Classification Of Plants Research Articles

Plant Disease Detection and Classification

The Plant Disease Detection and classification project, aims to develop an automated system using machine learning and deep learning techniques to detect and classify plant diseases early on by analyzing leaf images. Early disease detection is crucial for increasing crop yields, reducing pesticide use, and promoting sustainable farming practices. The system will provide detailed descriptions of detected diseases along with recommended prevention and treatment methods. By using advanced machine learning and deep learning algorithms and data augmentation techniques, the system aims to improve the accuracy and efficiency of disease detection. While existing research shows promise, challenges like dataset robustness and environmental variability remain. This project addresses these challenges by integrating diverse datasets and leveraging sophisticated machine learning algorithms. The ultimate goal is to provide farmers with a reliable tool for managing plant health, ensuring food security, and contributing to sustainable agricultural practices. This will help farmers enhance resilience in the face of increasing agricultural challenges. Key Words: Plant Disease Detection, Automated Disease Detection System, Machine Learning, Deep Learning, Data Augmentation, Data Normalization, Support Vector Machine, KNN Classifier, Convolutional Neural Network, Decision Trees, Random Forest.

Classification of offshore oil and gas plants

Oil and gas plants can be classified into onshore plants and offshore plants, colloquially called oil and gas platforms or simply oil platforms. Oil rigs represent an enormous feat of engineering developed from over 150 years of industrial expertise. This paper shows the classification of oil and gas platforms according to their constructions and purposes and describes the types of oil platforms, their construction, advantages and disadvantages.

Artificial Intelligence-Based Segmentation and Classification of Plant Images with Missing Parts and Fractal Dimension Estimation

Existing research on image-based plant classification has demonstrated high performance using artificial intelligence algorithms. However, limited camera viewing angles can cause parts of the plant to be invisible in the acquired images, leading to an inaccurate classification. However, this issue has not been addressed by previous research. Hence, our study aims to introduce a method to improve classification performance by taking these limitations into account; specifically, we incorporated both segmentation and classification networks structured as shallow networks to expedite the processing times. The proposed shallow plant segmentation network (Shal-PSN) performs adversarial learning based on a discriminator network; and a shallow plant classification network (Shal-PCN) with applied residual connections was also implemented. Moreover, the fractal dimension estimation is used in this study for analyzing the segmentation results. Additionally, this study evaluated the performance of the proposed Shal-PSN that achieved the dice scores (DSs) of 87.43% and 85.71% with PlantVillage and open leaf image (OLID-I) open datasets, respectively, in instances where 40–60% of plant parts were missing. Moreover, the results demonstrate that the proposed method increased the classification accuracy from 41.16% to 90.51% in the same instances. Overall, our approach achieved superior performance compared to the existing state-of-the-art classification methods.

Ethnotaxonomy and Ethnobotanical Studies of Honey and Resinous Plants in the Tchamba Prefecture in Togo

Natural plant products have gained increasing attention, but climate change and human activities have led to the degradation of ecosystems and loss of biodiversity. This study aims to contribute to the preservation of local knowledge related to the classification and use of honey-producing and resinous plants in the Tchamba prefecture, Togo. Ethnotaxonomic and ethnobotanical data were collected through semi-directed interview and focus groups in the four localities, involving 92 respondents. A total of 96 plants of beekeeping were identified, classified into 88 genera, 33 families, and 22 orders, with 16 ethnotaxonomic criteria used for classification. The most common criteria were "plant use" and "plant habit". The study highlights the rich local knowledge of plant uses for food, medicine, and rituals, emphasizing the importance of conserving both biodiversity and cultural heritage. These findings contribute to understanding how indigenous knowledge can support conservation and sustainable use of plant resources.

