Identification Of Plant Diseases Research Articles

A Hybrid Deep Learning Architecture for Apple Foliar Disease Detection

Incorrectly diagnosing plant diseases can lead to various undesirable outcomes. This includes the potential for the misuse of unsuitable herbicides, resulting in harm to both plants and the environment. Examining plant diseases visually is a complex and challenging procedure that demands considerable time and resources. Moreover, it necessitates keen observational skills from agronomists and plant pathologists. Precise identification of plant diseases is crucial to enhance crop yields, ultimately guaranteeing the quality and quantity of production. The latest progress in deep learning (DL) models has demonstrated encouraging outcomes in the identification and classification of plant diseases. In the context of this study, we introduce a novel hybrid deep learning architecture named “CTPlantNet”. This architecture employs convolutional neural network (CNN) models and a vision transformer model to efficiently classify plant foliar diseases, contributing to the advancement of disease classification methods in the field of plant pathology research. This study utilizes two open-access datasets. The first one is the Plant Pathology 2020-FGVC-7 dataset, comprising a total of 3526 images depicting apple leaves and divided into four distinct classes: healthy, scab, rust, and multiple. The second dataset is Plant Pathology 2021-FGVC-8, containing 18,632 images classified into six categories: healthy, scab, rust, powdery mildew, frog eye spot, and complex. The proposed architecture demonstrated remarkable performance across both datasets, outperforming state-of-the-art models with an accuracy (ACC) of 98.28% for Plant Pathology 2020-FGVC-7 and 95.96% for Plant Pathology 2021-FGVC-8.

Automated Plant Disease Detection Using Deep Learning

Traditional methods of diagnosing plant diseases are mainly based on expert diagnosis which easily causes delay in crop disease control and crop management. Due to the problems of many target areas and similar target types in the process of plant disease detection, the identification accuracy and speed are required to be high. Therefore, it is necessary to optimize and improve the existing methods (CNN, RCNN, Fast RCNN, Faster RCNN, and SSD) to meet the detection needs. So, we came up with a deep learning-based approach to identify the plant leaf diseases and classify the diseases using an object detection model called YOLO. There are different versions of YOLO, proposed in recent times, in that YOLOv5 model is considered one of the best models and the other is the recent version, YOLOv8 which was proposed in 2022. So, in this paper we compared the two models of YOLO, YOLOv5 and YOLOv8 on the same dataset, and found that for the dataset used the YOLOv5 model was found to be the better model with a mAP of 0.641, while the YOLOv8 model has mAP of 0.516. This study proposes that the YOLOv5 model is suitable for plant disease identification tasks by comparing it with the latest version of the YOLOv8 model which was proposed in 2022. To make the YOLO model to be better it can be optimized and the transfer learning ability of the model can be used to expand the application scope in the future. Keywords— Deep Learning, CNN, RCNN, Fast RCNN, Faster RCNN, SSD, YOLO, mAP, Transfer Learning, Disease Classification, Detection.

Identification of potato plant diseases using CNN model

Identification of potato plant diseases using CNN model

Identification of Plant Diseases Using Imaging Processing and Machine Learning 1st

This comprehensive review explores the most current developments in the use of image processing and machine learning methods for the diagnosis of plant diseases. After a thorough review of the literature, we critically assess the different frameworks and methods used to classify disorders affecting plants. Our study highlights the advantages and disadvantages of each approach while focusing on how accurate it is in diagnosing a wide variety of illnesses. In addition, we investigate new developments and trends in this quickly developing industry. We conclude by talking about the ongoing difficulties and suggesting some directions for further study to improve the effectiveness of plant disease detection systems. Keywords— CNN, VGG16, image processing, classification, neural networks, and machine learning

AgriSolutions: Innovations in Crop Disease Prediction, Crop Recommendation, and E Commerce

Abstract: Crop disease prediction and crop recommendation are important steps towards sustainable agriculture. The use of deep learning, EfficientNet_v2 and image processing technologies can help in improve accuracy in these tasks. Identification of plant diseases and recommendation of crops cannot maximize only yield production but can be supportive for varied types of agricultural practices The existing system of crop recommendation employs data analysis to suggest suitable crops based on soil and climate factors, while disease prediction system utilizes predictive models to anticipate and prevent crop diseases. In this Paper, we propose a model that combines these techniques to accurately identify to recommend suitable crops based on the Soil Parameters and climate conditions of the farm. Our proposed model achieved and classify plant diseases, recommend suitable crops based on the soil condition. To achieve this, we trained a deep neural network on a large dataset of images of healthy and diseased plants, and performed image preprocessing.

