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Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model
Tomatoes are widely cultivated and consumed worldwide and are susceptible to various leaf diseases during their growth. Therefore, early detection and prediction of leaf diseases in tomato crops are crucial. Farmers can take proactive measures to prevent the spread and minimize the impact on crop yield and quality by identifying leaf diseases in their early stages. Several Machine Learning (ML) and Deep Learning (DL) frameworks have been developed recently to identify leaf diseases. This research presents an efficient deep-learning approach based on a hybrid classifier by optimizing the CNN and LSTM models, which helps to enhance classification accuracy. Initially, Median Filtering (MF) is used for leaf image pre-processing. Then, an improved watershed approach is used for segmenting the leaf images. Subsequently, enhanced Local Gabor Pattern (LGP) and statistical and color features are extracted. An optimized CNN and LSTM are used for classification, and the weights are tuned using the SISS-OB (Self Improved Shark Smell With Opposition Behavior) algorithm. Finally, we have analyzed the performance using various measures. Since we have done segmentation, feature extraction, and optimization improvisations, our proposed methodology results are higher than other available methods and existing works. The results obtained at Learning Percentage (LP) is 90% which is far superior to those obtained at other LPs. The FNR (False Negative Rate) is much lower (0.05) at the 90th LP. The proposed model achieved better classification performance in terms of Accuracy of 97.13%, Sensitivity of 95.09%, Specificity of 95.24%, Precision of 94.31%, F measure of 96.71% and MCC 87.34%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm
Since the beginning of time, humans have relied on plants for food, energy, and medicine. Plants are recognized by leaf, flower, or fruit and linked to their suitable cluster. Classification methods are used to extract and select traits that are helpful in identifying a plant. In plant leaf image categorization, each plant is assigned a label according to its classification. The purpose of classifying plant leaf images is to enable farmers to recognize plants, leading to the management of plants in several aspects. This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes. This modified algorithm works on different sets of plant leaves. The proposed algorithm examines several benchmark functions with adequate performance. On ten plant leaf images, this classification method was validated. The proposed model calculates precision, recall, F-measurement, and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms. Based on experimental data, it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%. 2022 Tech Science Press. All rights reserved. -
Plant disease diagnosis and solution system based on neural networks
Plant diseases are one of the major factors affecting crop yield. Early identification of these diseases can improve productivity and save money and time for the farmer. This paper presents a novel technique to diagnose plant diseases using a mobile application. A Convolutional Neural Network (CNN) model was built and trained using MobileNetV2 architecture with the help of image processing techniques and transfer learning. A dataset comprising 87,000 images that contain 38 classes of diseases belonging to 14 different crops was used to train the model. The model achieved an accuracy of 98.69% and a loss of 0.5373. A mobile application was built in Android Studio with the help of a trained model. The mobile application built works without a need for a remote server. The application can identify the disease, gives information regarding the identified disease and also suggests necessary remedies to tackle the disease. 2021, Engg Journals Publications. All rights reserved. -
Plant Disease Detection and Classification using Emperor Penguin Optimizer (EPO) based Region Convolutional Neural Network (RCNN)
Agriculture stands as India's most crucial industry, despite grappling with a 35% annual loss in crop yield attributed to plant diseases. Traditionally, the detection of plant diseases has been a laborious process, hampered by insufficient laboratory infrastructure and expert knowledge. Plant disease detection methods that are automated provide a useful way to expedite the labor-intensive process of keeping an eye on large-scale agricultural fields and recognizing disease symptoms as soon as they appear on plant leaves. Current developments in deep learning (DL) and computer vision have highlighted the benefits of creating autonomous models for plant disease identification based on visible symptoms on leaves. In this study, we propose a novel method for detecting and classifying plant diseases by combining the Emperor Penguin Optimizer (EPO) with a Region Convolutional Neural Network (RCNN). The suggested methodology uses EPO to improve the discriminative power of features extracted from plant pictures, allowing for a more robust and accurate classification procedure. The Classification Region Convolutional Neural Network (RCNN) is used to leverage spatial correlations within the image, allowing for exact disease region localization. The goal of this integration is to increase the overall efficiency and dependability of plant disease detection systems. The investigations made use of the well-known PlantVillage dataset, which comprises 54,305 data of different plant disease types in 38 categories. Furthermore, an analysis was carried out in comparison with similar advanced investigations. According to the experiment results, RCNN-EPO outperformed in terms of classification accuracy, achieving 94.552%. 2024 IEEE. -