Medical Plant Recognition and Classification using CNN

Medical Plant Recognition and Classification using CNN

Tailoring convolutional neural networks for custom botanical data

AbstractPremiseAutomated disease, weed, and crop classification with computer vision will be invaluable in the future of agriculture. However, existing model architectures like ResNet, EfficientNet, and ConvNeXt often underperform on smaller, specialised datasets typical of such projects.MethodsWe address this gap with informed data collection and the development of a new convolutional neural network architecture, PhytNet. Utilising a novel dataset of infrared cocoa tree images, we demonstrate PhytNet's development and compare its performance with existing architectures. Data collection was informed by spectroscopy data, which provided useful insights into the spectral characteristics of cocoa trees. Cocoa was chosen as a focal species due to the diverse pathology of its diseases, which pose significant challenges for detection.ResultsResNet18 showed some signs of overfitting, while EfficientNet variants showed distinct signs of overfitting. By contrast, PhytNet displayed excellent attention to relevant features, almost no overfitting, and an exceptionally low computation cost of 1.19 GFLOPS.ConclusionsWe show that PhytNet is a promising candidate for rapid disease or plant classification and for precise localisation of disease symptoms for autonomous systems. We also show that the most informative light spectra for detecting cocoa disease are outside the visible spectrum and that efforts to detect disease in cocoa should be focused on local symptoms, rather than the systemic effects of disease.

Review of Medicinal Plant classification System

Previous studies in the fields of botanical sciences and medicine have looked closely at the categorization and identification of therapeutic plants. In the past, scholars have delved into conventional knowledge bases with the goal of deciphering the botanical characteristics and medicinal applications of many plant species, establishing the foundation for contemporary research. Nevertheless, distinguishing and categorizing a wide range of medicinal plant species proved to be a significant obstacle for studies. The challenge was precisely identifying and classifying the subtle and unique characteristics shared by various plant species. These obstacles impeded the creation of thorough and accurate classification schemes designed especially for medicinal plants. Our suggested Medicinal Plant Classification System uses a novel strategy that makes use of convolutional neural networks (CNNs) to overcome these difficulties. The CNN-based method greatly improves our classification system's accuracy and versatility while making it easier to identify subtle botanical features. For our work, CNNs seem to be an essential answer, demonstrating unmatched effectiveness in capturing various complex properties present in photos of medicinal plants. The flexibility, capacity for feature extraction, and hierarchical learning that CNNs possess highlight their critical role in creating a strong and adaptable classification system designed especially for the identification of medicinal plants.

Classification Of Plants By Their Fruits And Leaves Using Convolutional Neural Networks

The population growth of the world is exponential, this makes it imperative that we have an increase in food production. In this light, farmers, industries and researchers are struggling with identifying and classifying food plants. Over the years, there have been challenges that come with identifying fruits manually. It is time-consuming, labour intensive and requires experts to identify fruits because of the similarity in fruit’s leaves (citrus family), shapes, sizes and colour. A computerized detection technique is needed for the classification of fruits. Existing solutions to fruits classifications are majorly based on fruit or leave used as input. A new model using Convolutional Neural Network (CNN) is proposed for fruits classification. A dataset of 5 classes of fruits and fresh dry leaves plants (Mango, African almond, Guava, Avocado and Cashew) comprising of 1000 images each. The proposed model hyperparameters were: Conv2D layer, activation layer, dense layer, a learning and dropout rates of 0.001 and 0.5 respectively were used for the experiment. Various performances for accuracies of 91%, 97%, 78% and 97% were obtained for proposed model on local dataset, proposed model on benchmark dataset, benchmark model on local dataset and benchmark model on benchmark dataset. The proposed model is robust on both local and benchmark datasets and can be used for effective classification of plants

Morphological diversity and taxonomic implications of seedling structures in selected species of Fabaceae from Karachi (Pakistan)

Seedling morphology is a valuable tool for plant identification and classification, particularly in early growth stages. This study investigates the morphological characters of seedlings from randomly selected taxa within the Fabaceae family, focusing on cotyledons, first two leaves, and subsequent leaves to support taxonomic differentiation. Not all subfamilies within Fabaceae are fully represented in this study. Seedlings from 14 randomly selected taxa were analyzed using morphological traits such as cotyledon shape, phyllotaxy, leaf shape, and venation patterns. An artificial key was constructed, and three dendrograms were developed based on cotyledon morphology, first two leaves, and subsequent leaves to illustrate taxonomic relationships. Statistical analyses were employed to assess clustering patterns among the taxa. The artificial key categorized the taxa into subfamilies within Fabaceae, including Caesalpinoideae, Mimosoideae, and Papilionoideae, based on cotyledon shape and leaf morphology. Dendrogram analysis revealed distinct clustering patterns, grouping species according to key morphological features. Not all subfamilies of Fabaceae were covered, but the analysis provided clear separation among the species studied. The morphological traits observed were consistent with previous taxonomic findings and offered a reliable basis for classification. The morphological features of seedlings, especially cotyledons and eophylls, serve as reliable taxonomic markers for identifying and classifying species within Fabaceae. Although the study does not encompass all subfamilies, the randomly selected taxa demonstrated clear taxonomic distinctions. Dendrograms combined with artificial keys provided a useful framework for classification. Further studies should expand the scope to include more taxa, covering all subfamilies of Fabaceae. Integrating molecular and other taxonomic tools such as anatomy, cytology, and chemistry can enhance the precision of classification frameworks and provide deeper insights into the evolutionary relationships within the family.

