An Analysis of Plant Diseases Identification Based on Deep Learning Methods.

<span id="docs-internal-guid-01580d49-7fff-6f2a-70d1-7893ec0a6e14"><span>Plant diseases are a major cause of destruction and death of most plants and especially trees. However, with the help of early detection, this issue can be solved and treated appropriately. A timely and accurate diagnosis is critical in maintaining the quality of crops. Recent innovations in the field of deep learning (DL), especially in convolutional neural networks (CNNs) have achieved great breakthroughs across different applications such as the classification of plant diseases. This study aims to evaluate scratch and pre-trained CNNs in the classification of tomato plant diseases by comparing some of the state-of-the-art architectures including densely connected convolutional network (Densenet) 120, residual network (ResNet) 101, ResNet 50, ReseNet 30, ResNet 18, squeezenet and Vgg.net. The comparison was then evaluated using a multiclass statistical analysis based on the F-Score, specificity, sensitivity, precision, and accuracy. The dataset used for the experiments was drawn from 9 classes of tomato diseases and a healthy class from PlantVillage. The findings show that the pretrained Densenet-120 performed excellently with 99.68% precision, 99.84% F-1 score, and 99.81% accuracy, which is higher compared to its non-trained based model showing the effectiveness of using a combination of a CNN model with fine-tuning adjustment in classifying crop diseases.</span></span>

With the rapid development of precision agriculture and smart agriculture, the need to build an automatic identification and detection system for diseases and insect pests is increasing. Using computers to correctly label plant diseases and insect pests is an important prerequisite for achieving accurate classification of plant diseases and insect pests and ensuring system performance. In order to improve the accuracy of computer classification of plant pests and diseases, this paper proposes an automatic pest identification method based on the Vision Transformer (ViT). In order to avoid training overfitting, the plant diseases and insect pests data sets are enhanced by methods such as Histogram Equalization, Laplacian, Gamma Transformation, CLAHE, Retinex-SSR, and Retinex-MSR. Then use the enhanced data set to train the constructed ViT neural network, so as to realize the automatic classification of plant diseases and insect pests. The simulation results show that the constructed ViT network has a test recognition accuracy rate of 96.71% on the plant disease and insect pest public data set Plant_Village, which is about 1.00% higher than the Plant disease and pest identification method based on traditional convolutional neural networks such as GoogleNet and EfficentNetV2.

This composition bargains with the Tomato leaf infection discovery and classification utilizing different strategies like Convolutional Neural Network (CNN), Regions with CNN (R-CNN), Fast R-CNN and Faster R-CNN. The main issue in the agricultural sector is leaf diseases, which have an impact on crop yield and financial gain. Early detection of leaf diseases in plants is essential to prevent losses to the agricultural sector. There are several tomato leaf diseases that affect the crop's leaves, including the Mosaic virus, Early Blight, Healthy, Septoria leaf, and Bacterial spot. Using deep learning algorithms and image processing methods, we can identify the diseases in tomato leaves using developing deep learning approaches. The implementation procedures in our proposed work involves data collection, pre-processing, training, feature extraction, testing, and classification utilising the Visual Geometry Group (VGG 16) to identify damaged or healthy leaves. VGG 16 is incorporated to categorise the leaves as healthy or diseased based on the data and Regression's boundary box method is adopted. Therefore, using Faster RCNN, a model is created to identify and categorise diseases from every image of a tomato leaf that is used as an input, providing a forecast with a considerably greater degree of accuracy. We obtain an accuracy of approximately 98% after fitting the collected features into the neural network over 20 iterations.

