Plant Leaf Images Research Articles

The role of ascorbate redox turnover in iron toxicity tolerance

Iron (Fe) toxicity is a major abiotic stress in lowland rice production. Breeding tolerant varieties has proven challenging due to the complex genetic architecture of Fe toxicity tolerance and the strong genotype-by-environment interactions. Additionally, conventional methods for phenotyping visible stress symptoms are often inaccurate, inconsistent, and lack reproducibility.In our previous work, we identified that ascorbate redox regulation, mediated by the activities of dehydroascorbate reductase (DHAR) and ascorbate oxidase (AO), contributed to high tolerance in an indica rice genotype across various environments. To explore whether this mechanism is common among other rice genotypes, we selected ten genotypes with contrasting stress symptoms under Fe-toxic conditions to examine the roles of DHAR and AO in regulating Fe toxicity tolerance. Additionally, we aimed to develop objective and accurate image-based phenotyping methods to replace the traditional leaf bronzing scoring method.Among the ten genotypes we tested, we found significant positive correlations between DHAR activity and stress symptoms in plants grown under both Fe toxicity and control conditions, suggesting a general link between ascorbate redox regulation and Fe toxicity tolerance. Using RGB signals from leaf images of plants exposed to 1000 mg/L Fe2+, we evaluated 36 different color indices to quantify stress symptoms. We identified the normalized green‒red difference index as most significant in quantifying stress symptoms under Fe toxicity conditions.Our findings suggest that DHAR activity could be potentially employed as a biomarker in the screening of rice germplasms and breeding tolerant cultivars to Fe toxicity.

IMAGE PROCESSING-DRIVEN DEEP LEARNING MODEL FOR PLANT DISEASE DETECTION TO ENHANCE IRRIGATION EFFICIENCY IN SMART AGRICULTURE

In the era of smart agriculture, efficient irrigation management is crucial for optimizing crop yield and resource use. Traditional methods of plant disease detection often rely on manual inspection, which is time-consuming and prone to errors. Smart agriculture leverages technology to improve agricultural practices. Accurate and timely plant disease detection is vital for effective irrigation management and overall crop health. Current methods are limited by their manual nature and inability to process large volumes of data quickly. Manual plant disease detection is labor-intensive and may not provide timely information, leading to inefficient irrigation practices. This inefficiency can result in reduced crop yield and wasted resources. LeNet integrates advanced image processing techniques with a deep learning architecture tailored for plant disease detection. The model utilizes convolutional neural networks (CNNs) to analyze plant leaf images, identifying disease patterns with high precision. LeNet incorporates preprocessing steps such as image normalization and augmentation to enhance model robustness. The network is trained on a comprehensive dataset of plant disease images, employing transfer learning to leverage pre-trained weights for improved accuracy. Evaluation of LeNet on a test dataset comprising 10,000 images demonstrated an impressive accuracy of 92.5%, with a precision of 90.3% and recall of 94.1%. The model significantly outperforms traditional methods, reducing disease detection time by 60% and enhancing irrigation efficiency by 30%. The reduction in water usage and increased crop yield were observed in practical trials.

ICS-ResNet: A Lightweight Network for Maize Leaf Disease Classification

The accurate identification of corn leaf diseases is crucial for preventing disease spread and improving corn yield. Plant leaf images are often affected by factors such as complex backgrounds, climate, light, and sample data imbalance. To address these issues, we propose a lightweight convolutional neural network, ICS-ResNet, based on ResNet50. This network incorporates improved spatial and channel attention modules as well as a deep separable residual structure to enhance recognition accuracy. (1) The residual connections in the ResNet network prevent gradient loss during deep network training. (2) The improved channel attention (ICA) and spatial attention (ISA) modules fully utilize semantic information from different feature layers to accurately localize key features of the network. (3) To reduce the number of parameters and lower computational costs, we replace traditional convolutional computation with a depth-separable residual structure. (4) We also employ cosine annealing to dynamically adjust the learning rate, enhancing the network’s training stability, improving model convergence, and preventing local optima. Experiments on the corn dataset in Plant Village compare the proposed ICS-ResNet with eight popular networks: CSPNet, InceptionNet_v3, EfficientNet, ShuffleNet, MobileNet, ResNet50, ResNet101 and ResNet152. The results show that the ICS-ResNet achieves an accuracy of 98.87%, which is 5.03%, 3.18%, 1.13%, 1.81%, 1.13%, 0.68%, 0.44% and 0.60% higher than the other networks, respectively. Furthermore, the number of parameters and computations are reduced by 69.21% and 54.88%, respectively, compared to the original ResNet50 network, significantly improving the efficiency of corn leaf disease classification. The study provides strong technical support for sustainable agriculture and the promotion of agricultural science and technology innovation.