Bioactive composition and medicinal properties of Walnut kernels

The purpose of this review is to evaluate the biological compounds present in walnut kernels and also to assess potential human health benefits. Walnuts are one of the most important tree nuts, belonging to the Juglandaceae family. They are cultivated worldwide, primarily in temperate regions. Throughout history, walnut kernels have been utilized to treat various disorders such as diarrhea, diabetes mellitus, thyroid issues, cancer, anorexia, and infectious diseases, in addition to their dietary benefits. Presently, advanced techniques like UHPLC-MS/MS, HPLC-DAD chromatography, and GC–MS are employed to identify key compounds in walnuts. These include phenolic compounds, phenolic acids, tannins and related compounds, flavonoids, phytosterols, fatty acids, triglycerides, tocopherols, vitamins, and carotenoids. The review also demonstrates that walnut kernels possess numerous medicinal properties, including antioxidants, cardiovascular effects, anti-cancer effects, antidiabetic effects, hepatoprotective effects, and antimicrobial effects. The current review also gives details on plant origin, distribution, classification, cultivation, and consumption as food. The review also described the recent medicinal plant profile, identification, and screening of the active ingredients of walnut's kernels. Researchers around the world are taking an interest in the clinical evaluation of walnut's active ingredients. The current review also gives details of the most recent conducted clinical studies. Mostly the reviews of walnuts are focused on walnut cultivars and/or specific types of compounds, although our current review is mostly focused on the active ingredients of walnut kernels and their specified biological role. In conclusion, different varieties of walnuts are abundant in significant bioactive compounds that possess important medicinal properties.

5s rDNA intergenic spacer of some species of Cruciata and Chondrophyllae sections and its application in taxonomy of genus Gentiana

Aim. The 5S rDNA intergenic spacer (IGS) region is a convenient tool for studying genome evolution and systematics of higher plants. The aim was to investigate the nucleotide sequence of 5S rDNA in some species of the sections Cruciata and Chondrophyllae, as well as to study the peculiarities of this region organisation by comparative analysis with other Gentiana species. Methods. Polymerase chain reaction, cloning, sequencing, bioinformatic analysis. Results. The cloned IGS regions of G. laciniata, G. cruciata and G. dahurica were sequenced and analysed. Typical regulatory elements were found in the regions of terminator and external promoter of 5S rRNA gene. Based on the comparative analysis, two groups of species were distinguished that differ significantly in the organisation of this region. One includes G. asclepiadea and species of the sections Gentiana, Ciminalis and Calathianae, and the other includes species of the sections Cruciata and Pneumonanthe. G. laciniata (sect. Chondrophyllae) differs in the IGS organization from species of both groups. Conclusions. The results of taxon grouping are generally consistent with the genus systematics and indicate that 5S rDNA IGS region can be a convenient tool for studying evolution and systematics of the genus.

A proposed plant classification framework for smart agricultural applications using UAV images and artificial intelligence techniques

