Plant Disease Diagnosis Research Articles

An ensemble of deep learning architectures for accurate plant disease classification

A substantial fraction of agricultural produce loss can be attributed to plant diseases. Agricultural yield loss can have far-reaching consequences for a country's economy and contribute to global food insecurity. Early detection of plant diseases can be instrumental in maintaining global health and welfare. A pathologist's visual evaluation is typically used to make an early diagnosis of plant diseases. This technique involves experts or farmers examining plants with the naked eye and classifying the disease depending on their previous experience. This conventional approach includes drawbacks like low accuracy and the need for human expertise. This motivates researchers to investigate automated systems for the early diagnosis of plant diseases.To achieve this goal an ensemble of different deep learning architectures (DenseNet201, efficientNetB0, inceptionresnetV2, efficientNetB3) is introduced to increase the classification accuracy of plant leaf diseases. In this work, a novel image-processing technique is proposed to increase the efficiency of deep-learning models. Also, a data balancing technique is used to solve the problem of the imbalanced dataset. Five different deep-learning models are trained and tested using the largest plant disease dataset; PlantVillage. Ten different ensembles (chosen randomly) of the deep learning models are tested and compared to find the ensemble with the highest accuracy.The proposed ensemble model was able to achieve 99.89% accuracy on the New PlantVillage dataset. PlantVillage is a challenging dataset with 38 classes. Achieving high accuracies on such a dataset proves the ability of the system to generalize on unseen data or real-world scenarios. A comparison with the state-of-the-art is made with other available models from the literature. A section about this is added to show the superior performance of the proposed ensemble model in terms of accuracy and F1-score.

Presymptomatic Leaf Reflectance of Fusarium virguliforme-Infected Soybean Plants in Greenhouse Conditions

Sudden death syndrome of soybean is caused by a soilborne pathogen, Fusarium virguliforme. Prior to visible foliar symptoms, a destructive technique can be carried out to diagnose root infection. Hyperspectral sensors can be a presymptomatic and nondestructive alternative for plant disease diagnosis. This study was designed to relate leaf spectral reflectance to F. virguliforme root infection in the absence of foliar symptoms. Soybean plants were grown under controlled greenhouse conditions. The spectral reflectance of the plants was measured weekly beginning at 21 days after transplanting up until 42 days after transplanting using a swing hyperspectral imaging system fixed on a gantry. Destructive root sampling confirmed F. virguliforme root infection using real-time PCR. The most relevant wavelengths for discrimination were selected using the ReliefF algorithm. Three machine learning models (partial least squares discriminant analysis, support vector machine, and random forest) were evaluated for classification accuracy using the selected wavelengths. Relevant wavelengths for differentiating between the healthy and F. virguliforme-infected plants were found in the visible and red-edge region from 500 to 750 nm and the shortwave infrared region from 1,400 to 2,350 nm. In the absence of visible foliar symptoms, classification results showed over 79% mean F1 scores for all models. Partial least squares discriminant analysis was able to differentiate healthy and F. virguliforme-infected plants with a mean F1 score of 83.1 to 85.3% and a kappa statistic of 0.43 to 0.54. This work supports the use of hyperspectral remote sensing for early presymptomatic disease diagnosis under a controlled environment. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Various tomato infection discrimination using spectroscopy

Diagnosing plant diseases is a difficult task, but it could be made easier with the use of advanced instrumentation and the latest machine learning techniques. This paper is a further development of a previous authors study by the authors, which has been extended to provide the classification method for tomato diseases and to indicate the spectral ranges of greatest importance for this process. As tomatoes are one of the most popular and consumed vegetables, and diseases of this crop even reduce yields by up to 80% every year, their detection is a vague topic. This manuscript describes research in which spectroscopy was used to develop methods for discriminating between selected tomato diseases. The following, frequently occurring diseases were investigated for this research: anthracnose, bacterial speck, early blight, late blight, and Septoria Leaf Spot. The study used a dataset consisting of 3877 measurements taken with the ASD FieldSpec 4 Hi-Res spectroradiometer in the 350–2500 nm range from 2019/09/10 to 2019/12/20. The highest classification efficiency (F1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$F_1$$\\end{document} score) of 0.896 was obtained for the logistic regression based model which was evaluated on Septoria Leaf Spot disease records.

