Plant Disease Classification Research Articles

Image‐based detection and classification of plant diseases using deep learning: State‐of‐the‐art review

AbstractPlant diseases are assumed to be one of the primary causes regulating food manufacturing and reducing deficits in crop yield, and it is crucial that plant diseases have rapid spotting and identification. Since the demand for food is rising quickly due to population growth, the main issue for any country is plant disease mechanization in agricultural science. Recent developments in deep learning techniques have found use in plant disease recognition, offering a robust tool with exceedingly precise results. Our main goal is to employ deep learning mechanisms to better understand diseases and how to identify them quickly. In this regard, we analyzed 94 publications chosen from the last 7 years (2016–2023) that show how CNN's philosophy has evolved over this period with various approaches to treating plant diseases. Furthermore, a full description of numerous crops, diseases connected to them, various datasets relating to plant diseases, existing CNN models, and customized CNN architectures is provided. The results of this state‐of‐the‐art review can be implemented to comprehend the cutting‐edge trends in the application of deep learning (CNNs) to detect plant diseases as well as pinpoint any research gaps that require the scientific community's attention.

Ensemble of pre-trained vision transformer models in plant disease classification, an efficient approach

Ensemble of pre-trained vision transformer models in plant disease classification, an efficient approach

A Novel Ensemble Model for Strawberry Plant Diseases Detection and Classification

A Novel Ensemble Model for Strawberry Plant Diseases Detection and Classification

Optimal Fuzzy Deep Neural Networks-Based Plant Disease Detection and Classification on UAV-Based Remote Sensed Data

Optimal Fuzzy Deep Neural Networks-Based Plant Disease Detection and Classification on UAV-Based Remote Sensed Data

Plant Disease Detection and Classification System Recent Studies and Beyond

Plant Disease Detection and Classification System Recent Studies and Beyond

AI-powered Solution for Plant Disease Detection in Viticulture

In an era dominated by the intersection of advanced technology and traditional industries, the domain of agriculture is on the verge of a revolutionary transformation. This article introduces a solution for vineyard producers, harnessing satellite imagery, weather data, and deep learning (DL) to identify vineyard diseases robustly. This solution, designed for proactive plant health management, stands as a transformative tool towards digital viticulture. Such tools transition from luxuries to essentials as vineyards confront evolving challenges like climate change and new pathogens. Our research builds on the hypothesis that customising deep learning architectures for specific tasks is crucial in enhancing their effectiveness. We contribute by introducing a tailored convolutional neural network (CNN) architecture, developed specifically for the classification of plant diseases using vineyard imagery. The experimental results demonstrate that our custom CNN architecture exhibits performance on par with established state-of-the-art models like ResNet50 and MobileNetV2, underscoring the value of specialized solutions in addressing the unique challenges of viticulture. This paper introduces an overview of the solution’s architecture, presents the implementation of DL modules with their corresponding results, and describes use case scenarios.

Prospect of Smart Agriculture Using IoT and Data Analytics: A Perspective of Kebbi State, Northwestern Nigeria

Nigeria is an agricultural nation, with majority of its citizenry predominantly relied on it for survival. Recently, the sector is receiving a lot of attention due to the need to diversify and a bit away from an oil-driven economy. The sector is one among the most contributing to the nations` Gross Domestic Product (GDP), recording 21% in the previous year. However, one of the causes of low crop yield is diseases caused by agents such as fungi, bacteria, and viruses. The advent of technology has led to its influence in the agricultural sector. The recent evolution and fusion of IOT, ML and Data Analytics has brought succour for especially plant monitoring and management. This research work investigated the capability of IOT, ML and DA in tomato plant disease classification in Yauri Emirate, Northwestern Nigeria. We further conduct a survey with a view to understanding the knowledge, acceptability or otherwise of the mentioned techniques. The result of our classification using CNN, a DL model achieves a near-optimal accuracy of 98.6% with a loss of 0.03% while recording over 98.3 % for both precision and recall on the predicted labels. We also observed that our target audience for the survey lacks near total knowledge of smart farming, hence the need for the stakeholders in the domain to embark on sensitization and awareness towards reaping its numerous advantages

Yolov5-based Convolutional Feature Attention Neural Network for Plant Disease Classification

Yolov5-based Convolutional Feature Attention Neural Network for Plant Disease Classification

Improving Plant Disease Classification With Deep-Learning-Based Prediction Model Using Explainable Artificial Intelligence

Improving Plant Disease Classification With Deep-Learning-Based Prediction Model Using Explainable Artificial Intelligence

CSXAI: a lightweight 2D CNN-SVM model for detection and classification of various crop diseases with explainable AI visualization.