Utilizing Wireless Sensor Networks (WSNs), Internet of Things (IoTs) sensors, and Unmanned Aerial Vehicles (UAVs), in conjunction with optimization techniques and machine learning algorithms, can present a novel approach to Precision Agricultural (PA) crops. Agriculture has transitioned from traditional legacy systems to incorporate advanced smart technologies. Several complex farming problems are utilized to promote a variety of UAV sensing and Artificial Intelligence (AI) algorithms in PA applications of smart agriculture, especially in developing countries. This paper proposes the design of a conceptual UAV sensing system for crop management using a novel classification framework. UAV-based image datasets can be implemented and expanded to various types of crops. In addition, it can classify various types of rice species and detect weed farms as it consists of a multistage process for identifying and monitoring different crops. The proposed classification framework is evaluated using three different real UAV image datasets compared with Naive Bayes, Decision Tree (DT), Bagging, and Random Forest (RF) techniques. The classification performance metrics obtained are as follows: i) the WeedNet dataset with 100 % F1-score, 100 % recall, 100 % precision, and 100 % accuracy; ii) the Rice Seedling dataset with 99.5 % F1-score, 99.5 % recall, 99.5 % precision, and 99.5 % accuracy; and iii) the Rice Varieties dataset with 97.99 % F1-score, 97.99 % recall, 98.14 % precision, and 97.99 % accuracy. The success rates achieved by the proposed classification framework outperform those of other recent state-of-the-art techniques, demonstrating its efficacy in crop management applications.

TRANSFORMER NETWORKS TO CLASSIFY WEEDS AND CROPS IN HIGH-RESOLUTION AERIAL IMAGES FROM NORTH-EAST SERBIA

The intricate backgrounds present in crop and field images, coupled with the minimal contrast between weed-infested areas and the background, can lead to considerable ambiguity. This, in turn, poses a significant challenge to the resilience and precision of crop identification models. Identifying and mapping weeds are pivotal stages in weed control, essential for maintaining crop health. A multitude of research efforts underscore the significance of leveraging remote sensing technologies and sophisticated machine learning algorithms to enhance weed management strategies. Deep learning techniques have demonstrated impressive effectiveness in a range of agricultural remote sensing applications, including plant classification and disease detection. High-resolution imagery was collected using a UAV equipped with a high-resolution camera, which was strategically deployed over weed, sunflower, tobacco and maize fields to collect data. The VIT models achieved commendable levels of accuracy, with test accuracies of 92.97% and 90.98% in their respective evaluations. According to the experimental results, transformers not only excel in crop classification accuracy, but also achieve higher accuracy with a smaller sample size. Swin-B16 achieved an accuracy of 91.65% on both the training and test datasets. Compared to the other two ViT models, the loss value is significantly lower by half, at 0.6450.

Characterization of Mung Plants Treated with Iron Oxide Nanoparticles Using Raman and Ultraviolet-Visible Spectroscopy Coupled with Chemometrics

In order to meet the demands of the expanding population, nanotechnology-based materials are being investigated for a potential solution to enhance crop yield. This study examines the effect of different concentrations (0.2, 0.6, 1.0, and 1.4 mM) of iron oxide nanoparticles (Fe2O3 NPs) on mung plants using confocal micro Raman and ultraviolet-visible spectroscopy. The analysis shows that the biochemical profile of the plants is altered. The increased intensity values of carotenoids, pectin, lignin, proteins, cellulose, carbohydrates, and aliphatics observed in Raman spectroscopy, along with increased concentrations of photosynthetic pigments in ultraviolet-visible spectroscopy, suggest that Fe2O3 NPs positively impact mung. In agro-related studies, Raman spectroscopy in assistance with chemometric analysis provides a reliable, noninvasive, fast, and label-free recognition and classification of plants under biotic or abiotic stress. Principal component analysis (PCA), linear discriminant analysis (LDA), and principal component analysis-linear discriminant analysis (PCA-LDA) were applied to the Raman spectra of treated and untreated mung leaves for rapid categorization and precise prediction. The results show that PCA-LDA achieved superior predicting performance and visualization, with an accuracy of 70%, along with higher recall, precision, f1 score, and area under receiver operating characteristics curves compared to LDA. The study reveals that spectroscopic techniques combined with chemometrics have great potential for confirmatory, nondestructive, and label-free assessments of biotic and abiotic stresses in plants.