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.

A deep learning approach for Maize Lethal Necrosis and Maize Streak Virus disease detection

Maize is an important crop cultivated in Sub-Saharan Africa, essential for food security. However, its cultivation faces significant challenges due to debilitating diseases such as Maize Lethal Necrosis (MLN) and Maize Streak Virus (MSV), which can lead to severe yield losses. Traditional plant disease diagnosis methods are often time-consuming and prone to errors, necessitating more efficient approaches. This study explores the application of deep learning, specifically Convolutional Neural Networks (CNNs), in the automatic detection and classification of maize diseases. We investigate six architectures: Basic CNN, EfficientNet V2 B0 and B1, LeNet-5, VGG-16, and ResNet50, using a dataset of 15344 images comprising MSV, MLN, and healthy maize leaves. Additionally, We performed hyperparameter tuning to improve the performance of the models and Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability. Our results show that the EfficientNet V2 B0 model demonstrated an accuracy of 99.99% in distinguishing between healthy and disease-infected plants. The results of this study contribute to the advancement of AI applications in agriculture, particularly in diagnosing maize diseases within Sub-Saharan Africa.

Advancements in Crop Disease Detection: Analytical Methods for Recognizing Disease Stages through Leaf Analysis

Plant diseases have the potential to damage the livelihoods of farmers and impede their capacity to generate an adequate quantity of food. Diagnosis and early detection of plant diseases are critical for their effective management and control. Leaf analysis shows great potential as a method for forecasting disease stages due to its ability to detect subtle alterations in leaf physiology and appearance that may occur prior to the onset of conspicuous symptoms. By utilizing the algorithms of machine learning and deep learning, it is possible to classify characteristics extracted from photographs of leaves into distinct disease stages. Recent studies have demonstrated the potential of these algorithms, as they have achieved remarkable accuracy in disease stage prediction despite having limited training data. The integration of machine learning and deep learning techniques with foliage analysis holds promise for revolutionizing plant disease management through the provision of timely identification, accurate diagnosis, and customized treatment. In order to formulate efficacious disease management strategies, precise determination of the developmental stage of plant diseases is imperative. Scholars are presently devising novel approaches to identify the stages of plant diseases by employing diverse methodologies, including spectroscopy, machine learning, and image processing. This can benefit producers in substantial economic and environmental ways, as well as contribute to the improvement of food security. The primary investigation comprises an assortment of articles spanning the years 2014 to 2023. After evaluating various search strategies, a total of 117 research publications were identified, of which 43 were pertinent. The article examines numerous developments in deep learning research. In addition, it will facilitate the assessment of the present and prospective state of plant disease research by employing deep learning methodologies.

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

A Deep Learning-based System for Automated Plant Disease Severity Detection and Fertilizer Optimization

Abstract: Because food is a basic requirement for all living beings on Earth, agriculture is especially important in our daily life. Agriculture is our main source of food. In addition to plant diseases that impede the growth and quality of food crops, agriculture works to generate food to feed the growing population. We'll look at five plants: bitter gourd, mango tree, spinach, tomato, and hibiscus. This study proposes a CNN-based technique for early plant disease diagnosis. The approach consisted of three steps: image segmentation, feature extraction, and picture pre-processing. The results of these three processes are combined to form a Convolutional Neural Network (CNN) classifier. To research and analyse a plant, an input image of the damaged portions is obtained and compared to the desired dataset. The disease is then anticipated, along with therapeutic treatments. Once the disease has been recognized, the quantity of pesticides, recommended application place, and chemicals themselves will be displayed. It will also identify the nearest location where pesticides are available.

Detecting Three Different Diseases of Plants by Using CNN Model and Image Processing

Agricultural output is critical to Pakistan's economy, and plant disease detection is critical for both the environment and human health. This work presents a CNN-based approach for detecting plant diseases, which is evaluated on sample photos to assess the temporal complexity and infected area. The model was given three disease cases: corn common rust, tomato bacterial spot, and potato early blight. Using the CNN algorithm, the CNN model attained an accuracy of 95.55% for tomato bacterial spot, 96.72% for Corn Common Rust, and 97.63% for Potato Early Blight. This method may aid in the diagnosis and treatment of plant diseases.