Plant diseases significantly impact crop productivity and quality, posing a serious threat to global agriculture. The process of identifying and categorizing these diseases is often time-consuming and prone to errors. This research addresses this issue by employing a convolutional neural network and support vector machine (CNN-SVM) hybrid model to classify diseases in four economically important crops: strawberries, peaches, cherries, and soybeans. The objective is to categorize 10 classes of diseases, with six diseased classes and four healthy classes, for these crops using the deep learning-based CNN-SVM model. Several pre-trained models, including VGG16, VGG19, DenseNet, Inception, MobileNetV2, MobileNet, Xception, and ShuffleNet, were also trained, achieving accuracy ranges from 53.82% to 98.8%. The proposed model, however, achieved an average accuracy of 99.09%. While the proposed model's accuracy is comparable to that of the VGG16 pre-trained model, its significantly lower number of trainable parameters makes it more efficient and distinctive. This research demonstrates the potential of the CNN-SVM model in enhancing the accuracy and efficiency of plant disease classification. The CNN-SVM model was selected over VGG16 and other models due to its superior performance metrics. The proposed model achieved a 99% F1-score, a 99.98% Area Under the Curve (AUC), and a 99% precision value, demonstrating its efficacy. Additionally, class activation maps were generated using the Gradient Weighted Class Activation Mapping (Grad-CAM) technique to provide a visual explanation of the detected diseases. A heatmap was created to highlight the regions requiring classification, further validating the model's accuracy and interpretability.

A plant disease classification using one-shot learning technique with field images

Early diagnosis of plant diseases is crucial for preventing plagues and mitigating their effects on crops. The most precise automatic methods for identifying plant diseases using images of plant fields are powered by deep learning. Big image datasets should always be gathered and annotated for these methods to work, which is often not technically or financially feasible. This paper offers one-shot learning (OSL) techniques for plant disease classification with limited datasets utilizing Siamese Neural Network (SNN). There are five different crop kinds in the dataset: grape, wheat, cotton, cucumber, and corn. Five sets of images showing both healthy and diseased crops are used to represent each of the new crops. The dataset's includes 25 classes with 875 leaf images. Data augmentation techniques are used to enhance the size and dimension of the plant leaf disease image dataset. To provide effective segmentation, this paper provides a unique method for region-based image segmentation that divides an image into its most prominent regions. It also addresses issues with earlier region-based segmentation methods. SVM-based classifiers have better generalization properties as their efficiency does not depend on the number of features. Such merit is beneficial in primary diagnostics decisions to check if the input image is included in the database or not to reduce the consumed time. OSL was applied and compared to standard fine-tuning transfer learning utilizing Siamese networks and triplet loss. Siamese provides superior classification accuracy and localization accuracy with minimal errors than other approaches. The proposed approach has a total processing time of 5 ms, which makes it appropriate for real-time applications. In terms of specificity, sensitivity, precision, accuracy, MCC, and F-measure, the proposed approach beats all current machine learning algorithms for small training sets.

A Study on the Performance of Deep Learning Models for Leaf Disease Detection

The backbone of our Indian economy is agriculture. Plant diseases are a key contributor to substantial reductions in crop quality and quantity. Finding leaf diseases is a crucial job in the study of plant pathology. So, Deep learning models are essential for classification objectives with positive outcomes. Many different methods have been employed in recent years to classify plant diseases. This work has aided in identifying and categorizing a plant leaf disease. Images of Tomato, Potato, and Pepper plant leaves from the PlantVillage Database, which includes fifteen disease classifications, were used in this study. The pre-trained Deep learning models like InceptionV3, MobileNet, DenseNet121, Inception-ResNetV2, and ResNet152V2 are utilized to diagnose leaf diseases. The classification of both healthy and various sorts of leaf illnesses is taught to deep learning models.

Plant disease detection model for edge computing devices.