Complete mitochondrial genome of Agropyron cristatum reveals gene transfer and RNA editing events

BackgroundAs an important forage in arid and semi-arid regions, Agropyron cristatum provides livestock with exceptionally high nutritional value. Additionally, A. cristatum exhibits outstanding genetic characteristics to endure drought and disease. Therefore, rich genetic diversity serves as a cornerstone for the improvement of major food crops. The purposes of this study were to systematically describe mitogenome of A.cristatum and preliminarily analyze its internal variations.ResultThe A. cristatum mitogenome was a single-ring molecular structure of 381,065 bp that comprised 52 genes, including 35 protein-coding, 3 rRNA and 14 tRNA genes. Among these, two pseudoprotein-coding genes and multiple copies of tRNA genes were observed. A total of 320 repetitive sequences was found to cover more than 10% of the mitogenome (105 simple sequences, 185 dispersed and 30 tandem repeats), which led to a large number of fragment rearrangements in the mitogenome of A. cristatum. Leucine was the most frequent amino acid (n = 1087,10.8%) in the protein-coding genes of A. cristatum mitogenome, and the highest usage codon was ATG (initiation codon). The number of A/T changes at the third base of the codon was much higher than that of G/C. Among 23 PCGs, the range of Pi values is from 0.0021 to 0.0539, with an average of 0.013. Additionally, 81 RNA editing sites were predicted, which were considerably fewer than those reported in other plant mitogenomes. Most of the RNA editing site base positions were concentrated at the first and second codon bases, which were C to T transitions. Moreover, we identified 95 sequence fragments (total length of 34, 343 bp) that were transferred from the chloroplast to mitochondria genes, introns, and intergenic regions. The stability of the tRNA genes was maintained during this process. Selection pressure analysis of 23 protein-coding genes shared by 15 Poaceae plants, showed that most genes were subjected to purifying selection during evolution, whereas rps4, cob, mttB, and ccmB underwent positive selection in different plants. Finally, a phylogenetic tree was constructed based on 22 plant mitogenomes, which showed that Agropyron plants have a high degree of independent heritability in Triticeae.ConclusionThe findings of this study provide new data for a better understanding of A. cristatum genes, and demonstrate that mitogenomes are suitable for the study of plant classifications, such as those of Agropyron. Moreover, it provides a reference for further exploration of the phylogenetic relationships within Agropyron species, and establishes a theoretical basis for the subsequent development and utilization of A. cristatum plant germplasm resources.

Deep Learning–Based Enhanced Optimization for Automated Rice Plant Disease Detection and Classification

ABSTRACTEnsuring global food security necessitates innovative solutions for early detection and precise classification of diseases in staple crops like rice. This study introduces an advanced approach for automated rice plant disease detection and classification by integrating deep learning and metaheuristic optimization techniques. Specifically, a deep dense neural network (DNN) is employed for its capacity to capture intricate patterns in images and extreme learning machine (ELM) for classification. To enhance the optimization process, an innovative variant of the Shuffled Shepherd Optimization (SSO) algorithm, known as Enhanced Artificial Shuffled Shepherd Optimization (EASSO), is introduced. EASSO incorporates adaptive strategies and enhanced exploration–exploitation mechanisms, enabling more efficient convergence during the training of the DNN. The proposed system operates by processing high‐resolution images of rice plants through the DNN, extracting nuanced features indicative of various diseases, including blast, bacterial blight, and brown spots. EASSO optimizes the DNN's parameters, maximizing its accuracy in disease classification. The synergy between DNN and EASSO ensures a robust and adaptive model capable of handling diverse and complex disease patterns. This automated approach significantly reduces the reliance on manual inspection, enabling timely intervention and improving overall agricultural productivity. Experimental results demonstrate the superiority of the DNN‐EASSO framework over traditional methods, showcasing higher accuracy rates and faster convergence. The incorporation of Enhanced Artificial Shuffled Shepherd Optimization enhances the precision and reliability of disease classification, making this integrated system a valuable tool for farmers and agricultural practitioners. This research represents a significant stride toward sustainable agriculture, showcasing the potential of advanced technologies in ensuring food security worldwide.

Herbal plant leaves classification for traditional medicine using convolutional neural network

<p>The classification of herbal plant leaves can be implemented in agriculture and traditional medicine. Primarily, sorting leaves was done before it was processed into medicinal ingredients. Currently, the sorting was still done manually by writing it on notes. Sometimes there were errors in the selection of leaves for medicinal ingredients. Herbal plants had various forms and are very greatly. Artificial intelligence technology was needed to have fast-paced time efficiency in sorting leaves. In the field of artificial intelligence, there was a specific or detailed learning process known as deep learning. The objective of this research was to classify herbal plant leaves images by applying and combining the convolutional neural network (CNN) deep learning method with data augmentation methods without the pre-trained architecture such as MobileNet and LeNet. This technique consisted of 4 main stages such as collecting data, preprocessing or normalizing data, building a model, and evaluating. The dataset used in this research were 4 types of herbal plants that do not flower and do not bear fruit including gulma siam, piduh, sirih, and tobacco. Each class had 250 images with total dataset used in this research was 1,000 images of herbal plant leaves and divided into 2 data, namely 80% data training 20% data testing, and validation. The data was trained with the epoch of 100 for the best training. It had an accuracy score of 98.74%. Without the data augmentation process it had an accuracy score of 91.43%.</p>