Transfer learning by VGG-16 with convolutional neural network for paddy leaf disease classification

ABSTRACT One of the most recent areas of research in agriculture is the detection along with classification of diseases from plant leaf images. Since rice is the most widely consumed staple food worldwide, it is crucial to increase paddy production’s quality and quantity. In the production of paddy, early detection of pests and diseases at various growth stages is very important. Utilising image processing scheme to spot diseases of agricultural plants will lessen the need for farmers to safeguard agricultural products. The use of VGG-16 along with a Convolutional Neural Network (CNN) for the classification and recognition of paddy leaf diseases is proposed in this work. The rice plant leaves images taken from the kaggle dataset is utilised for the purpose of image acquisition. The Gaussian filter is used in pre-processing. The clustering technique is utilised for the segmentation of the diseased part, the normal part, and the surroundings. The proposed model is then utilised for disease classification. This study classifies 10 categories of paddy images. VGG16, InceptionV3, MobileNetV2, and ResNet-152, among other transfer learning methods, are evaluated and contrasted with the experimental results. The outcome accuracy of the proposed model achieves of nearly 99.9%.

An effective hybrid attention model for crop yield prediction using IoT-based three-phase prediction with an improved sailfish optimizer

Internet of Things (IoT)-based smart agriculture approaches have been adopted to achieve promising results in terms of food productivity. Moreover, high cost and technological complexity are major problems in existing agricultural management systems. Thus, a new IoT-based smart agriculture farming model is implemented in three phases namely crop detection, crop disease detection, and crop yield prediction. In the first phase, the plant leaf images are gathered from the Kaggle source PlantifyDr. The collected images are passed to the deep feature retrieval phase, where the Visual Geometry Group (VGG16) is utilized to calculate feature (f1). Then, a Hybrid Attention-based Crop Type Detection Network (HA-CTDecNet) is used for detecting the crop types, in which the parameters are optimized by Attack Power Improved Sailfish Optimizer (API-SFO). In the second step of crop disease detection phase, the input images are fed to Otsu Thresholding for Abnormality segmentation. The deep attributes from the segmented images are haul out using VGG16 and attain the feature (f2). The extracted features are forwarded to the Hybrid Attention-based Crop Disease Detection Network (HA-CDDecNet), where the constraints are optimized using API-SFO. In the third crop yield prediction phase, the data related to crop location are collected from the crop yield forecast dataset. Then, the collected data and the extracted features (f1) and (f2) are concatenated and fed to the Principle Component Analysis (PCA). Then, these features are given to the Hybrid Attention-based Crop Yield Prediction Network (HA-CYPreNet) with the parameter optimization of API-SFO. The investigation is accepted to verify the effectiveness of the suggested IoT-based smart agriculture model with the traditional approaches by considering distinct measures. The Mean Absolute Error (MAE) of the crop yield prediction model is improved with 35% than CNN, 27.5% than LSTM, 17.5% than GRU, and 7.5% than LSTM_GRU while considering the learning percentage value as 55.

Lightweight convolutional neural network-based plant disease identification for protection and landscape design

Plant diseases significantly impact landscape design, necessitating comprehensive consideration and effective management measures to ensure the health, aesthetics, and sustainability of landscapes. Early detection and timely control of plant diseases are crucial, but traditional monitoring methods, which rely on manual observation and sample collection, are inadequate for covering large garden areas and may delay necessary treatments. This study addresses these challenges by constructing a small Rosa chinensis disease dataset through field collection and data augmentation techniques. We propose MixResCoAtNet, an improved model based on the lightweight MixNet framework, to identify and categorize diseases from plant leaf images using convolutional neural networks (CNNs). Comparison experiments with various classical convolutional network models demonstrate that MixResCoAtNet outperforms these models, offering more competitive performance. Additionally, due to its lighter structure, MixResCoAtNet shows greater potential for deployment on mobile devices, facilitating real-time and efficient plant disease monitoring and management in landscape design.

From leaf to harvest: achieving sustainable agriculture through advanced disease prediction with DBN-EKELM.