PlantGuard: Leaf Disease Detecting and Suggests Remedy using Machine Learning

Abstract: Plantguard is a leader in the fusion of artificial intelligence and agriculture, with a primary focus on transforming cropmanagement via sophisticated leaf disease detection. The initiative provides quick and reliable plant disease identification from leaf photos by utilizing cutting-edge computer vision and machine learning algorithms, giving farmers the ability to take prompt action. Plantguard is dedicated to creating user-friendly interfaces that will allow precision agriculture to be smoothly incorporatedinto conventional agricultural methods. This will promote sustainable production, boost farmer productivity, and raise their financial stability. This novel strategy represents a significant step toward a future for global agriculture that is both resilient and more technologically advanced. Convolution neural network (CNN), transfer learning, deep learning, image processing, data augmentation, and agricultural applications are some of the index terms

PL-DINO: An Improved Transformer-Based Method for Plant Leaf Disease Detection

Agriculture is important for ecology. The early detection and treatment of agricultural crop diseases are meaningful and challenging tasks in agriculture. Currently, the identification of plant diseases relies on manual detection, which has the disadvantages of long operation time and low efficiency, ultimately impacting the crop yield and quality. To overcome these disadvantages, we propose a new object detection method named “Plant Leaf Detection transformer with Improved deNoising anchOr boxes (PL-DINO)”. This method incorporates a Convolutional Block Attention Module (CBAM) into the ResNet50 backbone network. With the assistance of the CBAM block, the representative features can be effectively extracted from leaf images. Next, an EQualization Loss (EQL) is employed to address the problem of class imbalance in the relevant datasets. The proposed PL-DINO is evaluated using the publicly available PlantDoc dataset. Experimental results demonstrate the superiority of PL-DINO over the related advanced approaches. Specifically, PL-DINO achieves a mean average precision of 70.3%, surpassing conventional object detection algorithms such as Faster R-CNN and YOLOv7 for leaf disease detection in natural environments. In brief, PL-DINO offers a practical technology for smart agriculture and ecological monitoring.

Utilizing Machine Learning Techniques forthe Detection of Plant Leaf Diseases

Identification of plant diseases is crucial for preserving crops and ensuring food security. Analysis of detectable chemicals in plants is essential to understand transmission mechanisms and develop effective strategies for disease control measures to conserve agricultural products and prevent losses. However, manual monitoring of plant health is labor-intensive and time-consuming, requiring specialized skills and knowledge. To overcome these challenges, random forest systems are emerging as a powerful tool for disease detection and classification in plants. The process involves several steps, including image acquisition, preprocessing, and segmentation, followed by feature extraction, model training, and testing. Leveraging machine learning techniques, the random forest algorithm enables accurate classification of healthy and diseased leaves based on selected features. Image classification techniques are utilized to extract color information, while global features such as size and texture are captured through annotation. The dataset used for model training and testing comprises diverse samples, encompassing healthy and diseased plants. The random forest model is trained on 70% of the data to ensure robust learning, while the remaining 30% is reserved for testing, facilitating the exploration of model performance and overall feasibility

Multiclass classification of diseased grape leaf identification using deep convolutional neural network(DCNN) classifier

The cultivation of grapes encounters various challenges, such as the presence of pests and diseases, which have the potential to considerably diminish agricultural productivity. Plant diseases pose a significant impediment, resulting in diminished agricultural productivity and economic setbacks, thereby affecting the quality of crop yields. Hence, the precise and timely identification of plant diseases holds significant importance. This study employs a Convolutional neural network (CNN) with and without data augmentation, in addition to a DCNN Classifier model based on VGG16, to classify grape leaf diseases. A publicly available dataset is utilized for the purpose of investigating diseases affecting grape leaves. The DCNN Classifier Model successfully utilizes the strengths of the VGG16 model and modifies it by incorporating supplementary layers to enhance its performance and ability to generalize. Systematic evaluation of metrics, such as accuracy and F1-score, is performed. With training and test accuracy rates of 99.18 and 99.06%, respectively, the DCNN Classifier model does a better job than the CNN models used in this investigation. The findings demonstrate that the DCNN Classifier model, utilizing the VGG16 architecture and incorporating three supplementary CNN layers, exhibits superior performance. Also, the fact that the DCNN Classifier model works well as a decision support system for farmers is shown by the fact that it can quickly and accurately identify grape diseases, making it easier to take steps to stop them. The results of this study provide support for the reliability of the DCNN classifier model and its potential utility in the field of agriculture.