In this paper, we address the question of achieving high accuracy in deep learning models for agricultural applications through edge computing devices while considering the associated resource constraints. Traditional and state-of-the-art models have demonstrated good accuracy, but their practicality as end-user available solutions remains uncertain due to current resource limitations. One agricultural application for deep learning models is the detection and classification of plant diseases through image-based crop monitoring. We used the publicly available PlantVillage dataset containing images of healthy and diseased leaves for 14 crop species and 6 groups of diseases as example data. The MobileNetV3-small model succeeds in classifying the leaves with a test accuracy of around 99.50%. Post-training optimization using quantization reduced the number of model parameters from approximately 1.5 million to 0.93 million while maintaining the accuracy of 99.50%. The final model is in ONNX format, enabling deployment across various platforms, including mobile devices. These findings offer a cost-effective solution for deploying accurate deep-learning models in agricultural applications.

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

Accurate identification of crop diseases is an effective way to promote the development of intelligent and modernized agricultural production, as well as to reduce the use of pesticides and improve crop yield and quality. Deep learning methods have achieved better performance in classifying input plant disease images. However, many plant disease datasets are often constructed from controlled scenarios, and these deep learning models may not perform well when tested in real-world agricultural environments, highlighting the challenges of transitioning to natural farm environments under the new demand paradigm of Agri 4.0. Based on the above reasons, this work proposes using a multi-level deep information feature fusion extraction network (DFN-PSAN) to achieve plant disease classification in natural field environments. DFN-PSAN adopts the YOLOv5 Backbone and Neck network as the base structure DFN and uses pyramidal squeezed attention (PSA) combined with multiple convolutional layers to design a novel classification network PSAN, which fuses and processes the multi-level depth information features output from DFN and highlights the critical regions of plant disease images with the help of pixel-level attention provided by PSA, thus realizing effective classification of multiple fine-grained plant diseases. The proposed DFN-PSAN was trained and tested on three plant disease datasets. The average accuracy and F1-score exceeded 95.27%. The PSA attention mechanism saved 26% of model parameters, achieving a competitive performance among existing related methods. In addition, this work effectively enhances the transparency of the features of the model attention to plant diseases through t-SNE with SHAP interpretable methods.

Automated DSegNet Model for the Classification and Recognition of Rice Plant Diseases using Transfer Learning

Automated DSegNet Model for the Classification and Recognition of Rice Plant Diseases using Transfer Learning

Learning Deep Asymmetric Tolerant Part Representation

Categorization objects at a sub-ordinate level inevitably poses a significant challenge, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , inter-class difference is very subtle and only exists in a few key parts. Therefore, how to localize these key parts for discriminative visual categorization without requiring expensive pixel-level annotations becomes a core question. To that end, this paper introduces a novel asymmetry tolerant part segmentation network (ATP-Net). ATP-Net simultaneously learns to segment parts and identify objects in an end-to-end manner using only image-level category labels. Given the intrinsic asymmetry property of part alignment, a desirable learning of part segmentation should be capable of incorporating such property. Despite the efforts towards regularizing weakly supervised part segmentation, none of them consider this vital and intrinsic property, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the spatial asymmetry of part alignment. Our work, for the first time, proposes to explicitly characterize the spatial asymmetry of part alignment for visual tasks. We propose a novel asymmetry loss function to guide the part segmentation by encoding the spatial asymmetry of part alignment, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , restricting the upper bound of how asymmetric those self-similar parts are to each other in the network learning. Via a comprehensive ablation study, we verify the effectiveness of the proposed ATP-Net in driving the network learning towards semantically meaningful part segmentation and discriminative visual categorization. Consistently superior/competitive performance are reported on 12 datasets covering crop cultivar classification, plant disease classification, bird/butterfly species classification, large-scale natural image classification, attribute recognition and landmark localization.

High-performance deep transfer learning model with batch normalization based on multiscale feature fusion for tomato plant disease identification and categorization