In the agricultural sector, the early identification of plant diseases presents a pressing challenge. Throughout the growing season, plants remain vulnerable to an array of diseases. Failure to detect these diseases at their early stages can significantly compromise the overall yield, thereby reducing profitability for farmers. To address this issue, several researchers have introduced standard methods that leverage machine learning and deep learning techniques. However, many of these methods offer limited classification accuracy and often necessitate extensive training parameter adjustments. The objective of this study is to develop a new deep learning-based technique for detecting and classifying plant diseases at earlier stages. Thus, this paper introduces a novel technique known as the deep belief network-based enhanced kernel extreme learning machine (DBN-EKELM) that identifies a disease automatically and performs effective classification. The initial phase involves data preprocessing to enhance quality of plant leaf images, facilitating the extraction of critical information. With the goal of achieving superior classification accuracy, this paper proposes the use of the DBN-EKELM technique for optimal plant leaf disease detection. Given that KELM parameters are highly sensitive to minor variations, proper parameter tuning is essential and introduces a novel binary gaining sharing knowledge-based optimization algorithm (NBGSK). The efficacy of the proposed DBN-EKELM method is evaluated by comparing its performance with other conventional methods, considering various measures like accuracy, precision, specificity, sensitivity and F-measure. Experimental analyses demonstrate that the DBN-EKELM technique achieves an impressive rate of approximately 98.2%, 97%, 98.1%, 97.4% as well as 97.8%, surpassing other standard methods. © 2024 Society of Chemical Industry.

DeepLeaf: Automated Plant Disease Diagnosis using Deep Learning Approach

Automated diagnosis of plant diseases is critical for ensuring food security and agricultural sustainability. In this study, we propose DeepLeaf, a novel deep-learning framework for the automated recognition and classification of plant diseases. DeepLeaf leverages convolutional neural networks to analyze plant leaf images and accurately identify disease symptoms. The framework is trained on a large dataset of annotated images, encompassing a wide range of plant species and disease types. Through extensive experimentation, we demonstrate the effectiveness of DeepLeaf in accurately diagnosing plant diseases across diverse environmental conditions and varying degrees of disease severity. Our results show that DeepLeaf achieves high accuracy and robustness, outperforming traditional methods and commercial systems in speed and reliability. Furthermore, DeepLeaf is designed to be easily deployable in real-world agricultural settings, enabling farmers and agronomists to identify and mitigate plant diseases quickly, thus refining crop yield and dropping economic losses.

PLANT DISEASE RECOGNITION USING VGG-16

Agriculture and modern farming is one of the fields where IoT and automation can have a great impact. Maintaining healthy plants and monitoring their environment in order to identify or detect diseases is essential in order to maintain a maximum crop yield. The implementation of current high rocketing technologies including artificial intelligence (AI), machine learning, and deep learning has proved to be extremely important in modern agriculture as a method of advanced image analysis domain. Artificial intelligence adds time efficiency and the possibility of identifying plant diseases, in addition to monitoring and controlling the environmental conditions in farms. Several studies showed that machine learning and deep learning technologies can detect plant diseases upon analyzing plant leaves with great accuracy and sensitivity. In this study, considering the worth of machine learning for disease detection, we present a convolutional neural network VGG-16 model to detect plant diseases, to allow farmers to make timely actions with respect to treatment without further delay. To carry this out, 19 different classes of plants diseases were chosen, where 15,915 plant leaf images (both diseased and healthy leaves) were acquired from the Plant Village dataset for training and testing. Based on the experimental results, the proposed model is able to achieve an accuracy of about 95.2% with the testing loss being only 0.4418. The proposed model provides a clear direction toward a deep learning-based plant disease detection to apply on a large scale in future. Keywords—Machine learning; VGG-16; disease detection; convolutional networks; Plant Village; modern farming. Keywords—Machine learning; VGG-16; disease detection; convolutional networks; Plant Village; modern farming.

REVOLUTIONIZING BIOLOGY LEARNING THROUGH AR: THE CASE OF LEAFCAPTURE APPLICATION DEVELOPMENT

REVOLUTIONIZING BIOLOGY LEARNING THROUGH AR: THE CASE OF LEAFCAPTURE APPLICATION DEVELOPMENT

Deep neural network-based plant protection strategy in rural garden landscape construction

Rural landscaping enhances the quality of the environment in rural areas by planting trees and flowers and plants and at the same time can contribute to the protection of plants in rural areas. The occurrence and development of landscape plant diseases are very insidious, which may lead to poor tree growth and affect the annual growth of timber, or even lead to the death of the whole forest and the reverse succession of the forest. Plant leaf disease lesion location is random, and plant leaf image shooting is susceptible to light, background, angle and other objective factors can only rely on artificial identification, but the traditional artificial identification and physiological information extraction methods such as low accuracy, low efficiency and cumbersome. The rapid development of deep neural networks provides an effective solution for plant leaf recognition based on image analysis. In this paper, a DCA-ResNet model is constructed based on ResNet101 to recognize plant leaves by introducing the attention mechanism and optimizing the model structure. The validation of the DCA-ResNet model using public datasets on three species of plant leaves and plant leaf diseases achieved accuracy rates of 96.79%, 96.64%, and 96.44%, respectively.