LWDN: lightweight DenseNet model for plant disease diagnosis

LWDN: lightweight DenseNet model for plant disease diagnosis

Chilly Leaves Diseases Identification Using MobileNet V2 Deep Learning Model

This work describes the application of pre-trained deep learning models (Mobile Net V1, Mobile Net V2) to image classification issues such as plant disease diagnosis. Regarding this, We provide a deep learning-based feature extraction technique for classifying leaf diseases of cold plants and identifying plant species. The automated diagnosis of plant diseases has brought about a transformation in the agriculture industry. Our main goal is to use neural networks for agricultural disease diagnosis and treatment. Plant diseases seriously impair the agriculture industry's financial standing. Managing an illness is a challenging endeavor. Usually, infections appear on the leaves or stems of plants, as do their symptoms, which include colorful spots or steaks. Compared to human labor, image processing technology enables faster and more accurate illness detection. Image processing is critical in the detection of plant diseases because it gives the best results with the least amount of human intervention. Deep learning helps to diagnose diseases and provide focused treatment.

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.

Plant Disease Detection Using Machine Learning

Agriculture plays a crucial role in sustaining societies worldwide, and effective crop management is paramount for food security. Plant diseases pose significant threats to agricultural productivity, making timely detection imperative. Leveraging advancements in deep learning and machine vision, this research explores the application of Convolutional Neural Networks (CNNs) to detect tomato leaf diseases. A novel dataset comprising images of diseased and healthy tomato leaves is introduced and utilized for model training. By employing the Inception V3 architecture and data augmentation techniques, the proposed CNN model demonstrates promising results in disease classification. This study presents a framework for leveraging AI/ML techniques to enhance plant disease diagnosis and contribute to global food security efforts. Keywords: Plant Disease Detection, Deep Learning, Convolutional Neural Networks, Inception V3, Agriculture, Tomato Leaf Diseases.

Enhanced deep learning technique for sugarcane leaf disease classification and mobile application integration

With an emphasis on classifying diseases of sugarcane leaves, this research suggests an attention-based multilevel deep learning architecture for reliably classifying plant diseases. The suggested architecture comprises spatial and channel attention for saliency detection and blends features from lower to higher levels. On a self-created database, the model outperformed cutting-edge models like VGG19, ResNet50, XceptionNet, and EfficientNet_B7 with an accuracy of 86.53%. The findings show how essential all-level characteristics are for categorizing images and how they can improve efficiency even with tiny databases. The suggested architecture has the potential to support the early detection and diagnosis of plant diseases, enabling fast crop damage mitigation. Additionally, the implementation of the proposed AMRCNN model in the Android phone-based application gives an opportunity for the widespread use of mobile phones in the classification of sugarcane diseases.

Methods and tools for diagnosis of plant diseases: the case of tip rot of carrot

Methods and tools for diagnosis of plant diseases: the case of tip rot of carrot

Artificial Intelligence Plant Doctor: Plant Disease Diagnosis Using GPT4-vision

Integrated pest management is essential for controlling plant diseases that reduce crop yields. Rapid diagnosis is crucial for effective management in the event of an outbreak to identify the cause and minimize damage. Diagnosis methods range from indirect visual observation, which can be subjective and inaccurate, to machine learning and deep learning predictions that may suffer from biased data. Direct molecular-based methods, while accurate, are complex and time-consuming. However, the development of large multimodal models, like GPT-4, combines image recognition with natural language processing for more accurate diagnostic information. This study introduces GPT-4-based system for diagnosing plant diseases utilizing a detailed knowledge base with 1,420 host plants, 2,462 pathogens, and 37,467 pesticide instances from the official plant disease and pesticide registries of Korea. The AI plant doctor offers interactive advice on diagnosis, control methods, and pesticide use for diseases in Korea and is accessible at https://pdoc.scnu.ac.kr/.