Investigating attention mechanisms for plant disease identification in challenging environments

There is an increasing demand for efficient and precise plant disease detection methods that can quickly identify disease outbreaks. For this, researchers have developed various machine learning and image processing techniques. However, real-field images present challenges due to complex backgrounds, similarities between different disease symptoms, and the need to detect multiple diseases simultaneously. These obstacles hinder the development of a reliable classification model. The attention mechanisms emerge as a critical factor in enhancing the robustness of classification models by selectively focusing on relevant regions or features within infected regions in an image. This paper provides details about various types of attention mechanisms and explores the utilization of these techniques for the machine learning solutions created by researchers for image segmentation, feature extraction, object detection, and classification for efficient plant disease identification. Experiments are conducted on three models: MobileNetV2, EfficientNetV2, and ShuffleNetV2, to assess the effectiveness of attention modules. For this, Squeeze and Excitation layers, the Convolutional Block Attention Module, and transformer modules have been integrated into these models, and their performance has been evaluated using different metrics. The outcomes show that adding attention modules enhances the original models' functionality.

CNN based plant disease identification using PYNQ FPGA

This research presents a novel approach for plant disease identification utilizing Convolutional Neural Networks (CNNs) and the PYNQ FPGA platform. The study leverages the parallel processing capabilities of FPGAs to accelerate CNN inference, aiming to enhance the efficiency of plant disease detection in agricultural settings. The implementation involves optimizing the CNN architecture for deployment on the PYNQ FPGA, considering factors such as image size and learning rates. Through experimentation, the research refines hyper parameters, achieving improved accuracy and F1 scores. Visualizations using heat maps highlight the CNN's reliance on color, shape, and texture for feature extraction in disease identification. The integration of FPGA technology demonstrates promising advancements in real-time, high-performance plant disease classification, offering potential benefits for precision agriculture and crop management. This research contributes to the growing field of FPGA-accelerated deep learning applications in agro technology, addressing challenges in plant health monitoring and fostering sustainable agricultural practices.

Deep Learning-Based Leaf Disease Detection in Crop Using Images for Agricultural Application

The "Leaf Disease Detection" system addresses the critical challenge of plant diseases in agriculture through the implementation of an automated solution leveraging deep learning techniques. In this comprehensive endeavor, convolutional neural networks (CNNs), specifically DenseNet-121, ResNet-50, VGG-16, and Inception V4, are fine-tuned for efficient and accurate identification of plant diseases. The project utilizes the Plant Village dataset, encompassing 54,305 images across 38 plant disease classes, to conduct a comparative analysis of model performance. DenseNet-121 emerged as the top-performing model, achieving an exceptional 99.81% classification accuracy, surpassing other state-of-the-art models. The system's methodology strategically employs transfer learning to overcome computational challenges associated with training deep CNN layers. This approach, coupled with the multi-class classification strategy, proves robust in handling diverse plant species and diseases within each class. The results highlight the superior efficiency of transfer learning in comparison to building models from scratch, showcasing the potential for real-world applications in agriculture. The system's success is attributed to the careful optimization of hyper parameters and the adoption of advanced deep learning techniques, offering a promising avenue for automated and accurate plant disease detection, with implications for improving agricultural practices, minimizing economic losses, and ensuring global food security.

Detection of Diseases in Blackgram (Vigna mungo L.) Using Machine Learning Models: A Case Study

Black gram (Vigna mungo L.) is widely used in Indian cuisine and one of the most significant pulses cultivated in India. Identification of plant diseases at their earlier stages is essential to take necessary plant protection measures to reduce yield loss to the farmers. Anthracnose and Powdery Mildew are the major diseases in black gram which causes significant yield losses to the farmers. In this research study, advanced disease detection machine learning models such as Multinomial Logistic Regression, Random Forest Classifier were employed to assist the farmers in detection of plant leaf diseases in blackgram at their early stages of growth. For this present study, Image data sets were collected from Thanjavur block, Thanjavur district, Tamil Nadu. Results of the study showed that accuracy of Random Forest Classifier was higher with train accuracy 99.17% and test accuracy 97.00% when compared to the other machine learning methods for detection of plant leaf diseases in black gram, which aids in promotion of smart agriculture.