Plano framework for graph indexing - A statistical analysis
Graph Mining is becoming one of the most dominant fields of research. There are plenty methods to index, re-index and to search the features throughout the index but still from the literature study there is no specific frame work which can sum up all three so that indexing and updating the index with new feature can be done in consistent intervals according to the arrival of new features. PLANO is the frame work which has the latest algorithms to look into the data and index. In this paper, Time and Memory efficiency of the proposed algorithms in the PLANO framework is tested statistically and compared with the existing algorithms memory and time usage. Research India Publications. -
Planned fashion obsolescence in the light of supply chain uncertainty
Fast fashion has popularised the phenomenon of perceived obsolescence whereby customers try to stay in line with the current fashion trends in the market even though the apparel they own are in perfect condition. This has ultimately led the fashion industry to become the second largest polluter in the world. The primary objective of this research paper is to comprehend how the media manoeuvres customers to indulge in fast fashion and how that in turn leads to uncertainty in the supply chain. To understand this, a maximum variation sampling method was adopted which consisted of customers, supply chain partners and marketers. In order to draw a parallel between the variables researched in the past and the present day scenario, an interview schedule was employed. Through the variables selected with the help of Dedoose, a model was created to identify the hurdles faced by suppliers as well as the customer in the fast fashion cycle. The results found that the power to break the fast fashion phenomenon lay in the hands of the media as it is through them that customers' perception can be altered. The importance of artificial intelligence in SCM and the modern tools used in industry 4.0 have also been discussed. 2020 Allied Business Academies. -
Planetary Ball Milling and Tailoring of the Optoelectronic Properties of Monophase SnSe Nanoparticles
Downscaling of tin monoselenide (SnSe) samples to the nanometer regime (~8020nm) without affecting the structure, homogeneity, and optoelectronic properties was carried out by high-energy planetary ball milling (BM). The milling rate was varied from 200rpm to 800rpm by adopting a dry and wet-grinding top-down approach on customized stoichiometric SnSe precursors. The degree of crystallinity was assessed by powder x-ray diffraction (PXRD) and selected area electron diffraction. The lattice parameters, a = 4.435 b = 11.498 and c = 4.148 of the nanoparticles were calculated from the PXRD data. Energy-dispersive x-ray analysis confirmed the chemical homogeneity (49.88:51.12 at.%) of the samples. The effects of rotational velocity as well as mode of grinding on the morphology and the size of SnSe powders were investigated using electron microscopes. The direct optical transition with band gap varied from 1.75eV to 2.28eV was elucidated from UV-Vis-NIR data. Photoluminescence revealed an increase in the intensity of the emission peak at 462.97nm with angular velocities for both types of grinding. The variation of electrical resistivity (36107 ? cm) and mobility (3.451.12 cm2/Vs) with rotational speed was calculated for all the samples. The results obtained for the ball-milled nanoparticles pave the way towards the reduction of particle size, formation of stable morphology, and appreciable crystalline structure quality suitable for solar cell absorbers. Graphical Abstract: [Figure not available: see fulltext.] 2023, The Minerals, Metals & Materials Society. -
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Planetarism and eco humanism in the buddhist beat bards allen ginsberg and gary snyder an interdisciplinary thematic study on their literature philosophy and ecological perspectives
The Beat Generation, also known as the Beat movement, were a group of American writers who emerged in 1950s. Among its most influential members were Allen Ginsberg, Gary Snyder, Jack Kerouac, William Burrough, William Carlos Williams and Lawrence Ferlenghetti. What could be loosely described as the underlying philosophy was visionary enlightenment, Zen Buddhism, environmentalism and Amerindian culture. The Beat Generation invented a literary collage movement as a counter-brain wash method for reversing effect of Mass media-Military-Industrial-Communist-Capitalist-CIA-KGB disinformation reality image bank. A common theme that linked them together was a rejection of the newlineprevailing American middle class values, deterioration of the Planet s health, the newlinepurposelessness of modern society and the need for withdrawal and protest. The Beats have tried to break the restraints imposed on the western man s mind