Multi-classification of disease induced in plant leaf using chronological Flamingo search optimization with transfer learning.

Agriculture is imperative research in visual detection through computers. Here, the disease in plants can distress the quality and cultivation of farming. Earlier detection of disease lessens economic losses and provides better crop yield. Detection of disease from crops manually is an expensive and time-consuming task. A new scheme is devised for accomplishing multi-classification of disease using plant leaf images considering the chronological Flamingo search algorithm (CFSA) with transfer learning (TL). The leaf image undergoes pre-processing using Adaptive Anisotropic diffusion to discard noise. Here, the segmentation of plant leaf is done with U-Net++, and trained by the Moving Gorilla Remora algorithm (MGRA). The image augmentation is further applied considering two techniques namely position augmentation and color augmentation to reduce data dimensionality. Thereafter the feature mining is done to produce crucial features. Next, the classification in terms of the first level is considered for classifying plant type and classification in terms of the second level is done to categorize disease using convolutional neural network (CNN)-based TL with LeNet and it undergoes training using CFSA. The CFSA-TL-based CNN with LeNet provided better accuracy of 95.7%, sensitivity of 96.5% and specificity of 94.7%. Thus, this model is better for earlier plant leaf disease detection.

Multilevel detection and classification of diseased plant leaf images using high-resolution superlet transform and E-ResNet

Multilevel detection and classification of diseased plant leaf images using high-resolution superlet transform and E-ResNet

Fusion of AI Techniques: A Hybrid Approach for Precise Plant Leaf Disease Classification

Classification of plant leaf diseases is an important step in protecting the world's food supply and agricultural yields. There has been encouraging progress in improving the efficiency and accuracy of plant leaf disease diagnosis via the combination of deep learning methods with artificial intelligence (AI) in recent years. This research introduces a new hybrid strategy, CNN+SVM and CNN+RF, that uses deep learning techniques like Convolutional Neural Networks (CNN) in conjunction with more traditional machine learning algorithms like Random Forest (RF) and Support Vector Machine (SVM). Moreover, two hybrid variants, CNN + RF and CNN + SVM, are proposed to exploit the strengths of both paradigms synergistically. To further improve classification accuracy, the study employs Particle Swarm Optimization (PSO) as a feature selection technique. PSO optimizes the feature subset for each classification model, facilitating the extraction of the most informative features, which leads to better discrimination between healthy and diseased plants. The dataset used for experimentation consists of a comprehensive collection of plant leaf images representing various diseases across multiple plant species. Experimental results demonstrate the efficacy of the proposed hybrid approach compared to individual classification methods. The hybrid models achieve higher accuracy rates and improved generalization performance, showcasing the synergistic benefits of combining AI and deep learning techniques. Furthermore, the feature selection process through PSO contributes significantly to enhancing the classification outcomes, providing insights into the discriminative power of selected features. This research contributes to the advancement of plant leaf disease classification methodologies by offering an innovative hybrid approach that leverages the complementary strengths of AI, deep learning, and feature selection techniques. The study's findings underscore the potential for improving plant leaf disease management strategies, ultimately leading to enhanced crop productivity and sustainable agriculture. The proposed hybrid framework can serve as a blueprint for similar classification tasks in other domains, demonstrating the broader impact of synergizing different AI techniques for improved accuracy and performance. CNN+RF gives 95% accuracy, 93% precision, 96% recall and 94% F1 score, whereas CNN+SVM gives 93% accuracy, 91% precision, 94% recall and 92% F1 score.

PLANT DISEASE DETECTION BY USING MOBILENTV2 AND XCEPTION ON FILTERED AND ENHANCED IMAGES

The gathering, sorting, and processing of plant leaf images serves as the foundation for this study. These are crucial first steps in the plant health monitoring process that guarantee reliable findings. The work classifies and detects plant leaf photos, extracting data on plant health using state-of-the-art deep learning models like Xception and MobileNetV2. In order to assess the effectiveness of the system, additional filters are applied to the photos of plant leaves in order to adjust characteristics like brightness, contrast, sharpness, and blur. The study's results show that the deep learning models employed could accurately determine the health of plant leaves, offering important new information for related future research.

