A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

© 2023 Tech Science Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Tomato leaf diseases significantly impact crop production, necessitating early detection for sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying and classifying tomato leaf diseases. However, current DL methods often require substantial computational resources, hindering their application on resource-constrained devices. We propose the Deep Tomato Detection Network (DTomatoDNet), a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this. The Convn kernels used in the proposed (DTomatoDNet) framework is 1 × 1, which reduces the number of parameters and helps in more detailed and descriptive feature extraction for classification. The proposed DTomatoDNet model is trained from scratch to determine the classification success rate. 10,000 tomato leaf images (1000 images per class) from the publicly accessible dataset, covering one healthy category and nine disease categories, are utilized in training the proposed DTomatoDNet approach. More specifically, we classified tomato leaf images into Target Spot (TS), Early Blight (EB), Late Blight (LB), Bacterial Spot (BS), Leaf Mold (LM), Tomato Yellow Leaf Curl Virus (YLCV), Septoria Leaf Spot (SLS), Spider Mites (SM), Tomato Mosaic Virus (MV), and Tomato Healthy (H). The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%, demonstrating excellent accuracy in differentiating between tomato diseases. The model could be used on mobile platforms because it is lightweight and designed with fewer layers. Tomato farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.Peer reviewe

An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm

Improving wireless communication and artificial intelligence technologies by using Internet of Things (Itoh) paradigm has been contributed in developing a wide range of different applications. However, the exponential growth of smart phones and Internet of Things (IoT) devices in wireless sensor networks (WSNs) is becoming an emerging challenge that adds some limitations on Quality of Service (QoS) requirements. End-to-end latency, energy consumption, and packet loss during transmission are the main QoS requirements that could be affected by increasing the number of IoT applications connected through WSNs. To address these limitations, an effective routing protocol needs to be designed for boosting the performance of WSNs and QoS metrics. In this paper, an optimization approach using Particle Swarm Optimization (PSO) algorithm is proposed to develop a multipath protocol, called a Particle Swarm Optimization Routing Protocol (MPSORP). The MPSORP is used for WSN-based IoT applications with a large volume of traffic loads and unfairness in network flow. For evaluating the developed protocol, an experiment is conducted using NS-2 simulator with different configurations and parameters. Furthermore, the performance of MPSORP is compared with AODV and DSDV routing protocols. The experimental results of this comparison demonstrated that the proposed approach achieves several advantages such as saving energy, low end-to-end delay, high packet delivery ratio, high throughput, and low normalization load.publishedVersio

A PROSPECTIVE OBSERVATIONAL STUDY ON ADVERSE DRUG REACTIONS OF ANTIBIOTICS IN A TERTIARY CARE HOSPITAL

The aim of the present study was to detect and analyze adverse drug reactions of antibiotics in a tertiary care hospital. This was a prospective observational study carried out in the Department of General Medicine (Osmania General Hospital) over a period of six months. The present study was conducted to assess the prescription pattern of antibiotic usage. Standard pro-forma was used to collect the information regarding antibiotics, its dose, duration, first line of antibiotics and second line of antibiotics and adverse drug reactions. A Total of 100 ADRs was reported from 100 patients during the study period with female predominance (72%) over males. The average age of the patients in the study was found to be 55-70 years. The majority of the ADRs occurred in the age group of 40-80 years. More number of ADRs was from General Medicine Departments in which the most affected organ systems were the GIT (22%) and the skin (19%). The antibiotic classes mostly accounted were cephalosporin (16%) followed by other. The severity assessment revealed that most of them were moderate followed by mild and severe reactions. Of the reported reactions, 30 % were definitely preventable and causality assessment was done which showed that the reactions were probable, possible. Results show that cephalosporin was extensively used in the department of General medicine. The system should promote the spontaneous reporting of Adverse drug reactions to antibiotics. Proper documentation and periodic reporting to regional Pharmacovigilance centre’s to ensure drug

An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model

IntroductionRecently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. MethodThis research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications.ResultsThe proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively.DiscussionThe experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases

On smart gaze based annotation of histopathology images for training of deep convolutional neural networks

Unavailability of large training datasets is a bottleneck that needs to be overcome to realize the true potential of deep learning in histopathology applications. Although slide digitization via whole slide imaging scanners has increased the speed of data acquisition, labeling of virtual slides requires a substantial time investment from pathologists. Eye gaze annotations have the potential to speed up the slide labeling process. This work explores the viability and timing comparisons of eye gaze labeling compared to conventional manual labeling for training object detectors. Challenges associated with gaze based labeling and methods to refine the coarse data annotations for subsequent object detection are also discussed. Results demonstrate that gaze tracking based labeling can save valuable pathologist time and delivers good performance when employed for training a deep object detector. Using the task of localization of Keratin Pearls in cases of oral squamous cell carcinoma as a test case, we compare the performance gap between deep object detectors trained using hand-labelled and gaze-labelled data. On average, compared to 'Bounding-box' based hand-labeling, gaze-labeling required 57.6% less time per label and compared to 'Freehand' labeling, gaze-labeling required on average 85% less time per label