by the official ways of thinking. They find the official morality unacceptable and tried to arrive at metaphysical and ethical enlightenment through the methods adopted by the oriental thinkers like Gauthama Buddha, and Zen ascetics. They begin with the problems the western civilization is encountering, like the one of war and ecological degradation and use the concepts of oriental civilizations to find causes and remedies of the miseries people and the planet have been ecountering. The mechanistic paradigm underlying the industrial society gives way to the newlineviirealization that we belong to a living, self-organizing cosmos. General systems newlinetheory, emerging from the life sciences, brings fresh evidence to confirm ancient newlineindigenous teachings; the earth is live, mind is pervasive, all beings are our relations. newlineThe Beats rediscovered this perennial truth which changed our views about the planet newlineand its life systems. The beats were a product of the Second World War and of the cold war. -
Place-based strategies, multichannel merger, and context-driven alerts for engagement with mobile marketing
Mobile marketing is essential for timely, personalized communication in the digital world. Engagement is increased by location-based strategies like geofencing and context-driven notifications. Integrating social media improves ties with customers. In the digital age, multi-channel integration guarantees a smooth and personalized experience. As per the authors, this chapter explores how location-based suggestions, context-driven notifications, and multi-channel integration enhance client connections while highlighting the significance of geolocation data for targeted content. For context-driven notifications to be effective, helpfulness and privacy must be balanced. Companies create stronger relationships with their consumers and improve the customer experience, which motivates both present and new customers to engage and connect with their brand. An analysis is conducted on the changing field of mobile marketing, emphasizing the use of location-based tactics, multi-channel integration, and context-driven notifications to increase user engagement. 2024, IGI Global. All rights reserved. -
Pixels to Pathogens: A Deep Learning Approach to Plant Pathology Detection
It is known that accurately identifying, early and timely treatment and elimination of the plant diseases is essential for crop protection and healthy crop growth. In traditional or conventional methods, identification and classification were done by testing in laboratories or through visual inspection by farmers. Now going through the testing in labs is very time consuming, while the visual inspection requires enough experience and knowledge. To solve this problem, our study proposes a robust plant pathogen detection method based on a Deep Learning approach on a large dataset containing about 38 categories of different species like Maize, Potatoes, Tomatoes, Bell Pepper, Peach, Strawberry etc. and diseases like rust, molds, blight (late and early). This crop disease detection model leverages the power of the EfficientNetB3 architecture, a state-of-art convolutional neural network(CNN). The main backbone is served by EfficientNetB3and then it is fine-tuned using different hyperparameters and other regularization techniques like weight decay, dropout method and optimizers like RAdam,to enhance the overall accuracy coupled with dynamic learning rate adjustment. In the testing set of the dataset, the proposed model shows encouraging accuracy of about 99.25%, high precision of about 97.35%. A thorough evaluation of the model's functionality is given by the help of training and validation line chart and loss chart that gives the in-depth information on the prediction. And then we implemented the detection model in our mobile application whose interface screen shots are given below. In the application the image can be taken by camera or fed from folders and it will detect the type of disease. 2024 IEEE. -
Piracy in fashion business and protection for the creativity of designers: A comparative study /
The fashion business is one of the fastest-paced industries today. Beyond the simple act of selling clothing, a company's ability to create and capitalize on a distinctive brand is a crucial factor in achieving sustained success in this industry. When a style or brand becomes well-known, many others copy it mindlessly, which causes a huge loss for the people who made the original products. We also encounter fake products from well-known brands very often, which not only ruin the fashion industry but also pose a serious risk to the economy. Fashion nowadays thus goes beyond only clothing and accessories. The substantial expansion of the fashion industry is significantly influenced by intellectual property. “Intellectual property law may be used to safeguard the originality of a wide variety of creations, including those in the fashion industry. The types of intellectual property and their applicability to the fashion industry have only been touched on briefly. The purpose of this dissertation is to educate the reader about current fashion trends, hotly debated problems, and the importance of intellectual property rights in the fashion business”. -