Enhanced symbiotic organism search optimization algorithm for plant disease classification

Since it satisfies all prerequisites for the growth of humanity, agriculture is currently regarded as being the most significant sector for civilization. One of the main forms of human energy production is thought to be plants, which also provide nutrients, cures, etc. Any damage or disease brought on by exposure to pathogens, viruses, bacteria, etc., while cultivating plants results in a decline in productivity, making it crucial to prevent such diseases and take the required precautions to avoid them. Accurately identifying such fatal diseases is a crucial first step for both the businesses and farmers. Six different Convolutional Neural Networks (CNNs) that accept plant leaf images as input, along with the Enhanced Symbiotic Organism Search (ESOS) optimization algorithm, have been implemented in our research. We intend to extensively contrast the various models based on accuracy, precision, recall, and F1-score. In the area of image recognition and classification, convolutional neural networks (CNNs), in particular, and deep learning, in general, are developing. The literature contains a variety of CNN designs. The dataset size, the number of classes, the model’s weights, hypermeters, and optimizers are a few examples of the variables that have an impact on a CNN model’s performance. Because of its benefits, transfer learning and fine-tuning a pre-trained model are now very popular. This study examines the impact of six popular CNN models: DenseNet, MobileNet, EfficientNet, VGG19, ResNet and Inception. As a result, DenseNet demonstrates an optimal accuracy rate of 98% when compared to other models.

Geometric entropy of plant leaves: A measure of morphological complexity.

Shape is an objective characteristic of an object. A boundary separates a physical object from its surroundings. It defines the shape and regulates energy flux into and from an object. Visual perception of a definite shape (geometry) of physical objects is an abstraction. While the perceived geometry at an object's sharp interface (macro) creates a Euclidian illusion of actual shape, the notion of diffuse interfaces (micro) allows an understanding of the realistic form of objects. Here, we formulate a dimensionless geometric entropy of plant leaves (SL) by a 2-D description of a phase-field function. We applied this method to 112 tropical plant leaf images. SL was estimated from the leaf perimeter (P) and leaf area (A). It correlates positively with a fractal dimensional measure of leaf complexity, viz., segmental fractal complexity. Leaves with a higher P: A ratio have higher SL and possess complex morphology. The univariate cluster analysis of SL reveals the taxonomic relationship among the leaf shapes at the genus level. An increase in SL of plant leaves could be an evolutionary strategy. The results of morphological complexity presented in this paper will trigger discussion on the causal links between leaf adaptive stability/efficiency and complexity. We present SL as a derived plant trait to describe plant leaf complexity and adaptive stability. Integrating SL into other leaf physiological measures will help to understand the dynamics of energy flow between plants and their environment.

HYBRID DEEP LEARNING WITH ALEXNET FEATURE EXTRACTION AND UNET CLASSIFICATION FOR EARLY DETECTION IN LEAF DISEASES

This study addresses the imperative need for early detection of leaf diseases in tobacco, pepper, and tomato plants, as these diseases significantly impact crop yield and quality. Existing methods often fall short in accurately identifying diseases across diverse plant species. The research aims to bridge this gap by proposing a hybrid deep learning approach, combining the robust feature extraction capabilities of AlexNet with the precise segmentation and classification prowess of UNet. The proposed hybrid model leverages AlexNet proficiency in extracting hierarchical features from plant leaf images and subsequently utilizes UNet for accurate disease classification. This synergistic combination enables the model to overcome the challenges posed by the varied morphologies of tobacco, pepper, and tomato leaves. Experimental results demonstrate the effectiveness of the proposed methodology, showcasing superior performance in terms of accuracy, sensitivity, and specificity compared to existing techniques. The hybrid deep learning approach exhibits promising potential for early and accurate detection of leaf diseases, contributing to sustainable crop management practices.

Algorithm for extracting contours of agricultural crops images

Abstract. Currently, identification of crop diseases and their prevention is one of the main problems in the field of agriculture. Conventional visual inspection is a time and money consuming process for farms. Therefore, images taken by unmanned aerial devices or satellites are used to assess the condition of crops, control them and identify diseases. In particular, when identifying crop diseases, it is necessary to first solve the problem of automatic recognition of their type through the image of crops. Usually contour separation algorithms are widely used in the segmentation of objects in the image. This work is aimed at solving the problem of separating the contour of the object, in which algorithms are formed based on Canny, Sobel and Robinson filters, which are considered to be popular and classical methods of contour separation, and their various combinations. In the computational experiments, a set of contour images, whose contours were separated by an expert, was used. Evaluation was performed by comparing the image obtained by applying the combination of filters to the original image and the corresponding contour image pixels separated by an expert. The proposed approach has been tested on a set of plant leaf images and shown to be effective.

Robust Model Selection for Plant Leaf Image Recognition Based on Evolutionary Ant Colony Optimization With Learning Rate Schedule

Robust Model Selection for Plant Leaf Image Recognition Based on Evolutionary Ant Colony Optimization With Learning Rate Schedule