Deep learning and machine learning are cutting-edge methods for analysing images that have considerable potential. Artificial Neural Networks (A-NNs), one of the most well-known methods of computer intelligence, are now used in machine learning (ML) and deep transfer learning (DL) to raise plant production and quality. Identification and primary prevention of plant diseases at the appropriate time are essential for boosting productivity. Due to the phenomenon of minimally intense data in the background and foreground areas of the image, the extensive colour similarity between regions of unhealthy and normal leaves, the presence of noise in the sampling data, and changes in the location, size, and shape of plant leaf, it is difficult to correctly identify and classify plant diseases. In an effort to address these issues, a reliable technique for classifying plant diseases was developed by using a deep AlexNet CNN architecture as the main network with batch normalisation. In the three-step process, the first annotation is made to obtain the RoI (region of interest). The AlexNet CNN is therefore suggested for deep primary feature extraction in a constructed efficient network. The research demonstrates that the existing strategy is superior to more recent ones in terms of accuracy and dependability in recognising diseases in plants. Based on a deep transfer AlexNet CNN model, this research work developed a model for diseases identification and classification in plant leaves. It is trained using additional datasets that include a variety of plant leaf classifications and background images. From Plant Village and Kaggle, we gathered data on healthy and diseased tomato plant leaves. We are obtaining a near-balanced dataset containing ten different leaf disease kinds, such as bacterial, fungal, viral, and nutrient insufficiency. Ten classes have been considered for this research by gathering a dataset with associated images of the typical and abnormal tomato plant leaves. Considered in this work were the various labels for healthy and diseased tomato leaves, such as early blight, Bacterial spot, late bright mold, healthy, etc. Since deep CNN models have shown notable machine vision results, they are used in this case to diagnose and categorise plant illnesses from their leaves. As a result, the proposed CNN models can thus now be evaluated from confusion matrix using data analysis criteria, primarily focusing on metrics for evaluation like training and validation accuracy, loss, Recall, Precision, F1 score, processing speed, and performance.

A deep learning-based mobile app system for visual identification of tomato plant disease

&lt;span lang="EN-US"&gt;Tomato is one of many horticulture crops in Indonesia which plays a vital role in supplying public food needs. However, tomato is a very susceptible plant to pests and diseases caused by bacteria and fungus. The infected diseases should be isolated as soon as it was detected. Therefore, developing a reliable and fast system is essential for controlling tomato pests and diseases. The deep learning-based application can help to speed up the identification of tomato disease as it can perform direct identification from the image. In this research, EfficientNetB0 was implemented to perform multi-class tomato plant disease classification. The model was then deployed to an android-based application using machine learning (ML) kit library. The proposed system obtained satisfactory results, reaching an average accuracy of 91.4%.&lt;/span&gt;

Advanced Hybrid Model for Multi Paddy diseases detection using Deep Learning

INTRODUCTION: Rapid developments in deep learning (DL) techniques have made it possible to find and recognize objects in pictures. To create a network that is significantly more successful than a single CNN, GAN, RNN, etc., we can mix various neural network models (CNN, GAN, RNN).this combination is known as hybrid model. Hybrid model of deep leaning is give more accurately result for detection and identification of paddy diseases.&#x0D; OBJECTIVES: I have studies outcome of hybrid model 1(DCNN+SVM) and Hybrid model 2 (DCNN + Transfer Learning) to increase accuracy of Rice plant disease detection and classification. The Researched model detects multiple rice plant diseases and it is giving same result in multiple data sets.&#x0D; METHODS: The Proposed System have used Deep Learning Image Processing algorithm and neural Network Like DCNN ,SVM and Transfer Learning .The brand new model is DST where D stands for DCNN, S stands for SVM and T stands for transfer learning.&#x0D; RESULTS: The Researched DST model achieved 95% Training accuracy and 85% validation Accuracy. The Researched model detect multiple rice plant diseases and it is giving same result in multiple data set.&#x0D; CONCLUSION: The proposed model combined 2 existing model and developed hybrid model that a detect various rice plant diseases with better accuracy from available existing model.

Detection and Classification of Plant Diseases in Crops (Solanum lycopersicum) due to Pests Using Deep Learning Techniques: A Review

All people have access to food thanks to agriculture, even in areas with rapid population expansion. In order to ensure that the entire population has access to food, early diagnosis of plant diseases is recommended. However, when crops are still maturing, it might be challenging to predict diseases. The goal of this paper is to inform farmers about the use of machine learning to recognize various pest species in various crop plants. Deep learning establishes a continuing, cutting-edge method for analysing images with significant potential and promising outcomes. DL has expanded into the field of agriculture after demonstrating its effectiveness in a number of applications. Here, we reviewed various research articles that used deep learning methods to address diverse research issues in tomato (Solanum lycopersicum) plants. We look at the study areas in tomato plants where deep learning, data preprocessing, transfer learning, and augmentation approaches are performed. studied dataset details, including the number of photos, classes, and train test to validation ratios that were employed. We also look at comparisons made across different deep learning architectures and analyse the results. The results demonstrated that deep learning techniques beat all other image processing methods, however how well DL works depended on the dataset employed.