Piperine, an alkaloid of black pepper seeds can effectively inhibit the antiviral enzymes of Dengue and Ebola viruses, an in silico molecular docking study
Ebola and Dengue are the critical diseases caused by RNA viruses, especially in the tropical parts of the globe, including Asia and Africa, and no prominent therapeutic options are available so far. Here, an effort was made to evaluate the efficacy of black pepper (Piper nigrum L.) alkaloid Piperine as a potential drug through computational docking simulation. Eight structurally essential proteins of Dengue and Ebola virus were selected as in silico docking targets for Piperine. Absorption, Distribution, Metabolism, and Excretion profile showed that Piperine was safe and possessed significant drug-like properties. Molecular dynamic simulation and binding free energy calculation showed that Piperine could inhibit Methyltransferase (PDB id 1L9K) of Dengue and VP35 Interferon Inhibitory Domain (PDB id 3FKE) of Ebola virus in comparison with the commercial antiviral Ribavirin. Furthermore, statistical analysis based on multivariate and clustering approaches revealed that Piperine had more affinity towards viral proteins than that of Ribavirin. 2020, Indian Virological Society. -
Pink floyd's time: An aural metanarrative exploring time through form, lyric, and musical arrangement
The inability of language to capture the essence of time is a crisis that has been expressed by philosophers starting from St. Augustine to Paul Ricoeur. Appearing on their seminal album, Dark Side of the Moon, Pink Floyd's Time is a profound artistic attempt which transcends this language barrier by using music to bring the listeners to a more direct confrontation with time; doing so by juxtaposing time as calibrated and as experienced through the music and the lyrics, and by making the reader experience time-based affects such as impatience, expectation, monotony, and such. As a direct function of song, time is experienced as musical time in the song, thereby ensuring that the listener's confrontation with time is immersive, with lyrics that describe the nature of experienced and calibrated time working synchronously with the music to complete the image. In the context of its release in 1974, the 6:52 minute song was in engagement with the concept of time as well, in that it was among the pioneering ones which redefined radio broadcast time beyond the standard 3 minutes afforded to popular music tracks, with the commercially preferred listener span in mind. The matter of time thus becomes a multi-layered formal engagement in the song, at the level of lyric, recording, music and listening, thereby making possible an image of time that is polished and rounded. These aural, lyrical and production-based concepts will be addressed and expanded upon to show how Pink Floyd's Time functions as a metanarrative in how it uses and invokes the elements of time to talk about time. AesthetixMS 2020. -
Pilot Study on Adoption and Usage of AI in Food Processing Industry by UTAUT2
Artificial intelligence (AI) improves the efficiency of work and effectiveness in the output. Currently, food processing industries have started using AI in their business operations. It is crucial to have an in-depth understanding of the adoption and usage of AI systems in food processing industries. Therefore, this paper validates the Unified Theory of Acceptance and Use of Technology (UTAUT2) in the context of the food processing industry. This study applied AI to the food processing industries in the Bengaluru region. The study's objective is to build a clear vision of the factors that affect the user acceptance and behaviour intention of the user by pilot test. The pilot survey collected 62 responses through the questionnaire. The respondents were employees from the food processing industries in Bengaluru. The reliability test of the questionnaire was done by using Jamovi 2.3.16 software. The questionnaire was tested in three ways: Cronbach Alpha, McDonalds Omega, and Inter-rater reliability. The results of the entire test were reliable since overall Cronbach Alpha of 0.874, which is within the range of 0.800.90, and considered good internal consistency. Similarly, McDonalds Omega is within the range of 0.800.90, which is excellent consistency, and Inter-rater reliability is within the range of moderately acceptable scores from 50 to 75%. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
PICTURE PROCESSING ON ISOMETRIC FUZZY REGULAR ARRAY LANGUAGES
Isometric array grammar is one of the simplest model to generate picture languages, since both sides of its production rule have the same shape. In this paper, we have introduced isometric fuzzy regular array grammars to generate isometric fuzzy regular array languages and discussed its closure properties. Also, the relation between isometric fuzzy regular array grammar and boustrophedon fuzzy finite automata has been discussed. Moreover, we study the relation between two dimensional fuzzy regular grammars with returning fuzzy finite automata and boustrophedon fuzzy finite automata. Further, the hierarchy results of these three classes of languages have been discussed. 2024 KSCAM. -
Pi-MnO2 decorated poly-3-thienylacetic acid on carbon fiber paper for electrochemical synthesis of 2-formyl-thiophene