The accurate recognition and early warning for plant diseases and pests are a prerequisite of intelligent prevention and control for plant diseases and pests. As a result of the phenotype similarity of the hazarded plant after plant diseases and pests occur, as well as the interference of the external environment, traditional deep learning models often face the overfitting problem in phenotype recognition of plant diseases and pests, which leads to not only the slow convergence speed of the network, but also low recognition accuracy. Motivated by the above problems, the present study proposes a deep learning model EResNet-support vector machine (SVM) to alleviate the overfitting for the recognition and classification of plant diseases and pests. First, the feature extraction capability of the model is improved by increasing feature extraction layers in the convolutional neural network. Second, the order-reduced modules are embedded and a sparsely activated function is introduced to reduce model complexity and alleviate overfitting. Finally, a classifier fused by SVM and fully connected layers are introduced to transforms the original non-linear classification problem into a linear classification problem in high-dimensional space to further alleviate the overfitting and improve the recognition accuracy of plant diseases and pests. The ablation experiments further demonstrate that the fused structure can effectively alleviate the overfitting and improve the recognition accuracy. The experimental recognition results for typical plant diseases and pests show that the proposed EResNet-SVM model has 99.30% test accuracy for eight conditions (seven plant diseases and one normal), which is 5.90% higher than the original ResNet18. Compared with the classic AlexNet, GoogLeNet, Xception, SqueezeNet and DenseNet201 models, the accuracy of the EResNet-SVM model has improved by 5.10%, 7%, 8.10%, 6.20% and 1.90%, respectively. The testing accuracy of the EResNet-SVM model for 6 insect pests is 100%, which is 3.90% higher than that of the original ResNet18 model. This research provides not only useful references for alleviating the overfitting problem in deep learning, but also a theoretical and technical support for the intelligent detection and control of plant diseases and pests. © 2024 Society of Chemical Industry.

Corn is one of the cereal crops and an important food ingredient because it contains a source of carbohydrates. It is the third largest food crop in the world after rice and wheat. In Indonesia, corn is one of the second largest food crops after rice. This plant has high yields and wide uses. However, in terms of disease, there are still many farmers who still have difficulty in distinguishing diseases, therefore digital assistance is needed to distinguish the characteristics of these plant diseases. Diseases used in this study were: blight and leaf rust. The dataset used has 2 types of corn plant diseases and a total of 2000 images of corn diseases. Convolutional Neural Network (CNN) algorithm is used for classification of corn plant diseases, which is included in the Deep Learning method which has good ability to recognize and classify a digital image object. This study uses the python programming language and the Tensorflow framework to conduct training and data testing. In this study, the classification of corn plant diseases using the Convolutional Neural Network (CNN) method was obtained with a total training accuracy of 97.5%, on data validation it got 100% accuracy and the level of accuracy in testing data using new data was 94%.

Background: Plant diseases and pests were natural disasters that seriously threaten agricultural production. Leaf diseases posed a significant threat to the overall productivity and quality of apple orchards. Currently, the diagnosis of plant diseases in apple orchards mainly relied on manual labor, which was both time-consuming and costly. It took a considerable amount of time to train an expert capable of accurately identifying apple diseases and pests, and different crop diseases vary. However, with the use of AI technology, training could be completed in just a few hours to a day, transforming computers into experts in disease and pest identification. Although China became a major apple cultivation nation, the development data from 2021 revealed that traditional advantageous production areas were undergoing dynamic adjustments due to optimization of industrial structure, leading to a decrease in cultivated land. Conversely, the demand for apples in the Chinese market continued to rise, even as the country's population structure gradually trended towards an aging population. This situation underscored the urgent need to replace the laborious and high-cost task of identifying apple foliar diseases in the cultivation process with intelligent agricultural technological solutions. Taking into account the aforementioned factors of the natural environment and socio-cultural considerations and addressing the issue of reduced apple yields due to the high impact of foliar diseases, we initiated study on disease prevention and control during the cultivation process. Methods: To overcome the objective impact of environmental factors, the system extracted images and performed operations such as enhancement and segmentation. Subsequently, a convolutional neural network was employed to process the enhanced images, extracting features such as color, shape, and texture of apple foliar lesions, which were then summarized into histograms using mathematical methods. These histograms served as the primary reference for distinguishing various diseases. Python was chosen as the programming language for the system, and the recognition system was operated by integrating frontend technologies, backend database design, and other relevant techniques. Results: Based on a substantial number of experimental results, the apple leaf disease and nutrient deficiency recognition system could accurately and with extremely high precision identifying major diseases commonly occurring in apple plants, including apple scab, leaf spot disease, rust disease, gray mold disease, and brown spot disease. Moreover, the system was capable of proposing remedies tailored to the specific pain points of each disease. It not only detected apple leaf diseases but also performs nutrient deficiency detection. This holds significant importance in enhancing both the yield and quality of apples. Conclusion: For the commonly occurring foliar diseases in cultivation, the system exhibited the capability to autonomously recognize and provide decision feedback. In contrast to the less efficient and more costly traditional approach of consulting experts, the more accessible apple foliar disease recognition system was poised to replace it as a primary tool for prevention and control assistance. This system will serve as a guiding companion, safeguarding the robust growth of apple plants.