Comparative analysis of whole chloroplast genomes of three common species of Nekemias from vine tea

Nekemias grossedentata (N. grossedentata) is a medicinal and edible plant. The young leaves and tender stems are specifically utilized to manufacture vine tea, which is traditionally employed in the treatment of conditions such as the common cold fever, sore throat, jaundice hepatitis, and other ailments. The morphologically of N. grossedentata similar to Nekemias cantoniensis (N. cantoniensis) and Nekemias megalophylla (N. megalophylla), which lead to a chaotic market supply. Numerious studies have confirmed that chloroplast genomes and chromatography play important role in plant classification. Here, the whole chloroplast (cp) genomes of the three Nekemias species were sequenced in Illumina sequencing platform. Meanwhile, their chromatographic fingerprints have constructed using high-performance liquid chromatography (HPLC). The annotation results demonstrated that the three chloroplast genomes were typical quadripartite structures, with lengths of 162,147 bp (N. grossedentata), 161,981 bp (N. megalophylla), and 162,500 bp (N. cantoniensis), respectively. A total of 89 (N. grossedentata) /86 (N. megalophylla and N. cantoniensis) protein-coding genes, 37 tRNA gene and 8 rRNA genes were annotated. The IR/SC boundary regions were relatively conserved across the three species, although three regions (rps19-rpl2, rpl32-trnL-UAG, ccsA-ndhD) exhibited nucleotide diversity values (Pi) of variable sites higher than 1%. Phylogenetic analysis indicated that N. grossedentata had a closer genetic relationship with N. megalophylla than that of N. cantoniensis. Moreover, the chromatographic fingerprints revealed that the main functional components and genetic relatedness of three species were highly similar with their morphological results. In conclusion, N. grossedentata and N. megalophylla can be consider as the origin plants of vine tea. This study provides appropriate information for species identification, phylogeny, quality assessment of three medicinal plants of the genus Nekemias and will contribute to the standardization of vine tea raw materials.

Deep learning based multi disease classification of plant leaves using light weight residual architecture

Plant diseases can severely impact crop yields, posing a major risk to worldwide food stability. Prompt and precise identification of these diseases is crucial for early intervention and efficient crop administration. This paper introduces an innovative method for detecting plant leaf diseases using residual networks (ResNets) and the PlantVillage dataset. To develop light weight residual (LWR) architecture, five convolutional layers are interleaved with five max-pooling layers, making up the architecture of ten layers. The number of filters in the convolutional layers is gradually increased from 32 to 64 and up to 512 with a 3×3 kernel. A fully connected layer is the last layer of the network which provides the classification of leaf diseases The LWR architecture is trained and evaluated using the PlantVillage dataset, a broad collection of annotated images. This dataset serves as the basis for the system. The findings of the experiments provide evidence that the suggested system has higher accuracy, sensitivity, and specificity measures. The use of residual networks in LWR architecture improves the capability of the model to acquire complicated representations, which in turn enables a more precise differentiation between healthy and unhealthy plant leaves.

An Improve Method for Plant Leaf Disease Detection and Classification using Deep Learning

In countries like India, whose important occupation is agriculture, face a huge loss when the crops get affected by any type of disease. These diseases attack the crops in various stages and can destroy the entire production. Since most diseases are transmitted from one crop to another there is an essential requirement to detect the type of disease in the early stage so that farmers can take the required action to “save the crops” and production. Early disease detection is one of the essential activities for enhancing agricultural productivity. Diseases spread very quickly in the parts of the leaves that affect the growth of the plants. Early detection is a challenging task as the symptoms are mild for accurate identification. This research paper presents an enhanced CNN based MCC-ECNN model with fine-tuned hyper-parameters and various batch sizes for accurate plant leaf disease classification.