Sustainable and environmentally benign synthesis methods have captured researchers' minds in recent times and have contributed a lot towards the green synthesis of organic compounds. This work presents an efficient green method for synthesizing 2-formyl thiophene using a newly designed Pi-MnO2 deposited on poly-3-thienylacetic acid coated Toray carbon fiber paper (PThAA-Pi-MnO2-TCFP) electrode. Cyclic voltammetry (CV), chronoamperometry (CA) and bulk electrolysis (BE) techniques were employed for the optimization and synthesis of 2-formyl thiophene and the product obtained was characterized by proton nuclear magnetic resonance (1HNMR) spectroscopy. The efficacy of the developed electrode was examined by different electrochemical and physicochemical studies. It is an intriguing approach for the 4-acetamido-TEMPO (4-ACT) mediated, PThAA-Pi-MnO2-TCFP catalyzed electro-oxidation of thiophene-2-ylmethanol (TM). This method is handy and reasonably practical since the developed electrocatalyst is inexpensive, and the synthesis is environmentally benign. Hence it is a highly efficient method for synthesizing 2-formyl thiophene, a much sought-after starting material in pharmaceuticals, agrochemicals, and cosmetics. 2023 -
Phytonanotechnology for the Removal of Pollutants from the Contaminated Soil Environment
Over-consumption of chemically synthesized components aids country toward industrial revolution, which symbolizes for economic prosperity. On the other hand, industrial revolution is responsible for soil pollution, due to its toxic effluents. The main source of soil pollutants includes fertilizers, pesticides, untreated wastewater used for irrigation, land application of sewage sludge due to rich organic content, petroleum leakage and leaching from landfills, etc. The crops grown out of this contaminated soil make the plant to changes its nutritional valve, bioaccumulates the chemicals, and also hinder with its vigor. Studies proved that prevent measures should prioritize in minimizing the adverse effect on the environment. Use of Phyto-nanotechnology in wastewater treatment, as nano fertilizer, nanotechnology-based biocontrol agents, and other areas before the hazardous chemicals entering soil. Green synthesized nanoparticles assist as excellent bio remedial agents as they are rich in biomolecules like carbohydrates, proteins, lipids, and several enzymes also deter-mine its efficacy of action. Hence, this chapter highlights the various eco-friendly and inexpensive products or formulation used for removal of toxic and recalcitrant materials which are dreadfully risky to human health. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Phytogenic synthesis and antimicrobial activity of ZnO nano bow ties (ZnO NBTs): An experimental and computational study
Phytogenic synthesis is a sustainable and eco-friendly approach for producing nanoscale particles, using biological entities such as plants and their byproducts. In this study, Allium sativum extract was selected as a capping and reducing agent due to the presence of phytochemicals such as allicin, diallyl disulfide (DADS), vinyl dithiins, ajoene (E- and Z-ajoene), diallyl trisulfide (DATS), and thiol (sulfhydryl) groups. The resulting ZnO Nano Bow Ties (ZnO NBTs) were characterized using FE-SEM, XRD, EDX, DLS, zeta potential, FTIR, and UV-Vis spectroscopy to evaluate the size, morphology, and crystallinity. The obtained XRD, SEM, and DLS results suggested an average longitudinal length of ?372 nm with a maximum lateral width of ?64 nm and a Bow Tie shape. Gas Chromatography-Mass Spectroscopy (GC-MS) analysis was employed to elucidate the prominent phytochemical constituents of the Allium sativum extract. Preliminary antibacterial assays reveal significant inhibition zones and growth inhibition effects against gram-negative bacteria of both Klebsiella pneumoniae and Escherichia coli, suggesting the promising antimicrobial potential of these ZnO NBTs. Monte Carlo simulations revealed that the cone-shaped ZnO NBTs bind strongly to the active sites of the target proteins with binding affinities of ?36.20 and ?32.14 kcal/mol for Klebsiella pneumoniae and Escherichia coli respectively, which correlates with their activities. The ZnO NBTs complexes formed stronger hydrophobic interactions and hydrogen bonds with amino acid residues of Escherichia coli than with Klebsiella pneumoniae. This integrated experimental and computational study underscores the potential of the use of ZnO NBTs as a sustainable and effective strategy to combat bacterial pathogens. The findings of this study indicate that efficient morphology (shape) is a major contributor to the protein binding affinities of ZnO NBTs, with promising implications for the design of antibacterial drugs in nanomedicine. 2024 The Authors -
Phytogenic CeO2-Sm2O3 nanocomposites with enhanced catalytic activity for reduction of 4-nitrophenol
The phytogenic synthesized CeO2-Sm2O3 is a green, efficient and cost-effective catalyst. The CeO2-Sm2O3 composite was characterized using XRD, FTIR, Raman, TGA, UV-DRS, TEM, FE-SEM and EDAX. The synthesized CeO2-Sm2O3 shows a high catalytic activity for the reduction of 4-nitrophenol in the presence of the sodium borohydride under ambient conditions. This CeO2-Sm2O3 nanocomposite catalyst shows good stability and reusability without much loss in conversion efficiency. CeO2-Sm2O3 possess great prospects in the reduction of nitro organic pollutants in water. 2019 IOP Publishing Ltd.