Citrus fruit crops are among the world’s most important agricultural products, but pests and diseases impact their cultivation, resulting in yield and quality losses. Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade, allowing for early disease detection and improving agricultural production. This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning (DL) model, which improved accuracy while decreasing computational complexity. The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy. Using transfer learning, this study successfully proposed a Convolutional Neural Network (CNN)-based pre-trained model (EfficientNetB3, ResNet50, MobiNetV2, and InceptionV3) for the identification and categorization of citrus plant diseases. To evaluate the architecture’s performance, this study discovered that transferring an EfficientNetb3 model resulted in the highest training, validating, and testing accuracies, which were 99.43%, 99.48%, and 99.58%, respectively. In identifying and categorizing citrus plant diseases, the proposed CNN model outperforms other cutting-edge CNN model architectures developed previously in the literature.

Plant disease identification and classification play a pivotal role in improving the agricultural sector's efficiency and impact. Traditional laboratory testing methods have inherent limitations, including time-consuming processes and the poten-tial for misdiagnosis when relying on visual inspection alone. To address these challenges, the application of deep learning techniques, particularly convolutional neural networks (CNNs), has significantly reduced the need for manual, on-field efforts by utilizing cutting-edge technology. More recently, vision processing based transformers have emerged as a promising approach to enhance the accuracy of disease classification using high-dimensional visual data. Leveraging self-attention mechanisms and transformer architectures, these models have shown great potential for improving classification accuracy. In this research, we propose a vision transformer-based approach for classifying plant disease images. Our study utilizes the publicly available New Plant Diseases Dataset, comprising 70,295 training images and 17,572 validation images, encompassing 38 distinct plant disease and healthy classes. We fine-tune pre-trained vision transformer and VGG 16 models, achieving impressive validation accuracy rates of 99.16 % and 94 % respectively. Furthermore, we have developed a web-based application using the MobileNet model and deployed it using the Streamlit framework. This application provides a real-time classification of plant leaves, presenting the top three probabilities along with corresponding classes. It has demonstrated a validation accuracy rate of 98.72 % on the New Plant Diseases Dataset.

DFN-PSAN: Multi-level deep information feature fusion extraction network for interpretable plant disease classification

Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.

Plant diseases cause significant productivity and economic losses, as well as a reduction in agricultural product quality and quantity. One principal impact on low crop yield is sickness due to bacteria, virus and fungus It is possible to avoid it by employing plant disease detection and categorization procedures. We used machine learning to detect and classify diseases in plant leaves because it evaluates data from several perspectives and categorizes it into one of several predefined classifications. In this research we create a model for the classification task which is sequential model. We trained a convolutional neural network (CNN) with help of the plant village dataset, which have 55,000 images divided into 39 completely distinct categories of each healthy and effected leaves. We trained data by using Adam optimization technique because it almost constantly plays quicker and higher global minimal convergence in comparison to the alternative optimization techniques. We achieved a validation accuracy of 98.74% using the architecture of CNN containing optimized parameters. CNNs, as can be observed, have a high-stop overall performance, making them surprisingly suitable for computerized identification of plant illnesses using simple plant leaf images. The experiment effects completed are similar with different current strategies in literature.

The increasing prevalence of plant diseases poses a significant threat to global cotton production, leading to substantial economic losses and reduced crop yield. Traditional manual disease detection methods are time-consuming, labour-intensive, and often inaccurate. To address this challenge, this research proposes an advanced deep learning-based approach for automated cotton plant disease detection using Convolutional Neural Networks (CNNs). The study evaluates multiple CNN architectures, including GoogleNet, VGG16, DenseNet201, ResNet50, and TLResnet152V2, to determine their effectiveness in identifying and classifying diseased cotton leaves. The proposed methodology leverages normalized and augmented datasets, utilizing data pre-processing, feature extraction, and transfer learning techniques to enhance model performance. Extensive experimental evaluations demonstrate that data augmentation significantly improves classification accuracy, enabling CNN models to generalize better across diverse disease conditions. Among the tested architectures, TLResnet152V2 achieved the highest accuracy (92.03%) and F1-score (0.8842), outperforming all other models, followed closely by ResNet50. These results highlight the superiority of deep residual learning in plant disease classification, ensuring robust feature extraction and precise detection. This studies also explores the combination of CNN-primarily based disorder detection into clever agriculture structures, allowing actual-time sickness classification via cell packages and IoT-based totally answers. The findings affirm that deep gaining knowledge of-pushed plant disorder detection can considerably enhance precision farming, reducing dependency on professional agronomists while improving early disorder intervention techniques. destiny studies will awareness on deploying light-weight CNN models for facet computing, integrating climate statistics for predictive disorder modelling, and exploring hybrid deep studying strategies for enhanced accuracy. The examine demonstrates that CNN-based automatic cotton plant disease detection is a transformative step closer to sustainable, AI-enabled smart agriculture, ensuring better productivity, decreased crop losses, and advanced food safety.