Deep Learning Enabled Efficient Net with KLM for Pragmatic Plant Disease Diagnosis

Research is still being done on the automated identification and classification of plant diseases. A quick and precise methodology for identifying plant diseases can improve both small-scale commercial crop protection and large-scale food security. Deep learning (DL) methods also enable the monitoring of plant health and the early diagnosis of illnesses. This study's DLEN-KLM model, which is built on EfficientNet and Kernel extreme Learning Machine (KLM) and is designed to diagnose plant diseases intelligently, addresses this issue. In contrast to limited adaptive histogram equalisation as a preprocessing method, the suggested DLEN-KLM model uses median filtering in its construction. A feature extractor built on EfficientNet B0 is also part of the DLEN-KLM model, which is used to create the best feature vectors before categorising them with the KLM model. Utilising a benchmark dataset, the effectiveness of the DLEN-KLM technique is validated. The experimental results showed that the technique outperformed more contemporary methods in terms of disease diagnosis.

Comprehensive Review on Plant Disease Detection and Identification

Image Agriculture is a major contributor to financial development and is the main source of income in many countries. The primary goal of disease prevention and treatment, notwithstanding the different challenges farmers face, including plant diseases, is to precisely identify and evaluate the disease while the plant is still growing. Object recognition and classification in images has been made possible by rapid advancements in deep learning, or DL, techniques. Recently, DL approaches have been applied to farming and other agricultural applications and have showed potential in a variety of areas. These plants are commonly affected by diseases such as Septoria leaf spots, bacterial spots, late plant blight, and curled yellow leaves. Our work presents a hybrid approach to early illness detection that applies region-based full convolutional networks (RFCN) for early disease identification and region-based convolutional neural networks (RCNN) for diseases classification. We construct a network model based on the EfficientNetB7 model that optimizes at several scales via dilated convolution. The network is constructed layer by layer, top-down, with several optimizations. Real-time crop images taken from crop fields are used in the experiment, and Efficient Net B7 and hybrid convolution models are used. When the suggested ensemble model is implemented in Python, the outcome demonstrates that it has high accuracy and low loss when compared to other current methodologies.

A few-shot learning method for tobacco abnormality identification.

Tobacco is a valuable crop, but its disease identification is rarely involved in existing works. In this work, we use few-shot learning (FSL) to identify abnormalities in tobacco. FSL is a solution for the data deficiency that has been an obstacle to using deep learning. However, weak feature representation caused by limited data is still a challenging issue in FSL. The weak feature representation leads to weak generalization and troubles in cross-domain. In this work, we propose a feature representation enhancement network (FREN) that enhances the feature representation through instance embedding and task adaptation. For instance embedding, global max pooling, and global average pooling are used together for adding more features, and Gaussian-like calibration is used for normalizing the feature distribution. For task adaptation, self-attention is adopted for task contextualization. Given the absence of publicly available data on tobacco, we created a tobacco leaf abnormality dataset (TLA), which includes 16 categories, two settings, and 1,430 images in total. In experiments, we use PlantVillage, which is the benchmark dataset for plant disease identification, to validate the superiority of FREN first. Subsequently, we use the proposed method and TLA to analyze and discuss the abnormality identification of tobacco. For the multi-symptom diseases that always have low accuracy, we propose a solution by dividing the samples into subcategories created by symptom. For the 10 categories of tomato in PlantVillage, the accuracy achieves 66.04% in 5-way, 1-shot tasks. For the two settings of the tobacco leaf abnormality dataset, the accuracies were achieved at 45.5% and 56.5%. By using the multisymptom solution, the best accuracy can be lifted to 60.7% in 16-way, 1-shot tasks and achieved at 81.8% in 16-way, 10-shot tasks. The results show that our method improves the performance greatly by enhancing feature representation, especially for tasks that contain categories with high similarity. The desensitization of data when crossing domains also validates that the FREN has a strong generalization ability.