Plant Disease Detection Using YOLOv7 Algorithm

Abstract: Plant diseases pose a serious danger to global food security and can result in huge financial losses for the agricultural sector. Earlier Plant disease detection and precise diagnosis are essential for putting management measures into place. Recent advancements in computer vision techniques have demonstrated encouraging outcomes in automating activities related to illness identification. This study uses the You Only Look Once (YOLOv7) object identification algorithm to present a novel method for plant disease diagnosis. The primary goal of this research is to create a reliable and effective system that can quickly and reliably identify plant diseases. YOLOv7, a highly accurate and speedy algorithm, will serve as the main underpinning for detection. The project's primary goal is to train the Yolov7 model to identify distinct citrus plant illnesses by using a large dataset that includes pictures of both healthy and diseased plants. This project categorizes leaf images recorded from a file or webcam into four categories: healthy, greening, blackspot, and canker. Early disease prediction allows farmers to take required security measures for their plants.

Automatic recognition of Rice Plant leaf diseases detection using deep neural network with improved threshold neural network

The farming industry widely requires automatic detection and analysis of rice diseases to avoid wasting financial and other resources, reduce yield loss, improve processing efficiency, and obtain healthy crop yields. The progress made in deep learning techniques has significantly impacted agricultural disease diagnostics. Rice Plant leaf diseases can have severe adverse effects on crop yield, and proper diagnosis of Rice Plant diseases is vital to avoid these effects. Most of the existing methods do not adequately detect the leaf images and cannot find the condition accurately. Previously, there were some problems with leaf segmentation. Complex disease stages are efficient but computationally time-consuming, and segmentation needs to be more accurate, affordable, and reliable. To overcome the issues, the proposed method of Deep Spectral Generative Adversarial Neural Network (DSGAN2) with Improved Artificial Plant Optimization for Rice Plant leaf disease detection. Initially, we fed into the input of healthy and non-healthy leaves from the collected dataset. Then, we apply an Improved Threshold Neural Network (ITNN) method to enhance the image quality. Next, it uses a Segmentation using a Segment Multiscale Neural Slicing (SMNS) algorithm to identify the support-intensive color saturation based on the enhanced image. After that, the Spectral Scaled Absolute Feature Selection (S2AFS) method is applied to select optimal features and the Closest Weight from segmented Rice Plant leaves. Social Spider Optimization to select the Feature with the Closest Weight (S2O-FCW) algorithm for analysis of the feature weight values. Finally, the proposed Soft-Max Logistic Activation Function with Deep Spectral Generative Adversarial Neural Network (DSGAN2) algorithm detects Rice Plant disease based on selected features. The proposed system Deep Spectral Generative Adversarial Neural Network (DSGAN2) produces a decreasing false rate compared to the existing system of ACPSOSVM-Dual Channels Convolutional Neural Network (APS-DCCNN) is 55.2 %, Alex Net is 50.4 %, and Convolutional Neural Network (CNN) is 49.5 %.

Potato leaf disease detection with a novel deep learning model based on depthwise separable convolution and transformer networks

Early diagnosis of plant diseases is essential in reducing economic losses for farmers and increasing production efficiency. Therefore, Computer-Aided Diagnosis (CAD) systems supported by artificial intelligence technologies can be developed to help diagnose diseases quickly and accurately by examining the symptoms and signs in plant leaves. In this study, Multi-head Attention Mechanism Depthwise Separable Convolution Inception Reduction Network (MDSCIRNet) architecture, an image-based deep convolutional neural network, is proposed for classifying potato leaf diseases. The main components of the MDSCIRNet architecture are depthwise separable convolution (DSC) and a multi-head attention mechanism. The proposed architecture has been compared with modern algorithms developed with DSC technology, such as Xception, MobileNet, and deep learning algorithms, such as ResNet101, InceptionV3, and EfficientNetB2, to evaluate its performance in the classification process. In addition, hybrid methods developed with the classical machine learning algorithms Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Adaptive Boosting (AdaBoost), and MDSCIRNet model, integrated deep learning model, hard voting ensemble learning model. Suggested methods such as these were also used in the experimental process. Moreover, techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE), Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), and Hypercolumn were used to improve the image quality of the data set. In the experimental process, the MDSCIRNet deep learning architecture achieved 99.24% accuracy in the study using the original dataset. While a 99.11% accuracy rate was achieved with the integrated deep learning model and hard voting ensemble learning model, the highest success rate of 99.33% was performed in the study conducted with the MDSCIRNet + SVM method. This study contributes to developing new and effective strategies in the agricultural industry for the early diagnosis and control of potato plant diseases. Machine learning-based approaches offer the potential to minimize economic losses and increase productivity in production by allowing farmers to intervene early.