- The Plant Disease Detection and Classification project is designed to help farmers identify crop diseases at an early stage using images of plant leaves. By applying advanced machine learning (ML) and deep learning (DL) techniques, this system aims to automatically detect and classify various plant diseases with high accuracy. Early identification of plant diseases plays a vital role in improving crop yield, reducing unnecessary pesticide usage, and encouraging sustainable farming practices. Once a disease is detected, the system not only identifies it but also provides a brief description and recommends appropriate prevention and treatment methods. To enhance performance, the project uses data augmentation and normalization techniques—helping the model handle variations in lighting, angles, and environmental conditions. The system is trained on a diverse dataset containing images of both healthy and diseased leaves from different plant species, categorized into 38 different classes. The project incorporates various machine learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, and Random Forests, as well as a Convolutional Neural Network (CNN) for deep learning. Among these, CNNs have shown outstanding performance due to their ability to learn spatial features from images effectively. While existing research has shown promising results, real-world challenges such as varying image quality, lighting conditions, and background clutter still affect accuracy. This project aims to overcome these issues by training on large, diverse datasets and fine-tuning model parameters. Ultimately, this project hopes to empower farmers with an intelligent tool to monitor crop health, make informed decisions, and contribute to food security through precision agriculture. Keywords: Plant Disease Detection, Automated Disease Detection System, Machine Learning, Deep Learning, Data Augmentation, Data Normalization, Support Vector Machine, KNN Classifier, Convolutional Neural Network, Decision Trees, Random Forest

Agriculture is the most critical part of the Indian economy so to prevent production loss, disease attacks must be predicted and treated early. In plants with differing climatic conditions, disease is very normal and natural which decreases the crop productivity. Study into the use of image processing techniques for plant disease identification has become a hot subject in order to address these issues. Deep learning (DL) is the golden age of machine learning (ML), and it is now helping to identify and classify plant diseases early. This review investigates and analyzed recent methods on Deep learning, Transfer learning and convolution neural network for crop disease detection. First, a look at the deep learning architectures, data sources and various image processing techniques that were used to process the imaging data. Many DL architectures have recently been adopted, along with visualization tools, which are critical for identifying signs and classifying plant diseases. We also go through some of the unsolved issues that must be tackled in order to create functional automated plant disease recognition systems that can be used in the field.

Plant diseases have a significant impact on global food security and the world's agricultural economy. Their early detection and classification increase the chances of setting up effective control measures, which is why the search for automatic systems that allow this is of major interest to our society. Several recent studies have reported promising results in the classification of plant diseases from RGB images on the basis of Convolutional Neural Networks (CNN). These studies have been successfully experimented on a large number of crops and symptoms, and they have shown significant advantages in the support of human expertise. However, the CNN models still have limitations. In particular, CNN models do not necessarily focus on the visible parts affected by a plant disease to allow their classification, and they can sometimes take into account irrelevant backgrounds or healthy plant parts. In this paper, we therefore develop a new technique based on a Recurrent Neural Network (RNN) to automatically locate infected regions and extract relevant features for disease classification. We show experimentally that our RNN-based approach is more robust and has a greater ability to generalize to unseen infected crop species as well as to different plant disease domain images compared to classical CNN approaches. We also analyze the focus of attention as learned by our RNN and show that our approach is capable of accurately locating infectious diseases in plants. Our approach, which has been tested on a large number of plant species, should thus contribute to the development of more effective means of detecting and classifying crop pathogens in the near future.