Apple leaf diseases recognition based on ResNet-101 and CBAM

Early identification and management of plant diseases is crucial to raising crop quality and yield. In order to create an effective deep learning model for autonomous apple leaf diseases identification, the issue of recognizing and classifying apple leaf infections is the main focus of this investigation. To improve the detection performance of the model, Convolutional Block Attention Module (CBAM) is added to the ResNet-101 deep convolutional neural network (CNN) model utilized in this work. According to the experimental findings, the ResNet-101 model with Convolutional Block Attention Module exhibits excellent performance when attempting to diagnose problems in apple leaves. Specifically, the ResNet-101+CBAM model outperforms the standard ResNet-101 model in the disease categories of 'Brown spot', 'Grey spot', 'Health' and 'Rust'. In some disease categories, such as 'Powdery mildew', the performance enhancement of the module (CBAM) is relatively insignificant due to the confusion caused by the similarity of features between the categories. Further analysis showed that the data enhancement technique had a significant positive impact on model performance.

An analysis to investigate plant disease identification based on machine learning techniques

AbstractIn agriculture, crops are severely affected by illnesses, which reduce their production every year. The detection of plant diseases during their initial stages is critical and thus needs to be addressed. Researchers have been making significant progress in the development of automatic plant disease recognition techniques through the utilization of machine learning (ML), image processing, and deep learning (DL). This study analyses the recent advancements made by researchers in the field of ML techniques for identifying plant diseases. This study also examines various methods used by researchers to produce ML solutions, such as image preprocessing, segmentation, and feature extraction. This study highlights the challenges encountered while creating plant disease identification systems, such as small datasets, image capture conditions, and the generalizability of the models, and discusses possible solutions to cater to these problems. Still, the development of a solution that automatically detects various plant diseases for various plant species remains a big challenge. To address these challenges, there is a need to create a system that is trained on an extensive dataset that contains images of various types of diseases a plant can suffer from, and plant images should be taken at various stages of the disease's development. This study further presents an analysis of various methods used at different stages of plant disease identification.

A bilinear transformer interactive neural networks-based approach to fine-grained recognition and protection of plant diseases for gardening design

Landscape gardening design is an important part of urban ecology and urban image, which is of great significance for urban plant protection. Plant diseases are a major challenge that must be faced in the conservation of landscape plants. Having timely and comprehensive information about the types and developmental stages of plant diseases is crucial for effective disease control. Traditional plant disease differentiation, which relies on manual inspection by professionals, is both time-consuming and labor-intensive. The rapid development of deep artificial neural network technology, especially the rise of computer vision technology, provides efficient and accurate technical means for plant disease identification. In this paper, we introduce the Interactive Bilinear Transformer Network (IBTN) model, which utilizes fine-grained recognition technology for landscape plant disease identification. We validate the effectiveness of model using several public datasets, demonstrating its competitive performance.

Deep Learning Techniques for Identification of Different Malvaceae Plant Leaf Diseases

INTRODUCTION: The precise and timely detection of plant diseases plays a crucial role in ensuring efficient crop management and disease control. Nevertheless, conventional methods of disease identification, which heavily rely on manual visual inspection, are often time-consuming and susceptible to human error. The knowledge acquired from this research paper enhances the overall comprehension of the discipline and offers valuable direction for future progressions in the application of deep learning for the identification of plant diseases.[1][2] AIM: to investigate the utilization of deep learning techniques in identifying various Malvaceae plant diseases. METHODS: AlexNet, VGG, Inception, REsNet and other CNN architectures are analyzed on Malvaceae plant diseases specially on Cotton, Ocra and Hibiscus, different data collection methods ,Data augmentation and Normalization techniques. RESULTS: Inception V4 have Training Accuracy 98.58%, VGG-16 have Training Accuracy 84.27%, ResNet-50 have Training Accuracy 98.72%, DenseNet have Training Accuracy 98.87%, Inception V4 have Training Loss 0.01%, VGG-16 have Training Loss 0.52%, ResNet-50 have Training Loss 6.12%, DenseNet have Training Loss 0.016%, Inception V4 have Test Accuracy 97.59%, VGG-16 have Test accuracy 82.75%, ResNet-50 have Test Accuracy 98.73%, DenseNet have Test Accuracy 99.81%, Inception V4 have Test Loss 0.0586%, VGG-16 have Test Loss 0.64%, ResNet-50 have Test Loss 0.027%, DenseNet have Test Loss 0.0154% . CONCLUSION: conclusion summarizes the key findings and highlights the potential of deep learning as a valuable tool for accurate and efficient identification of Malvaceae plant diseases.