Synthesis of efficient cobalt–metal organic framework as reusable nanocatalyst in the synthesis of new 1,4-dihydropyridine derivatives with antioxidant activity

Efficient cobalt–metal organic framework (Co-MOF) was prepared via a controllable microwave-assisted reverse micelle synthesis route. The products were characterized by SEM image, N2 adsorption/desorption isotherm, FTIR spectrum, and TG analysis. Results showed that the products have small particle size distribution, homogenous morphology, significant surface area, and high thermal stability. The physicochemical properties of the final products were remarkable compared with other MOF samples. The newly synthesized nanostructures were used as recyclable catalysts in the synthesis of 1,4-dihydropyridine derivatives. After the confirmation of related structures, the antioxidant activity of derivatives based on the DPPH method was evaluated and the relationship between structures and antioxidant activity was observed. In addition to recyclability, the catalytic activity of Co-MOF studied in this research has remarkable effects on the synthesis of 1,4 dihydropyridine derivatives

Low Temperature Synthesis of Superparamagnetic Iron Oxide (Fe3O4) Nanoparticles and Their ROS Mediated Inhibition of Biofilm Formed by Food-Associated Bacteria

In the present study, a facile environmentally friendly approach was described to prepare monodisperse iron oxide (Fe3O4) nanoparticles (IONPs) by low temperature solution route. The synthesized nanoparticles were characterized using x-ray diffraction spectroscopy (XRD), Raman spectroscopy, field emission scanning electron microscopy (FESEM) measurements, Fourier-Transform Infrared Spectroscopy (FTIR), and Thermogravimetric analysis (TGA) analyses. XRD patterns revealed high crystalline quality of the nanoparticles. SEM micrographs showed the monodispersed IONPs with size ranging from 6 to 9 nm. Synthesized nanoparticles demonstrated MICs of 32, 64, and 128 μg/ml against Gram negative bacteria i.e., Serratia marcescens, Escherichia coli, and Pseudomonas aeruginosa, respectively, and 32 μg/ml against Gram positive bacteria Listeria monocytogenes. IOPNs at its respective sub-MICs demonstrated significant reduction of alginate and exopolysaccharide production and subsequently demonstrated broad-spectrum inhibition of biofilm ranging from 16 to 88% in the test bacteria. Biofilm reduction was also examined using SEM and Confocal Laser Scanning Microscopy (CLSM). Interaction of IONPs with bacterial cells generated ROS contributing to reduced biofilm formation. The present study for the first time report that these IONPs were effective in obliterating pre-formed biofilms. Thus, it is envisaged that these nanoparticles with broad-spectrum biofilm inhibitory property could be exploited in the food industry as well as in medical settings to curtail biofilm based infections and losses

Experimental and in silico evaluation of Carthamus tinctorius L. oil emulgel: a promising treatment for bacterial skin infections

PurposeThe current study aimed to develop a topical herbal emulgel containing Carthamus tinctorius L. (CT) oil extract, which has been scientifically proven for its antibacterial and antioxidant activities for the ailment of bacterial skin infections.MethodThe CT emulgel was formulated by response surface methodology (RSM) and was evaluated by various parameters like extrudability, spreadability, pH, viscosity, and antibacterial and antioxidant activities. Molecular docking was also performed using AutoDock.ResultsAmong all formulated CT emulgels, F9 and F8 were optimized. Optimized formulations had shown good spreadability and extrudability characteristics. Sample F8 had % inhibition of 42.131 ± 0.335, 56.720 ± 0.222, and 72.440 ± 0.335 at different concentrations. Sample F9 had % inhibition of 26.312 ± 0.280, 32.461 ± 0.328, and 42.762 ± 0.398 at concentrations of 250 µg/ml, 500 µg/ml, and 1,000 µg/ml, respectively, which shows that both samples F8 and F9 have significant antioxidant potential. Optimized CT emulgels F8 and F9 had significant antibacterial activity against Staphylococcus aureus and Escherichia coli at p-value = 0.00, the Emulgel-F8 shows zone of inhibition of 24 mm for E-coli and 19 mm for S-aureus. Emulgel-F9 shows zone of inhibition of 22 mm for E-coli and 15 mm for S-aureus while pure CT- Oil extract shows zone of inhibition of 25 mm for E-coli and 20 mm for S-aureus and ciprofloxacin used as standard shows 36mm zone of inhibition against both E-coli and S-aureus. The comparative investigation through molecular docking binding affinities and interactions of ligands with various target proteins provides insights into the molecular processes behind ligand binding and may have significance for drug discovery and design for the current study.ConclusionThe current study suggests that C. tinctorius L.-based emulgel has good antioxidant and antibacterial activities against E. coli for the treatment of bacterial skin infections

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy