification of pest symptoms, allowing for prompt and effective pest management in smart farms. The present research employed an actual dataset of strawberry leaf images collected from a smart farm test bed. To expand the dataset, open data from sources such as Kaggle were utilized, while diseased leaf images were obtained through web crawling with the aid of the Python library. Subsequently, the expanded and added data were resized to a uniform size, and Pseudo-Labeling was implemented to ensure stable learning for both the training and test datasets. The RegNet and EfficientNet models were selected as the primary CNN-based image network models for repetitive learning, and ensemble learning was employed to enhance prediction accuracy. The proposed model is anticipated to facilitate the early identification and treatment of pests on strawberry leaves during the seedling period, a pivotal phase in smart farm development. Furthermore, it is expected to boost production in the agricultural industry and strengthen its competitive edge.

LBFNet: A Tomato Leaf Disease Identification Model Based on Three-Channel Attention Mechanism and Quantitative Pruning

The current neural networks for tomato leaf disease recognition have problems such as large model parameters, long training time, and low model accuracy. To solve these problems, a lightweight convolutional neural network (LBFNet) is proposed in this paper. First, LBFNet is established as the base model. Secondly, a three-channel attention mechanism module is introduced to learn the disease features in tomato leaf disease images and reduce the interference of redundant features. Finally, a cascade module is introduced to increase the depth of the model, solve the gradient descent problem, and reduce the loss caused by increasing the depth of the model. The quantized pruning technique is also used to further compress the model parameters and optimize the model performance. The results show that the LBFNet model achieves 99.06% accuracy on the LBFtomato dataset, with a training time of 996 s and a single classification accuracy of over 94%. Further training using the saved weight file after quantized pruning enables the model accuracy to reach 97.66%. Compared with the base model, the model accuracy was improved by 28%, and the model parameters were reduced by 96.7% compared with the traditional Resnet50. It was found that LBFNet can quickly and accurately identify tomato leaf diseases in complex environments, providing effective assistance to agricultural producers.